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AI & ML

The Context Engine

Executive Summary

The Context Engine

The model is not the problem. In every enterprise AI deployment that has hit a production wall in 2026, the failure lives one layer down: in how data is prepared, permissioned, and delivered before the model ever begins reasoning. Model choice has become the wrong question. With Anthropic's Claude surpassing OpenAI in U.S. enterprise adoption (34.4% vs. 32.3%, Ramp AI Index, April 2026), the market has already moved on. The competition has shifted from the Reasoning Engine to the Context Engine.

While nearly every enterprise has deployed frontier models, most are paying a Hallucination Tax they cannot see on their P&L. For an organization with 1,000 knowledge workers, the 4.3 hours per employee per week spent manually verifying AI outputs (Forrester, 2025) equates to approximately $16.8 million in annual salary drain, calculated at a conservative $75 per fully-loaded hour. Multiply that across a global enterprise, and it maps to the $67.4 billion in documented AI hallucination losses recorded in 2024 alone (AllAboutAI, 2025). This is not a failure of the model. It is a failure of architecture.

This paper argues that the next phase of enterprise AI requires a Deterministic Intelligence Layer: infrastructure that normalizes, indexes, and permissions customer data before it reaches the model. Teams replacing token-heavy RAG workflows with deterministic, pre-indexed context are seeing substantial reductions in cost per task while dramatically improving retrieval precision and AI reliability. More importantly, they are crossing the Threshold of Action: the point where AI becomes trustworthy enough to move from surfacing insights to executing workflows.

Section 1

The New Benchmark: Claude's Enterprise Breakout Moment

The AI market just had its crossover moment. As of April 2026, more U.S. businesses pay for Anthropic's Claude than for any other AI model. 34.4% vs. 32.3% for OpenAI, according to the Ramp AI Index, which tracks actual spending across more than 50,000 companies. This isn't a survey about intent. It's purchasing data.

By March 2026, Anthropic was capturing 73% of first-time business AI buyers (Axios, March 2026). A year earlier, one in 25 businesses on Ramp's platform paid for Anthropic. Today, it's nearly one in three.

Enterprise buyers don't switch defaults on a whim. They switch when something is demonstrably working better for the work they actually need done.

The Model Is Not the Problem

Here is the harder truth underneath that adoption story. Despite the crossover, most enterprise AI deployments are not delivering.

Widespread adoption. Widespread underdelivery. Both things are true simultaneously.

The instinct in most organizations is to treat this as a model problem: switch providers, upgrade to the latest version, hire a prompt engineer. None of it moves the needle in any sustained way, because the model is not where the failure lives. Claude is a reasoning engine. A sophisticated one. But a reasoning engine can only reason over what it's given. And in most enterprise deployments, what's given is a mess. Fragments.

The Performance Ceiling

Every technical leader deploying Claude at scale hits the same wall. The demo works. The pilot looks promising. Then it moves toward production, and something breaks. Not catastrophically, but consistently. The AI misattributes an item to the wrong account. It summarizes a customer's history using stale data. It generates an output that sounds authoritative and requires 20 minutes of human verification before it can be trusted.

"Feed a world-class reasoning engine confident, well-structured garbage, and you get the same in return."

This is not a failure of reasoning capability. It is a failure of context architecture. The data required to generate reliable outputs, account history, communications, support activity, call transcripts, and operational metadata typically exists across fragmented systems with inconsistent normalization, disconnected permissions, and no canonical entity resolution layer tying it together.

Context Is the New Infrastructure

The companies pulling ahead in 2026 are not winning because they chose a better model. They are winning because they solved the harder problem underneath it: delivering clean, resolved, permission-aware context before the model ever begins reasoning.

IT, Data, and Platform Engineering provide the Engine (Claude): a recurring operating expense. World-class reasoning, rented.
RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned.

Claude is the current catalyst. The model market will keep moving. New releases, new providers, new pricing. What doesn't move is the underlying problem: fragmented, unresolved, improperly permissioned data. Deterministic context is the durable architecture. The organizations building it now will carry that advantage into every subsequent model generation.

Most organizations already have the engine. What they lack is the map.

Section 2

The Hallucination Tax: Why Fragmented Data Kills AI Performance

If the model isn't the problem, why are so many production-grade AI initiatives hitting a performance ceiling? The answer is the Hallucination Tax.

In 2024, hallucinations cost enterprises an estimated $67.4 billion in global losses (AllAboutAI, 2025). By early 2026, the cost has shifted from outright fabrications to "silent hallucinations": outputs that look structurally perfect but are factually untethered from the current state of the business.

For an organization with 1,000 knowledge workers, the 4.3 hours lost per person per week equates to roughly 223,600 hours of wasted annual productivity, approximately $16.8 million in annual salary drain at a conservative, fully loaded rate. It never appears on the P&L as an AI cost. It shows up as underperformance, missed forecasts, and slower deal cycles.

This forces employees to act as "Human Middleware": the bridge between fragmented systems and the AI that was supposed to make them irrelevant. This tax is the direct result of four specific architectural failure modes.

Failure Mode 1: Retrieval Precision (The Token Tax)

Standard RAG is probabilistic. It retrieves semantically similar fragments, not operational truth. When a sales leader asks, "Why did we lose this seven-figure deal?", the system may surface an old QBR deck instead of the pricing objections in email, the procurement concerns buried in Slack, the legal escalation in Jira, and the product gaps discussed in call transcripts that actually determined the outcome.

Because retrieval is imprecise, teams over-index by stuffing the context window with every possible document to ensure the right one is in there. The result: thousands of reasoning tokens spent filtering noise. A world-class reasoning engine doing the work of a search index.

Failure Mode 2: "Lost in the Middle" (Attention Drift)

Research by Liu et al. (TACL, 2024) demonstrated that accuracy on multi-document reasoning tasks drops by more than 30 percentage points when relevant information is buried in the middle of a long context window. This matters enormously in enterprise environments, where critical signals are scattered across support escalations, pricing discussions, call transcripts, Slack threads, and CRM updates. Simply increasing context size does not solve the problem. In many cases, it amplifies it by forcing the model to attend to more noise.

Failure Mode 3: The Identity Crisis (Entity Disambiguation)

In a fragmented environment, identity is a variable, not a constant. "Jane Doe" in a Zoom transcript needs to resolve to the same Jane Doe in Salesforce, Gmail, Zendesk, Slack, and the CRM activity timeline. Without deterministic entity resolution, the model is forced to infer whether those interactions belong to the same person, account, or buying committee.

Without deterministic entity resolution, the model is forced to reconstruct identity probabilistically. A support escalation tied to one stakeholder, a pricing objection raised in a sales call, and an executive concern discussed over email may be incorrectly assembled into the wrong account narrative entirely.

Failure Mode 4: The Permission Ghost (Unauthorized Surface)

This is the silent killer of enterprise AI programs. Most RAG pipelines lack Source-System Parity. If the AI retrieves a snippet from a private executive email because it was "semantically relevant" to an intern's query, the system has failed regardless of whether anyone noticed.

Incidents like EchoLeak show exactly why retrieval-layer permission enforcement matters. In late 2025, researchers demonstrated a zero-click vulnerability in Microsoft 365 Copilot that could exfiltrate sensitive data from Copilot context without user interaction. No prompt injection required. The retrieval layer was the attack surface.

For most organizations, the permission layer isn't just a technical problem. It is an organizational liability that Legal and Security will eventually force you to solve on a deadline, under pressure, after something has already gone wrong.

The Production Wall

These four failure modes create the Production Wall. A curated demo can appear remarkably accurate. But production environments are not curated. They are noisy, fragmented, and constantly changing, with critical signals distributed across emails, calls, support threads, Slack conversations, and operational systems evolving in real time.

"You cannot solve these four problems by tuning the prompt. You have to solve them by fixing the context."

Section 3

The Deterministic Intelligence Layer

To climb over the Production Wall, enterprise architecture must evolve. The solution is not a larger context window or a more complex prompt. It is a fundamental shift in how data is prepared for the model. Enter the Deterministic Intelligence Layer: infrastructure that sits between your raw data silos and Claude, acting as the architectural antidote to the four failure modes in Section 2.

The Four Pillars

1. Precision Indexing (Ending the Token Tax)

Instead of relying on similarity search alone, the context layer resolves entities, removes duplication, and prioritizes high-signal interactions before retrieval. The model receives structured operational context rather than raw fragments competing for attention.

In Sturdy-observed deployments, replacing raw context with pre-indexed, distilled payloads has reduced token consumption by 80 to 90% on comparable workflows. Results vary by source data density and baseline architecture. You stop paying for Claude to be a search filter.

2. Signal Distillation (Solving "Lost in the Middle")

Semantic Pruning strips HTML headers, Slack noise, legal footers, and the RE: FWD: RE: reply chains that bury every actual decision in 40 lines of quoted text, distilling threads into thematic buckets: Bug Reports, Feature Requests, Sentiment Shifts. The most critical insights land at the beginning of the context window, bypassing the 30-point accuracy drop documented in long-context research.

3. Deterministic Entity Resolution (Fixing the Identity Crisis)

A Global Entity Map resolves disparate naming conventions into a single, immutable Customer ID. Claude is no longer guessing whether two conversations belong to the same account. It is being told they do.

4. Parity-Enforced Permissions (Exorcising the Permission Ghost)

The retrieval layer enforces source-system permissions before context assembly, so unauthorized records are excluded from the payload sent to the model. This is not a prompt-level instruction that can be overridden or confused. It is an architectural enforcement point that sits entirely upstream of the model.

Security becomes a structural property of the architecture, not a probabilistic instruction to the model. Incidents like EchoLeak show why this distinction matters: when permission logic lives inside the prompt, the retrieval layer remains an attack surface. When it lives at the data layer, it doesn't.

Reference Implementation: Sturdy + Claude via MCP

While the merits of this architecture are clear, building it internally results in years of maintenance debt (see Section 5). Sturdy leverages the Model Context Protocol to serve as the Context Engine for Claude, normalizing, indexing, and permission-stamping your customer intelligence layer across Salesforce, Gmail, Slack, and Zendesk before Claude ever queries it.

Claude provides the Reasoning Layer. Sturdy provides the Memory and Context Layer. Together, they move an enterprise from AI that reads your business to AI that acts on it.

Section 4

What It Unlocks: From Reading to Acting

In 2026, summarization is a commodity. The competitive advantage lies in moving from AI that reads your business to AI that acts on it. This transition requires a fundamental shift in how leadership views the AI stack and who owns what.

IT, Data, and Platform Engineering provide the Engine (Claude): recurring operating expense. World-class reasoning, rented.
RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned, not rented.

When the engine has a perfect map, the Acceleration Gap closes.

RevOps: The Revenue Architect

For the RevOps leader, a deterministic layer turns fragmented operational data into active revenue signals. Instead of building static dashboards that explain why a quarter was missed, RevOps can monitor the commercial signals that actually move deals: pricing hesitation in email, procurement delays, legal friction, competitive mentions, executive disengagement, stalled next steps, and tone changes across active opportunities.

A deterministic context layer resolves those signals to the right person, account, opportunity, and timeline before AI ever reasons over them. That is what turns scattered communication into reliable revenue action.

RevOps stops being a report generator. It becomes the operating system for revenue execution: designing the logic that turns verified commercial signals into coordinated GTM action.

Sales: Instant Account Intelligence

The average sales rep spends roughly 20% of their week on pre-call research. With a deterministic layer, the account briefing is no longer a probabilistic summary. It is a verified snapshot: "The customer's last three support tickets were resolved, but they haven't yet implemented the API update discussed in the March QBR."

Product: The Automated Feedback Loop

Product managers are often the most data-rich but insight-poor employees in the company. A deterministic layer moves PMs from reading feedback to querying insights. Claude analyzes 60 days of feedback across Slack and Zendesk and, with a single prompt, generates a high-fidelity Jira ticket including exact customer quotes, impacted account IDs, and revenue at risk.

Customer Success: Proactive Triage

In CS, latency is the enemy. A deterministic layer allows Claude to perform live triage. When a customer sends a frustrated email, the AI checks contract terms and recent product usage logs before the CSM has finished reading the subject line. It presents a Context-Aware Response ready to send, grounded in verified account data.

"The model you license today is rent. The customer intelligence layer you build is equity. One gets replaced. The other compounds."

Every account signal normalized, every entity resolved, every permission enforced. That accumulates. The organizations building this layer now are building institutional memory that makes every model they run on top of it better.

Section 5

The Build vs. Buy Reality

The instinct for most sophisticated IT and data teams is to build. It is a legitimate impulse. The stack looks deceptively simple: a few API connectors, a vector database, and some chunking logic. In the demo phase, an internal build often feels like the most cost-effective path.

The Four Hidden Engineering Hurdles

1. The Normalization Treadmill

Building a connector to Salesforce is straightforward. Maintaining the logic layer that resolves entity names across Salesforce, Slack, and Zendesk as those systems' schemas evolve is a full-time engineering job. This is Semantic Drift: hundreds of developer hours consumed by maintenance rather than innovation.

2. The Permission Mapping Paradox

Mapping row-level permissions from source systems into an AI context window is one of the most complex security challenges in modern software. Most internal builds rely on prompt-level security, which fails under the weight of incidents like EchoLeak. This isn't a technical trade-off. It is an organizational liability waiting to be forced into crisis.

3. The Latency Wall

A custom RAG pipeline often takes 5 to 10 seconds to fetch and clean data. In Sturdy-observed deployments, pre-indexed deterministic retrieval consistently operates under 1 second on production data volumes, but reaching that benchmark requires specialized search infrastructure expertise that is rarely the core competency of a generalist data team building from scratch.

4. The Token Optimization Tax

Without signal distillation, internal builds routinely pass 3x to 5x more tokens than necessary. Teams save on build costs only to spend twice as much on model API costs.

Where Does Your Engineering Dollar Go?

The strategic question isn't "Can we build this?" It's "Should we own the maintenance of this?"

Competitive advantage does not live in the plumbing. No customer chooses a vendor because their AI has a better Python script for cleaning Slack data.

By offloading the Normalization Treadmill to Sturdy, organizations are promoting their engineering teams from Data Cleaners to AI Product Owners, moving their best people away from the maintenance treadmill and toward the high-value work of building AI that drives revenue.

Buy the plumbing. Build the logic. The teams doing this are shipping revenue-generating AI workflows, while their competitors are still debugging entity-resolution scripts.

Section 6

What to Do Now: The 2026 Roadmap

The Acceleration Gap is not a permanent state. It is a choice of architecture. The move is not to wait for a smarter model. The move is to fix the context. Here are four moves for leadership to take in the next 90 days.

Move 1: Audit Your Retrieval Precision, Not Your Prompts

Most teams spend the majority of their time prompt-tuning errors caused by bad data retrieval. The action: Run a Ground Truth test. Take ten complex customer queries and manually check the data fragments Claude is being fed. If more than 20% of that data is noisy, stale, or misattributed, no prompt engineering will save the deployment. You have a plumbing problem, not a reasoning problem.

Move 2: Isolate a Multi-Source Workflow

The highest ROI for a deterministic layer is found where data is most fragmented. The action: Pick a high-value, closed-loop use case where data lives in at least three systems. For example: the path from customer feedback in Slack and Zendesk to an engineering action in Jira. Solve the context problem here, and you've built a blueprint for the rest of the organization.

Move 3: Enforce Permissions at the Data Layer

Stop treating security as a probabilistic instruction. The action: Move permission enforcement out of the system prompt and into the retrieval infrastructure. Ensure the retrieval layer enforces source-system permissions before context assembly, so unauthorized records never reach the model. The Permission Ghost is exorcised structurally, not instructionally, and the organizational liability is removed before Legal ever has to get involved.

Move 4: Define Where AI Earns the Right to Act

The distance between AI that summarizes and AI that executes is a trust gap, not a technology gap. The action: Build human-in-the-loop approval gates for high-stakes actions. Drafting a renewal contract. Creating a Jira ticket. Sending a support response. Use your deterministic layer to provide the required Confidence Equity. The threshold to target is a sub-5% error rate on AI-generated drafts. That is the point at which approval gates can be safely reduced, and workflows become self-sustaining.

Traditional probabilistic RAG architectures struggle to reach this threshold consistently at enterprise scale. Because probabilistic retrieval introduces entity errors, stale data, and permission noise, error rates on complex multi-source tasks typically stabilize in the 15 to 30% range regardless of prompt quality, even with hybrid retrieval and reranking layers added on top.

A deterministic layer that resolves entities before inference, distills the signal before retrieval, and enforces permissions before the model ever sees the data is the only architecture that makes sub-5% structurally achievable, rather than an occasional lucky outcome.

In Sturdy-observed deployments, teams that reach this threshold have consistently moved to reduced-oversight approval workflows within a quarter. Results depend on workflow complexity and baseline data quality. Reaching the sub-5% Trust Threshold is the definitive signal that an organization has graduated from "AI Experiments" to a Context Engine architecture capable of autonomous action. That is the architectural line between AI that assists and AI that acts.

Conclusion

The Architectural Advantage

Frontier models will continue to improve and commoditize. The durable advantage is no longer the model itself. It is the architecture surrounding it.

The long-term value does not live in another standalone AI interface. Interfaces change too quickly. The durable layer is the operational context infrastructure beneath them.

Organizations that solve deterministic context assembly, entity resolution, permission-aware retrieval, and operational state assembly gain a compounding advantage independent of whichever model, interface, or orchestration layer dominates next year.

Organizations that solve context architecture today are building infrastructure that compounds across model generations. As interfaces evolve and models improve, the operational context layer beneath them becomes increasingly valuable.

"The era of the Context Engine is here. Is your architecture ready for it?"

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Joel Passen

May 19, 2026

•

5 min read

Executive Summary

The Context Engine

Section 1

The New Benchmark: Claude's Enterprise Breakout Moment

Enterprise buyers don't switch defaults on a whim. They switch when something is demonstrably working better for the work they actually need done.

The Model Is Not the Problem

Here is the harder truth underneath that adoption story. Despite the crossover, most enterprise AI deployments are not delivering.

Widespread adoption. Widespread underdelivery. Both things are true simultaneously.

The Performance Ceiling

"Feed a world-class reasoning engine confident, well-structured garbage, and you get the same in return."

Context Is the New Infrastructure

IT, Data, and Platform Engineering provide the Engine (Claude): a recurring operating expense. World-class reasoning, rented.
RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned.

Most organizations already have the engine. What they lack is the map.

Section 2

The Hallucination Tax: Why Fragmented Data Kills AI Performance

If the model isn't the problem, why are so many production-grade AI initiatives hitting a performance ceiling? The answer is the Hallucination Tax.

Failure Mode 1: Retrieval Precision (The Token Tax)

Failure Mode 2: "Lost in the Middle" (Attention Drift)

Failure Mode 3: The Identity Crisis (Entity Disambiguation)

Failure Mode 4: The Permission Ghost (Unauthorized Surface)

The Production Wall

"You cannot solve these four problems by tuning the prompt. You have to solve them by fixing the context."

Section 3

The Deterministic Intelligence Layer

The Four Pillars

1. Precision Indexing (Ending the Token Tax)

2. Signal Distillation (Solving "Lost in the Middle")

3. Deterministic Entity Resolution (Fixing the Identity Crisis)

4. Parity-Enforced Permissions (Exorcising the Permission Ghost)

Reference Implementation: Sturdy + Claude via MCP

Claude provides the Reasoning Layer. Sturdy provides the Memory and Context Layer. Together, they move an enterprise from AI that reads your business to AI that acts on it.

Section 4

What It Unlocks: From Reading to Acting

IT, Data, and Platform Engineering provide the Engine (Claude): recurring operating expense. World-class reasoning, rented.
RevOps, Data, and AI Teams provide the Map (the Deterministic Data Layer): a long-term asset. Customer intelligence, owned, not rented.

When the engine has a perfect map, the Acceleration Gap closes.

RevOps: The Revenue Architect

RevOps stops being a report generator. It becomes the operating system for revenue execution: designing the logic that turns verified commercial signals into coordinated GTM action.

Sales: Instant Account Intelligence

Product: The Automated Feedback Loop

Customer Success: Proactive Triage

"The model you license today is rent. The customer intelligence layer you build is equity. One gets replaced. The other compounds."

Section 5

The Build vs. Buy Reality

The Four Hidden Engineering Hurdles

1. The Normalization Treadmill

2. The Permission Mapping Paradox

3. The Latency Wall

4. The Token Optimization Tax

Without signal distillation, internal builds routinely pass 3x to 5x more tokens than necessary. Teams save on build costs only to spend twice as much on model API costs.

Where Does Your Engineering Dollar Go?

The strategic question isn't "Can we build this?" It's "Should we own the maintenance of this?"

Competitive advantage does not live in the plumbing. No customer chooses a vendor because their AI has a better Python script for cleaning Slack data.

Buy the plumbing. Build the logic. The teams doing this are shipping revenue-generating AI workflows, while their competitors are still debugging entity-resolution scripts.

Section 6

What to Do Now: The 2026 Roadmap

Move 1: Audit Your Retrieval Precision, Not Your Prompts

Move 2: Isolate a Multi-Source Workflow

Move 3: Enforce Permissions at the Data Layer

Move 4: Define Where AI Earns the Right to Act

Conclusion

The Architectural Advantage

Frontier models will continue to improve and commoditize. The durable advantage is no longer the model itself. It is the architecture surrounding it.

The long-term value does not live in another standalone AI interface. Interfaces change too quickly. The durable layer is the operational context infrastructure beneath them.

"The era of the Context Engine is here. Is your architecture ready for it?"

‍

Our articles

Customer Intelligence

How to choose a customer intelligence platform

Joel Passen

October 24, 2022

•

5 min read

Despite customer intelligence still being an emerging field, there are already many incredible CI platforms that can help you get the most out of your data. Utilizing customer intelligence data will not only help improve your overall business strategy, but it’s also a powerful way to improve customer satisfaction and customer experience.

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Data on its own isn’t beneficial. What matters is understanding the customer journey of your users and analyzing data, customer feedback, and customer behavior to make better decisions.

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But as with most things in business, not all customer intelligence platforms are created equal. Depending on your goals, the size of your company, and your budget, each platform has its own strengths and weaknesses.

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Whether you’re already sold on the value of customer intelligence or looking for ways to take your business to the next level, this article will cover everything you need to know about choosing the right customer intelligence platform for your needs.

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Choose a customer intelligence platform that works well with your tech stack.

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Businesses today, on average, use 37+ tools across their teams and departments. Every department has its “go-to” tools. Yet, keeping track of all that data collected by these tools can take time, and it only gets more challenging the more systems your business uses. With so many silos, it can be impossible to understand all your data in aggregate.

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When choosing a customer intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack.

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For example, at Sturdy, many of our customers use Salesforce, so we began focusing on Salesforce integrations for our customers who rely on using the most popular CRM in the world. A customer intelligence platform can have flashy dashboards. Still, it will be challenging to realize game-changing value if it doesn’t pull the full payload from your CRM.

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At a minimum, buyers must choose a system that integrates directly into your CRM, email, and ticketing system. Be skeptical of CI tools that claim to integrate with hundreds of tools “out of the box.” Chances are these systems are using a third-party integration platform. While third-party integration platforms are great for some things, they can be limited when ingesting data from custom fields. And otherwise, they represent another failure point on the reliability daisy chain.

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Many CI platforms, such as our platform, Sturdy, become more valuable with more data they access. To that end, it’s essential to identify your largest customer feedback channels. For most of us, it’s likely email. Our research has shown that over 60% of B2B customer-to-business conversations are over email. This makes a tight integration with your email platform imperative. The right CI tools analyze email, and then and only then can they give you predictive customer intelligence data based on the bulk of your everyday customer interactions.

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Pro tip: When considering customer intelligence platforms, integrations matter. Choose a system that has authorized integrations with your other vendors’ marketplaces. Avoid systems that rely on third-party integration platforms. And, if email isn’t a core integration, you’ll likely be missing the lion’s share of insights about your customer relationships.

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A secure, privacy-first customer intelligence platform

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Let’s face it, there’s a consummate conflict of interest in businesses today. Business units must leverage data to turn raw information into actionable insights. On the other hand, InfoSec and privacy teams must ensure compliance with a myriad of regulations relating to collecting and using data, mainly when it contains PII.

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Personally identifiable information or PII is any information that permits an individual’s identity to be directly or indirectly inferred, including any information linked or linkable to that individual. But, if you collect someone’s name and email address, you are collecting PII. For this reason, you must choose a CI platform designed for the privacy-first era. Anything less is asking for trouble. Here are some tips to get started:

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First, ensure your potential partner maintains an information security program certified by yearly SOC2 Type II audits. This protects the security, availability, confidentiality, integrity, and privacy of their services and your customer data.

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Next, understand each provider’s approach to processing PII. Being SOC 2 Type II isn’t really about privacy. Otherwise, it’s essential to know if a vendor’s employees, consultants, or sub-processors have access to your customers’ PII. If they do, this is a problem. Look for a solution that offers a virtual data clean room. This way, you can ensure that data from different systems, including email, ticketing, and customer relationship management (CRM), is securely funneling into one spot. This data is encrypted and then anonymized, making it impossible for anyone in the data clean room to access PII.

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Choose a customer intelligence tool that gets buy-in across all your teams.

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There are very few teams in a SaaS business that don’t need more insights about customers. Customer intelligence is something your entire company should be involved in. Everyone in your organization will benefit from your chosen customer intelligence platform, from engineering to product to marketing.

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When choosing a CI platform, consider the following:

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Insights for various teams: Customer Intelligence isn’t just for customer success teams. Product and engineering teams can immediately benefit from learning more about customer frustration, confusion, and wants directly from the voice of the customer. Marketing teams can transform positive insights into customer references. Rev Ops and the BI team can create new analytical frameworks from previously unavailable data.

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Fast time to value: Let’s face it, we’ve all bought platforms that were oversold, hard to implement, and even harder to administer. Look for solutions that can deliver insights to your specific use cases quickly. Understand the resources required to start receiving value and what resources are needed to maintain the program in the future.

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Tech stack: When choosing a customer intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack. And don’t forget email. 60% of customer-to-business communications start with an email.

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Avoid duplicate functionality: CI platforms often have similar functionality to systems you already have, like customer success platforms and CRM systems. Look to compliment your existing system with rich data from a customer intelligence solution.

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Security: Does the platform have a clear and transparent take on data security? Ensure that any system you choose is SOC 2 Type II ready.

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Data privacy: How does the platform handle data privacy? Is the vendor using anonymization, pseudonymization, and de-identification techniques?

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Customer intelligence is not a magic bullet: Avoid platforms that make incredibly bold claims.

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It’s essential to have realistic expectations when choosing a CI tool. Just as AI-driven content marketing can be helpful for copywriting and content marketing, it won’t do all the legwork for you.

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This advice applies to customer intelligence platforms and any SaaS tool your business might use. Many “all in one” tools or “magic bullet” solutions claim they can do everything. But remember, the more the vendor tries to do, the more likely they, too, have “soft spots” where the technology isn’t good.

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At the end of the day, a customer intelligence solution should help you operationalize your practices and programs and get your entire organization enthusiastic about using insights to improve products, drive growth and expansion, and, ultimately, increase your NDR. Find solutions that demonstrate a clear path to value in the shortest time. These are the solutions that the C-suite can fund.

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Finally, customer intelligence is a hot topic. But it’s not exactly new. So with the tremendous growth in the CI world, some organizations have failed with products that don’t deliver value. The good news is that integrations, data sciences, and privacy tooling have all dramatically improved in the past 3-5 years. This has made products more powerful and easier to maintain.

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Turn customer feedback into actionable insights. Get clear on your CI goals.

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Customer intelligence tools continue to innovate incredibly quickly, but choosing a tool that serves your specific needs will make or break your experience.

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Perhaps you’re really focused on reducing churn. You may want a platform that streamlines your data points in one easy-to-read channel. Improving your customer experience is your number one goal. Increasing customer lifetime value, for example, is a common goal regarding competitive intelligence.

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Of course, you’re almost certainly going to have multiple business goals. Still, it’s critical to have a clear idea of what you’re hoping the CI platform can help you accomplish from the start. Before you schedule a demo or request more information, have 2-3 specific goals in mind.

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Invest in both the now and the future with customer intelligence

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There are significant gaps between what customers think about your products, the level of services you provide, and the execution of the journey you’ve outlined. The question is, “how seriously are you taking their feedback”? How closely are you listening to your customers? Churn doesn’t happen in a vacuum. It’s a culmination of feature requests, “how to” questions, executive changes, response lags, unhappy sentiment, and more. The right customer intelligence must deliver the insights to help teams create more enduring relationships with arguably the most significant cohort of humans outside your employees — your customers.

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While customer intelligence 2.0 is still in its infancy, businesses that utilize modern CI solutions effectively have a clear competitive advantage over those that do not. Nothing speaks louder than the voice of your customer. Today’s customer-obsessed teams make better decisions based on insights into the data customers generate for us with every conversation.

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Interested in seeing around the corners? Learn where customer intelligence is going. Schedule a demo with Sturdy today.

Customer Intelligence

What is customer intelligence?

Steve Hazelton

September 27, 2022

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5 min read

In today's increasingly competitive business landscape, you and your team need every possible advantage to help you stand out.

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From analyzing customer data to perfecting the customer journey for your users, there's no shortage of things to do to give yourself an edge. Today's most successful businesses continue to turn to customer intelligence to help them improve their products and services and to implement an effective business strategy.

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In this article, we'll answer the question: What is customer intelligence? As well as show how customer intelligence can be instrumental in improving customer loyalty, customer experience, and more.

What is Customer intelligence?

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Customer intelligence is the process of collecting and analyzing customer data from internal and external sources. It plays a critical role in unlocking customer insights.

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Customer intelligence (CI) covers everything from interviewing your customers and asking for direct feedback to looking at your data to know where there's room to optimize your funnel.

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The customer intelligence process is not something you can check off your to-do list and call it a day. Instead, it's a never-ending process that will keep you competitive.

How to turn customer data into actionable insights

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When a customer emails you, "Hey, can you add this feature?" they want you to use that data. In a perfect world, you could implement any feature a customer requests. Still, as you likely know all too well, resources are limited.

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To make matters worse, ensuring the data gets to the right team can take time and effort. Collecting data is easy, but turning that data into insights is the challenge. Unstructured data is one of the biggest challenges teams face today.

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It's not like your email messages have a data field that tells your engineering team, "Hey, build this."

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At smaller companies, you can get by manually recording this information with rules like "Hey, if something important happens, log it in Salesforce."

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At larger companies, this doesn't work.

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The conversion of unstructured data to useful data is the most challenging part and where you can reap the most significant benefits. Turning unstructured data into helpful info is one of the most critical parts of a successful customer intelligence strategy.

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Today, many organizations are getting hundreds, if not thousands, of messages daily. And virtually none of that data is converted to easy-to-use insights automatically.

Cracking the customer intelligence code: "turn noise into music."

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Imagine if all of the customer data across your company was working together (including your black holes, like email). Imagine the efficiency your organization can achieve when you're not only collecting relevant data but you know exactly what steps you and your team need to take.

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This is customer intelligence at its finest.

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If your best customer posts a bug, it might not be a big deal. If your best customer complains about a bug in chat, email, and ticket system, well, someone better take a look.

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Before the emergence of customer intelligence platforms, this type of identification and triage was almost impossible, which is one of the biggest reasons we created Sturdy.

Analyzing customer data to win big with your business

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We should continue doing everything possible to mine our customer communications and develop strategic customer signals. Yet, many companies know more about their anonymous website visitors than their paying customers.

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Truly understanding the customer journey of your customers from start to finish can pay massive dividends down the line. Understanding customer behavior and customer signals and being proactive in finding your users' pain points can dramatically improve the health of your business.

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Virtually every company has a way to track and monitor its website visitors—something we like to call table stakes. Yet, almost zero have any way to monitor and monetize the happiness of their actual customers.

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Here's a challenge...

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Answer this: If your company was about to lose a customer, who would be the best person to save that customer? What metrics would you use to support your answer?

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Most companies need customer intelligence data to answer this question.

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Let's take it one step further.

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How many times did a customer say, "You guys are great!" last month? How many times were those happy customers converted to references and case studies? And how many of those references are delivered to your sales team to help them close new business?

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Again, it's the 21st century.

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We realize the challenges of customer intelligence are great.

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But in this area, failure is unacceptable. To have a truly operationalized customer-focused company, you need to mine these communications without bias and without manual data entry.

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You need something that never gets tired, doesn't need training, and gets better as you throw more data at it. And most importantly, you can't wait until the quarterly business review is complete with triaging a churning customer.

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Customer intelligence solutions are the answer to staying relevant in today's business world. And here at Sturdy, we are on a mission to help businesses deliver better products, services, and experiences through actionable data.

Software

Sturdy announces SOC 2 Type II security compliance certification

Joel Passen

July 11, 2022

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5 min read

It's official: Announcing our SOC 2 Type II Report

Shortly after launching Sturdy, we started our SOC 2 certification process. A SOC 2 report is for services organizations that hold, store, or process the information of their users. You can read more about it here.

Late last year, we obtained our SOC 2 Type I report. This represents a "snapshot", indicating that we have robust controls in place to ensure the security and availability of our customers' data.

Today, we are announcing that Sturdy has obtained a SOC 2 Type II report. This is the most comprehensive SOC protocol, and attests not only to the suitability of our process and systems, but our operational effectiveness of sticking to those controls over a period of time.

The full writeup describes our suite of controls for securing and handling customer data, including:

System monitoring and ongoing risk assessments
Internal access control to production environments
Disaster recovery, data backup, and incident response processes
Communication of changes to customers
Employee on-boarding and termination processes

We're proud of this report. It is a reflection of our dedication to security and the product of many months of hard work from our team, particularly Eric Weidner. Our commitment to security is about more than checking a box: every day we make sure that our systems and processes are worthy of the important data our customers trust us with.

Sturdy is a data-centric system of intelligence for post-sales teams. Working with data, including some of our customer's most sensitive information is what we do. We work to earn their trust by putting security and privacy front-and-center. This includes industry-leading controls, data minimization, and a secure-by-design architecture. Perhaps most importantly, we have built a security-conscious culture from Day 1: everyone at Sturdy knows that we solve for security first. You can read more about our processes and approach below.

Security Program

At SturdyAI, the security and integrity of our customer's information is of utmost importance. Therefore, Sturdy has developed and maintains a comprehensive Information Security Management program to manage risks to the security, availability, confidentiality, integrity, and privacy of Sturdy systems and products. Our program has been independently audited and certified to meet the requirements of Trust Services Criteria SOC2 Type II.

Privacy

Sturdy products utilize customer communication data to detect important signals that may have private information included such as names and contact information. To protect the privacy of this information, we maintain policies and processes to comply with data privacy regulations such as CCPA and GDPR and to help our customers comply with their obligations as the controllers of this data. Please see the Sturdy privacy policy for more information on data privacy practices and controls.

Infrastructure

Sturdy utilizes Amazon Web Services (AWS) as the Infrastructure-as-a-Service hosting provider. All data stored in AWS data centers located in the United States. Communications into our services are encrypted-in-transit and data is stored encrypted-at-rest using industry standard encryption mechanisms. Web application firewalls and network management tools such as VPC's, private subnets, and security groups are used to manage the flow of information and access between services. Infrastructure services are defined, managed, and deployed with Infrastructure-as-Code orchestration tools for consistent and repeatable systems.Tenant data is isolated in separate systems and production systems are kept in restricted access accounts separated from the development environments. 3rd-party penetration testing is conducted annually.

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Questions about Sturdy's security program? Contact us at security @ sturdy.ai.

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Customer Intelligence

Sturdy raises $3.1 million to strengthen its AI-led customer intelligence and automation platform

Joel Passen

June 28, 2022

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5 min read

SturdyAI raises $3.1m to broaden awareness of Sturdy, the AI-powered customer intelligence and automation solution.

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We are excited to announce that we’ve raised $3.1M in a financing round led by Lawson DeVries at Grotech Ventures. We'd also like to welcome Lawson to the board of directors. He brings over 20 years of software-focused venture investing and management experience with him.

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Read the full press release here.

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The idea for SturdyAI came from running SaaS businesses for the past 15+ years.

Our “Aha!” moment was when we realized that our customers were actually telling us what they want and need, every day.

The idea for SturdyAI came from building, bootstrapping, and scaling successful SaaS businesses. While running companies we realized that there is an ever-growing body of valuable data being created by our users. This feedback is just sitting in email accounts, in video conferencing systems, in chat logs, and buried in ticketing systems. We founded SturdyAI to empower businesses to solve problems that we faced as entrepreneurs and executives. At the end of the day, running a SaaS company is about keeping customers and taking advantage of the long tail of subscription revenue.

With the subscription business model reaching near ubiquity in many industries, particularly in cloud-based software, driving dollar retention (NDR) has evolved as the most important business metric. Companies with higher dollar retention are simply healthier and more valuable. So how does a subscription-based business drive dollar retention? Our earliest decks talked about, “getting your data in one spot”. But that wasn’t the problem we were trying to solve (wanting to see all the data in one spot is a symptom, not a solution). The problem wasn’t really a communication problem, it was a mining and refining problem. The problem we solve is separating the signals form the noise.

When a customer requests a copy of her contract, that message must get forwarded to the "Saves Team" - immediately. Save a customer — improve NDR.

Customers give us information to run our businesses better, to predict churn, to capture references, to get in front of renewals, to prioritize features, yet these critical signals are trapped and decaying in dozens, if not hundreds of data silos. Our customers are giving us the "answer to the test" in Slack, Email, Zendesk, Salesforce, Gong, Zoom, etc. Today, the only way we utilize this information is if someone manually identifies, records and escalates it.

These signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value. And reducing churn in this example is just saving 5 customers.

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Today's CX stack is missing a systems of intelligence. Sturdy fills the void.

Greylock's Jerry Chan may have coined the term system of intelligence. He wrote about the category in 2017 saying that "What makes a system of intelligence valuable is that it typically crosses multiple data sets, multiple systems of record." He actually predicted that SturdyAI would exist — "The next generation of enterprise products will use different artificial intelligence (AI) techniques to build systems of intelligence."

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SturdyAI is a system of intelligence that bridges the gap between systems of record and systems of engagement.

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SturdyAI’s customer intelligence and automation solution empowers B2B SaaS companies and other subscription-based businesses to:

Unify all sources of customer feedback like email, tickets, chats, call transcripts, surveys, and more, into a unified channel.
Analyze all customer feedback for important business insights like churn triggers, contract requests, buyer changes, feature requests, quality of service issues, and more that help lift dollar retention (and more).
Create just-in-time automations to drive insights to the people, teams, and systems that need them most to enable immediate actions.

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Here's how it works. SturdyAI reads every email, ticket, call transcript, chat, and more to discover signals that impact relationships and revenue. Critical signals are then automatically delivered to the people, teams, and systems to take the next best action.

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We're just getting started.

We aren’t here to reinvent and change the way teams or companies work — necessarily. And that is what is so exciting about what we do. SturdyAI is the force multiplier for your business. If you already have a cutting edge BI tool, we just give it better data. If you have a good CX app, we make it more insightful. If you have spent years perfecting your customer health score, we have a new data source to make it more accurate. If you have a great Customer Success, Account Management, Operations, Marketing, and Product teams, we make them more efficient and provide them with better data.

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SturdyAI's customer intelligence and automation solution empowers teams to run a data driven customer operations strategy. This is a screenshot of the Sturdy Home Page.

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“SaaS companies collect a ton of information from their customers every day, but much of it fails to convert to useful and actionable data. Now using AI and automations businesses can proactively understand whether their customers are likely to churn, which features will entice them to renew, are they experiencing bugs, are they happy or not, and much more.,” said Lawson DeVries, Managing General Partner, Grotech Ventures. “Customer retention and expansion are critical for SaaS businesses to maintain consistent growth trajectories, especially as we head into a more challenging environment for acquiring net new customers. Actionable customer intelligence is no longer a nice-to-have aspect for companies of all sizes – it is mission critical for businesses to thrive in today’s market. Grotech has a long history in this segment of the software market, and we are proud to be a catalyst to help fuel Sturdy’s continued strong growth and bring AI to companies that will need to do more with less now and in the future,” continued Mr. DeVries.

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“Churn doesn't happen in a vacuum. It's a culmination of bug reports, feature requests, executive changes, response lags, unhappy sentiment, and more. Sturdy discovers the preemptive signals that help teams create more enduring relationships to lift dollar retention.,” said Steve Hazelton, CEO and co-founder of SturdyAI.

“Every SaaS company has a customer database of record, some have systems of action like customer success platforms but the critical component that most companies lack is a scalable system of intelligence — a system that listens to all of your customer feedback and routes the important things to the right people in the systems that they use every day. That is why we built Sturdy.”

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5 min read

Whether your SaaS business serves 500 or 500,000 users, success hinges on relationships with your users. Unfortunately, listening to users isn't all that easy given the volume of everyday communications and the number of tools in play like email, ticketing systems, chat, video calls, Gong, etc. To compound matters, users are often communicating with multiple people on multiple teams. Deriving value from this data set that is practically hiding in plain sight has been, until now, nearly impossible.

Enter automation. Automation allows you to better understand your relationships with users and provide a better user experience in the process. Automation in this context is the process of using AI and robotic process automation to discover insights and trigger actions at scale.

Introducing Sturdy Automations

Take your workflows a step further with our automations! This new powerful functionality allows you to create your own automated workflows without writing a single line of code. Build out new combinations tailored to the needs of specific teams that want more information about your company’s users. Then extend workflows in the systems they work in the most. Today, Sturdy customers can begin adding custom automations in a few easy steps.

Step 1. Choose a Signal

The first step in building your automations is to pick a signal. A signal is transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights. Customers send signals that help us predict churn, capture references, get in front of renewals, prioritize features, and just run our businesses better. Our customers are giving us this information every day in email, tickets, chats, calls, and more. In fact, we know that nearly 17% of all user-to-business communications contain a signal.

Step 2: Select a field

Depending on the signal you've chosen in step one, you will then select a field. A field is a set of identifiers and attributes that describe a customer. Common examples of a field may include the customer ARR, segment, territory, support level, even custom fields are pulled into Sturdy. The list of fields is populated via API from your master customer database of record like Salesforce.com.

Step 3: Set a value

Now that the first part of our automation (signal + field) is ready, it is time to pick a value. Like fields, values are populated from your master customer database sent to Sturdy via API. For our automation, we selected the field Salesforce Account Customer Segment. The corresponding value in Salesforce is a monetary value imported from a pick list in Salesforce.com.

Step 4: Pick someone to notify

Your automation recipe is nearly complete. Now you just need to pick someone or a system to notify. To customize the exact notification that will occur, pick a notification method. Email and Slack are automatically enabled and available today. In just a few weeks, you can add Salesforce, your CSP, Jira, etc.

What’s next.

In the next few months, we’ll empower Sturdy users to create longer and more complex automation recipes with multi-step automations. And, later this year, our plan is to create a library of pre-prepared recipes to make it even easier to get started.

Your customers are already telling you what's going to happen.

Connect what customers say to why your numbers move. Contextual revenueintelligence, ready for any LLM — or running natively in Ask Sturdy from day one.

No custom NLP pipeline. No 6-month data sprint. Set up in days, not quarters.

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