About us
Intelligence is constrained
by context.
Enterprise context is fragmented. Sturdy resolves it before inference, turning scattered data into structured, permission-aware intelligence.
Infrastructure evolution
Every technology era creates a new infrastructure layer.
We've spent two decades building through multiple platform shifts: systems of record, cloud infrastructure, data platforms, enterprise SaaS. Each era exposed a new bottleneck. Each created a new foundational layer.
1990s
Systems of record
exposed
Operational fragmentation
Businesses ran on disconnected workflows. CRM and ERP centralized operations into a single record.
2000s
Cloud platforms
exposed
Infrastructure scaling
On-prem couldn't scale with demand. Cloud became the foundational compute and storage layer.
2010s
Data warehouses
exposed
Structured analytics
Organizations had data but no unified way to query it. Warehouses made it operational.
2020s
Context infrastructure
exposed
Operational fragmentation
Businesses ran on disconnected workflows. CRM and ERP centralized operations into a single record.
AI outpaced the infrastructure beneath it — exposing the real bottleneck: enterprise context scattered across systems, identities, conversations, and workflows.
Content was always the highest-signal layer. AI made it accessible. It also revealed there was nothing resolving it.
Content was always the highest-signal layer. AI made it accessible. It also revealed there was nothing resolving it.
Every system has context. None of them share it.
Sturdy is the infrastructure layer that resolves context between your tools, models, and data — so your revenue team can finally act on the full picture.
Systems of record
Store what happened.
Rows, fields, and transactions: the durable record of the business. Every enterprise already has them. None of them agree with each other.
Systems of context
Infrastructure principles
The convictions the architecture is built on.
Not values. Doctrine. These decide what we build, what we refuse to build, and where the real problems in enterprise AI actually live.
1
Context must be structured before inference.
Structure is not a post-processing step. Entities, attribution, relationships, and permissions are resolved upstream, before a single token is generated. Everything Sturdy builds follows from this one assumption.
2
AI systems fail at the retrieval layer.
Model quality is rarely the constraint. What reaches the model is. Retrieval is the real surface of failure.
3
Permissions belong in the data layer.
Access control enforced at the point of retrieval, not the application surface. The model only ever sees what the caller is allowed to.
4
Raw corpora are not context.
A pile of documents is not a knowledge layer. Context is resolved, classified, attributed, and permission-aware, or it is just text.
Supporting principles
How we think about the problem.
5
Canonical identity is foundational.
Every signal is worthless until it resolves to one unambiguous entity. Identity is infrastructure, not a feature.
6
Communication is the highest-signal layer.
Calls, email, and chat hold the operational reality of the business that no structured field captures.
7
Determinism over plausibility.
Enterprise decisions need facts traceable to a source, not the most statistically likely sentence.
8
Systems of record are not systems of context.
Traditional software stores operational records. Intelligent systems require resolved context between them.
Team
Builders and operators.
A founding team with experience across enterprise software, operational systems, and infrastructure transitions.
You don't have 12 months to build this from scratch.
Connect what customers say to why your numbers move. Contextual revenue intelligence, ready for any LLM — or running natively in Ask Sturdy from day one.
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