Context is the moat: building AI around how your company works
General models know everything in general and your business in particular not at all. Closing that gap is where durable value is created.
A frontier model can pass the bar exam and write production code, yet it does not know your pricing exceptions, your escalation paths, or the three unwritten rules that govern how your best people make decisions. That gap — between general intelligence and institutional context — is where most AI value is won or lost.
Why context is hard to copy
Capability is increasingly a commodity; the leading models are remarkable and broadly available. Your context is not. It lives in your documents, your systems of record, and the tacit knowledge of your team. A system that faithfully encodes that context is difficult for a competitor to replicate precisely because it is specific to you.
How we encode it
We treat context as a first-class part of the system, not a prompt afterthought:
- Grounding in your authoritative sources, so answers reflect your reality rather than the open web.
- Workflow alignment, so the system participates in how work already moves through your organization.
- Continuous tuning, so the system stays accurate as your business changes.
The result
The outcome is an AI system that feels less like a generic assistant and more like a capable colleague who has worked at your company for years — one who knows where things are, how decisions get made, and what “good” looks like in your world.
This is the work we care about most, and it is the work we think will matter most over the next decade.