Most product AI is a chatbot in the corner that nobody opens twice. This enterprise software vendor didn’t want that. They wanted AI woven into the workflows their customers already used, suggesting the next step, summarizing the messy account, drafting the thing the user was about to write, grounded in each customer’s own data, and trustworthy enough to ship to demanding enterprise buyers. That’s a very different problem from wiring up a generic assistant.
It has to be useful in the flow of work, accurate enough that people rely on it, and governed enough that a security and compliance team will sign off on it touching customer data. Miss any of those and adoption quietly goes to zero.
The challenge
Could AI be embedded directly into the product’s core workflows, grounded in each customer’s own data, governed for enterprise security and compliance, in a way that users actually adopt and rely on, rather than a novelty they try once?
The approach
We designed the copilot into the product’s real workflows, not beside them. It retrieves and reasons over each tenant’s own data to make grounded suggestions inline, with guardrails, auditability, and per-tenant isolation built in, so it’s both useful in the moment and safe enough for the enterprise.
Embedded AI succeeds or fails on adoption. We optimized for the second week of use, not the first demo.
The outcome
The copilot now runs across a six-figure seat base, handling millions of tasks a week, with the majority of users active every week, the rare embedded-AI feature that became part of how people work rather than a checkbox. Because it’s grounded and governed, the vendor can sell it into their most demanding enterprise accounts.
A copilot nobody opens is just overhead. This one became part of the product.
The same grounded, governed copilot framework extends to new workflows and product areas, each new surface reuses the retrieval, guardrail, and isolation layers, so adding intelligence is incremental rather than a fresh integration each time.