Data and permission audit
What can be retrieved, by whom, and what must never cross a tenant boundary. Retrieval that ignores your permission model is a data-exposure incident with a chat interface.
AI features inside a product that already has users.
Adding AI to a live product is harder than starting with it. You have existing data models that were never designed for retrieval, permissions the feature must not leak around, latency budgets your users already feel, and a support team who will absorb whatever the model gets wrong.
We add AI features to products in production: retrieval built on your real data and permissions, evals written before the feature so regressions are caught by CI rather than by customers, cost ceilings per user and per tenant, and a feature flag that turns it off without a deploy.
Talk to Anupam →Fixed fee, scoped in writing before anything starts. No hourly billing, no verbal scope.
Scope
What an integration engagement covers.
Retrieval and evals together
Chunking, indexing and ranking tuned against a graded eval set built from your real queries. The eval set ships with the feature and runs in CI.
Cost and latency ceilings
Per-user and per-tenant spend caps, caching where it is safe, and a latency budget agreed before build, so the feature does not quietly become your largest line item.
Flags, traces and rollback
Feature-flagged rollout, request tracing, and an off switch that does not require a deploy. Documented so your team ships the next iteration without us.
Scope your AI feature on a qualifying call.
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Fixed fee, scoped in writing before anything starts. No hourly billing, no verbal scope.
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