AI product & software engineering
Build AI into an application people can use: authenticated requests, scoped retrieval, structured responses and confirmed actions. The interface shows progress and evidence while the backend enforces permissions and business rules.
A customer portal needs current account data, a useful interface and reliable actions. Retrieval and tool execution must respect the same identity and permissions as the rest of the application.
Inside the system
A synthetic order-status portal returns evidence and creates a support case only after user confirmation.
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Authenticate the request on the server and resolve the permitted account. Treat browser-supplied record IDs as requests to authorize, not proof of access.
Read the live record and retrieve approved policy passages. Enforce account access before any content reaches the model, and keep source identifiers attached.
Generate a typed answer with citations and a proposed action. Stream progress into the interface, but enable actions only after the complete result passes validation.
Show the action payload for confirmation. Recheck account access in the server-side tool, record the operation and show success only after the helpdesk confirms the case.
Inspect the behavior
Inspect how the same portal handles a confirmed action, missing evidence and an unauthorized account reference.
Illustrative system · Synthetic inputsAn authorized account user asks about ORD-7324, reviews the cited status and confirms the proposed support-case payload.
The response cites the order and policy. The simulated helpdesk confirms TKT-238 after user confirmation and a fresh account-access check.
Verification specification
We turn expected behavior into repeatable tests and operating controls. Acceptance thresholds are agreed for your workflow, data, and the consequences of an error.
Request records from a different account through UI and API.
Reject access before retrieval or model context construction.
Test malformed output, missing passages and unsupported claims.
Validate the complete schema and each displayed citation.
Change the proposed payload or revoke access before confirmation.
Reauthorize and reject stale or altered action payloads.
The handover
A defined implementation, reviewable evidence, and an operating model your team can own.
How we workFrontend, authenticated backend, data model and scoped integrations.
Retrieval, typed outputs, streaming states and action-confirmation flow.
Software tests, AI evaluations, deployment configuration and source documentation.
Architecture decisions
Yes. We review the architecture, dependencies, test coverage, and release process first. New services and AI features are designed around existing data contracts, identity systems, and deployment constraints.
We compare options against representative tasks and your data requirements. Output quality, tool support, latency, hosting constraints, and operating cost guide the choice. Conventional application code handles deterministic business logic, while model-dependent behavior gets its own evaluation coverage.
Talk to an engineer
Bring a workflow, a product brief, or an AI system that needs to ship.
