Search inside your product
Add natural language search across your customer's own records, returning ranked results with the passages that matched each query.
Capability
AI added to the systems you already run.
We add AI to existing products and internal systems, connecting model APIs, retrieval and agents to the software your team already depends on, without a rebuild.
Overview
The hard part is the integration.
Adding a model call is easy. Making it work inside a real product is the work: authentication, data access, latency, cost, error handling, privacy and the interface your users actually see. Most of that has little to do with the model itself. A feature that ignores those details will work in a demo and fail in daily use.
We come in at that layer. Whether you are adding a feature to a mature application or connecting an approved model provider to your internal tools, we handle the plumbing, the safeguards and the operational side, and we leave the system maintainable. Your team keeps ownership of the product; we make the new capability fit.
Works with your stack
Python, TypeScript, Node.js or .NET services, connected to the frameworks and databases your team already maintains.
Provider choice kept open
We can integrate the major model providers, or models hosted in your own cloud, and keep the choice replaceable.
Security from the start
Least-privilege access, secret management and clear data boundaries, agreed with your security team before anything is connected.
Latency and cost control
Caching, streaming and sensible routing keep responses quick and spending predictable as usage grows.
Observability built in
Logging, tracing and alerts for model calls, so failures and slow responses surface before your users report them.
Rollout you can reverse
Feature flags and a gradual release let you test with a small group and turn back if something looks wrong.
Use cases
Where integration work pays off
The best starting points are features your users already want, sitting next to data the model can safely reach.
Drafting in existing tools
Put a drafting step into the editor, inbox or ticket view your team already uses, with the person in control of the result.
Model access for internal teams
Give your engineers a governed way to call approved models, with keys, quotas and logging handled centrally for the whole company.
Connecting agents to internal APIs
Expose the right internal systems to an agent through safe, scoped tools, using MCP or your own API layer.
How we work
A practical path into production.
Discover
We review the existing system, the data it holds and the security constraints, then pick a first feature worth adding.
Design
We design the integration points, the data flow and the failure behavior, and confirm the approach with a small proof.
Build
We add the feature behind a flag, test it against real usage and measure quality, latency and cost as we go.
Support
We run a gradual rollout, watch the metrics and keep the integration current as providers and models change.
FAQ
Common questions.
Can you add AI to a legacy system?
Often, yes. If the system has an API or a database we can reach safely, we can usually add AI features around it without a rewrite.
Do we need to choose a model provider first?
No. We can help you compare options against your data and cost requirements, and we keep the integration flexible.
How is our data protected?
Access is scoped, secrets are managed properly and data flows are agreed with your security team. We can keep processing inside your cloud.
Will this slow our application down?
We design for it: streaming where it helps, caching where it is safe and timeouts so a slow model never blocks the rest of the page.
Next step
Start the conversation
Have a product that should be using AI?
Tell us what you have and what you want it to do. We will give you a clear view of what integration involves.