AI in your product
LLM features, agents and automation that ship
AI that does real work inside your software
We design and build AI features for new and existing products: from a smart search over your documents to agents that complete tasks across your systems.
We start from the problem, prove it on your data, and ship it with the testing, guardrails and cost control a production feature needs.
What we build
LLM features
Features your users notice: answers grounded in your own documents and data, smart search, summaries, drafting, and pulling structured data out of emails, PDFs and forms.
AI agents
Assistants that do multi-step work, not just chat: they look things up, call your systems through their APIs and hand off to a person whenever a decision needs a human.
Automation
Repetitive back-office work done by software: triaging support tickets, processing documents, routing requests and preparing reports, with people reviewing what matters.
AI in your existing software
No rewrite needed. We add AI features to the product you already run, behind your existing login, permissions and data rules, and ship them step by step.
How we keep it reliable
Measured, not guessed
We build a test set from real examples and score every change against it, so you know the feature is getting better before your users see it.
Your data stays yours
We choose providers and setups that fit your data rules, from hosted models with no-training settings to open models running on your own infrastructure.
Costs you can predict
We pick the smallest model that does the job, cache what can be cached and track cost per request, so the bill grows with value, not by surprise.
How a project runs
Find the right use case
We look at where your team or your users lose the most time and pick the problem where AI helps most, and where it would not.
Prototype on your real data
You see a working prototype on your own documents and cases, with an honest read on what it gets right and wrong, before you commit to a full build.
Build it into the product
We turn the prototype into a production feature: integration, security, monitoring, fallbacks when the model is unsure, and a clear way for users to give feedback.
Improve after launch
We watch quality and cost in production and keep tuning prompts, data and models as usage grows and better models come out.
Common questions
Do we need a lot of data to start?
Usually not. Most useful AI features today work with the documents, tickets or records you already have. We check this in the prototype step.
Which AI models do you use?
We are not tied to one provider. We compare models for your use case on quality, speed, cost and data requirements, and design the feature so the model can be swapped later.
Can you add AI to software another team built?
Yes. We start by reading your code and architecture, then add AI features in a way your team can maintain. See also software modernization.
