AI Agents & LLM Systems
AI agents that do real work inside your business.
We build assistants that answer from your documents and agents that act in your systems, then hand off to a person when they should. Every one ships with tests, safety limits and cost controls, because a demo that impresses is not the same as a system you can trust.
Good fit when
Where this work pays off
- Support, sales or operations teams answering the same questions from scattered documents
- Back-office steps that need judgement, not just rules, before a person signs off
- Products that want a chat or copilot experience on top of existing data
- Teams that piloted a chatbot and now need it to be accurate, secure and measurable
What we build
What you get: assistants and agents you can trust
- Knowledge assistants
- Answers from your documents, tickets, wikis and databases with sources cited and permissions respected (the technique is called retrieval-augmented generation, or RAG).
- Agent workflows
- Multi-step agents that plan, use your tools, update your CRM or ERP and escalate to people with a clear record of what they did.
- AI features in your product
- Model selection, structured outputs and streaming interfaces integrated into your web or mobile product.
- Testing and safety controls
- Real-case test sets, automated scoring, limits on what the agent may do and monitoring, so quality is measured, not assumed.
How it works
From a mapped workflow to an agent in production
Discovery
Map the workflow, the data and the ways it can fail. Define what "good" means in numbers.
Working prototype
A narrow version on real data in weeks, scored against the test set before anyone scales it.
Production build
Permissions, monitoring, cost limits and fallbacks. Integrated where people already work.
Operate and improve
Feedback loops and model updates as usage grows and models change.
A typical first engagement
What you need to bring
Discovery is a fixed-fee phase that ends in a written plan and a costed prototype scope. You can take that plan anywhere.
- Access to the documents or systems the agent must read or act on
- One person who owns the workflow and can judge a good answer
- Fifty or more real cases we can turn into a test set
Tools we typically reach for, chosen per project for fit and portability: OpenAI, Anthropic Claude, Open-weight models, LangGraph, pgvector / Pinecone, Python, TypeScript, AWS / Azure / GCP.
Frequently asked questions
How long does it take to build an AI agent?
A focused prototype on real data usually takes three to six weeks. A production system with integrations, security and monitoring typically takes two to four months depending on how many systems it touches.
Can it use our private data without leaking it?
Yes. Answers respect each user's permissions, data can stay inside your cloud account, and we can run open models privately when the use case demands it.
How do you stop the model from making things up?
Answers are grounded in your sources with citations, outputs are constrained to fixed formats, every change is scored against real cases, and low-confidence cases go to a person. We measure the error rate rather than promise zero.
Which AI provider do you recommend?
It depends on the task, speed, privacy and budget. We design so the model can be swapped as the market moves, and we test candidates on your own data before choosing.
Not sure which service fits?
Describe the problem in a few lines. We will tell you honestly whether it is an AI, data, automation or software problem, and what a sensible first step looks like.