Language models that know your business and do real work.
A chatbot that guesses is a liability. An assistant grounded in your documents, with tools it can call and limits it cannot cross, is a colleague. We build the second kind: inside your product, your support desk, your back office, with evaluation so you know when it gets worse before your customers do.
What it is
AI integration means putting a language model to work on your data and inside your systems, rather than in a separate chat window. That covers three things that often get mixed up. Retrieval: the model answers from your SOPs, contracts, product docs, tickets and call notes, with the source attached, and says so when it does not know. Tools: the model can look up an order, draft a reply, create a ticket or update a record, through functions you define with explicit permissions. Agents: the model runs a multi-step task, checking its own work, pausing for a person at the steps you mark.
The craft is in the parts around the model: how documents are chunked and indexed, what the model is and is not allowed to do, how answers are checked, and how you measure quality over time. We spend most of the build there. The model itself is swappable, and we pick it per task on cost, speed and accuracy on your examples.
What we build
- Knowledge assistants. Ask anything about your SOPs, policies, products or past projects, from Slack, WhatsApp, your intranet or inside your app. Every answer cites its source.
- Support triage and drafting. Classify incoming tickets and chats by intent and urgency, answer the routine ones with sources, draft the rest for an agent to approve.
- Document intelligence. Extract structured data from contracts, invoices, forms, photos and scans; flag what does not match; feed the result into your systems.
- Agents with tools. Research a lead, reconcile a statement, prepare a quote, chase a document: multi-step tasks with function calls, limits and a human checkpoint.
- Voice and vision. Call transcription and summaries, voice assistants on telephony, image and video understanding where it moves a process forward.
- AI inside your product. Features for your users: search that understands intent, summaries, recommendations, copilots, built on your data with your rate limits and costs under control.
How it works
- Collect real examples
Discover starts with the actual questions, tickets, documents or tasks: a few hundred of them. They become the test set every later decision is measured against.
Week 1 - Design the boundaries
What data the model sees, what tools it can call, what it must refuse, where a person reviews. The architecture, model choice and expected running cost are written down and the price is fixed.
Week 1–2 - Build and evaluate every week
Retrieval, prompts and tools are built against the test set with an evaluation score you can see. Weekly demos show real questions answered, including the ones it gets wrong and why.
Week 2–5 - Ship with monitoring
Go live behind a feedback button and a log of every answer. Quality, cost and latency are on a dashboard, and regressions show up before complaints do.
Week 5–6
Typical deliverables
- The assistant or agent, deployed where your people or users already are
- An indexed knowledge base that updates when the source documents change
- Tool definitions with permissions and audit logs for every action the model takes
- An evaluation set and score, re-run on every change
- Monitoring for quality, cost per query and latency
- Documentation, prompts and code in your accounts
Who it is for
- Teams that answer the same questions from a large body of documents, policies or past work
- Support and operations desks with volume that exceeds the people available
- Product teams who want AI features without building the infrastructure from scratch
Who it is not for
- Businesses whose knowledge lives only in people's heads and nowhere written; start with documenting it
- Anyone wanting a model to make irreversible decisions with no human checkpoint
- Simple rule-based tasks, which are cheaper and more reliable as automation
Tools we use
What people askabout AI agents.
How do you stop it making things up?
Retrieval first: the model answers from documents we fetch for the question, cites them, and is instructed to say when the answer is not in them. We measure hallucination on your test set and gate go-live on it. For actions, the model can only call tools you defined, with limits.
Is our data used to train anyone's model?
No. We use provider settings and agreements that exclude your data from training, and we can run open models in your own cloud where confidentiality requires it.
Which model will you use?
Whichever scores best on your examples for the cost. We usually test two or three and show you the trade-off. The build is model-agnostic so you can switch later.
What does it cost to run?
Model usage is metered per query. We report the expected cost per query in the proposal, cache aggressively, and route simple questions to cheaper models. Most assistants cost far less to run than the person-hours they replace.
How do we keep the knowledge up to date?
The index watches the source: a Drive folder, a Notion space, a database, a ticketing system. When a document changes, it is re-indexed. Nobody has to upload anything.
Not sure if it is an agent or an automation?
If the decision follows a rule, it is automation. If it needs judgement over messy text, it is an agent with a person in the loop. We will tell you which, honestly.