AI development
We build AI into the systems you already run.
Assistants connected to your own data, search that answers from your documents, agents that do one job well, and automation that turns paperwork into records — with the permissions and review a business needs.
Your team and your apps
Chat, copilots inside your screens, agents on a schedule
Orchestration
Agents, tool calls, guardrails, human sign-off
Connectors
MCP servers · a RAG index · your APIs
Your data
Database · documents · the tools you already use
What we build
Every part of an AI system, built to fit yours.
MCP servers
Connect Claude, ChatGPT or your own assistant to your database, documents and tools through the Model Context Protocol, with the same permissions a person would have.
Model Context Protocol · tools · resources
See how it worksRAG systems
Search that answers from your manuals, contracts and records — and shows the passage it used, instead of a model guessing.
Embeddings · vector search · citations
See how it worksDedicated agents
Agents with one job each — framing a request, reviewing a change, auditing a plan — working from rules written for your project.
Tool use · multi-step · human sign-off
See how it worksAssistants and copilots
An assistant inside your own system that knows your data and your vocabulary, not a general chatbot in another tab.
In-app chat · your data · your language
Document automation
Invoices, forms and contracts read into structured records, with anything uncertain flagged for a person.
Extraction · validation · review queue
See how it worksClassification and routing
Tickets, emails and requests sorted by what they are about and sent to whoever handles them.
Triage · tagging · routing
Model integration
Claude, OpenAI or open models behind one interface, so the model can change without rewriting the system.
Claude · OpenAI · open models
Evaluation and guardrails
Permissions, logs of every call, and tests on the answers themselves before anything reaches your users.
Permissions · audit log · evals
See how it works
MCP servers
Your assistant, reaching your systems — with your rules.
An MCP server is the bridge between an AI assistant and the software you run. It decides what the assistant can see and do, and it does it on behalf of the person asking.
Someone on your team asks
“Which orders from last week still have not shipped?”
Your MCP server
Checks what that person may see, and calls the one tool that answers it.
Your systems
Database · documents · the tools you already use
The answer comes back — With the records it came from, so it can be checked.
RAG
Answers from your own documents, with the source.
Retrieval-augmented generation: your documents are indexed once, the relevant passages are found for each question, and the answer is written from them — citing where each part came from.
Your documents
Manuals, policies, contracts, records
Indexed
Split into passages and made searchable by meaning
Retrieved
The passages that answer this question, and no others
Answered
Written from those passages, with citations
Example
How many days does a customer have to return an item?
Thirty days from delivery, if the item is unused. Custom orders cannot be returned.
Agents
Agents with one job each, and a person who signs off.
A general chatbot does everything a little. An agent built for one job — with its own tools and the rules written for your project — does that job properly, and hands the decision back to a person.
- A request arrives
- Requirements agent
- The work is done
- Review agent
- A person signs off
Requirements
Turns a request into what is actually true in your system, the approach, and the questions nobody has answered yet.
Review
Checks every change against the rules written down for your project, before a person does.
Audit
Flags when the plan and the work have drifted apart, so a stale note is never read as current.
Document automation
Paperwork in, records out.
Documents are read into the fields your system needs. What the model is sure of goes straight in; what it is not sure of waits for a person, with the reason shown.
Example
Record created
- Supplier
- Northwind Supplies
- Invoice no.
- INV-20417
- Date
- 12 Sep 2026
- Total
- $1,240.00
Needs a person — The tax ID does not match the supplier on file.
Built to be trusted
Your data stays yours, and a person stays in charge.
Same permissions as a person
The assistant can only reach what the person asking could reach themselves.
Every call logged
What was asked, what was looked up, and what was answered — kept and reviewable.
A person approves what matters
Anything that changes a record or leaves the building waits for a person.
Answers are tested
The answers themselves are checked against known cases before a release, like any other code.
Tell us what you would hand to AI first.
The draft takes a few minutes. Describe the work — the questions your team keeps answering, the documents you keep retyping — and you get a written scope back.
Start a draft