Local LLMs and AI agents for your business
AI that does real work inside the software you already run.
We connect your company's data to models you control, then build agents that act in your systems, with people approving the steps that matter.
Why pilots stall
A chatbot can't tell a customer where their order is
It can't see your order system. Most company AI pilots stop at exactly this point: the model is capable, but it has no access to the information or the tools the job needs.
That gap is what we work on. We get the right data in front of the model, connect it to the systems where the work happens, and decide with you which actions it may take alone and which wait for a person.
The result is less glamorous than a demo and more useful: an invoice entered without retyping, a support ticket answered from your own records, a report that builds itself on Monday morning.
What it looks like
One supplier invoice, start to finish
Three agents and one person. The agents read, match and draft. The accounts lead makes the one decision that involves money.
- Mismatches are flagged, not guessed at.
- Approval happens where the person already works.
- Every step is logged with the system call behind it.
- 09:14:02Inbox agentNew email from Northside Supplies with one PDF attached.classified → supplier invoice
- 09:14:05Document agentRead the invoice.INV-2291 · 14 lines · total 482,300.00
- 09:14:07Finance agentMatched it against the purchase order in the ERP.erp.get_purchase_order("PO-7731") → 13 of 14 lines match
- 09:14:08Finance agentLine 9 bills 40 units. The order was for 30.flagged · approval required
- 09:31:40Accounts leadApproved 30 units, rejected the extra 10.approved in Slack
- 09:31:42Finance agentCreated the bill for the approved amount.erp.create_bill(…) → draft BILL-0815
- 09:31:44Inbox agentDrafted a reply to the supplier about the 10 units.queued for sender review
- 09:31:45Run complete7 steps, 1 person involved, nothing retyped.
What we build
Six kinds of work
Agentic teams
Several narrow agents instead of one general assistant.
Each agent owns one job, has its own permissions and passes work along to the next. Narrow agents are easier to test, easier to trust and easier to fix when something goes wrong.
Private and local LLMs
Open-weight models running on infrastructure you control.
For data that shouldn't leave your network, we deploy models in your cloud account or on your own hardware. It costs more to run than an API, so we only recommend it where the data warrants it.
Data lakes
One governed store for the information your AI will need.
We map where your data lives, build pipelines out of the databases, CRM, ERP, documents and APIs that matter, and keep it synced and permissioned. Most projects spend more time here than anywhere else, and it is the part that decides whether the rest works.
RAG pipelines
Answers drawn from your own documents, with the source attached.
We index your documents, policies and product data, keep the index in step as they change, and retrieve the right passages before a model answers. Each pipeline is tested against real questions from your team before it ships. Fine-tuning comes in only where retrieval isn't enough.
Workflow automation
Whole processes, start to finish.
A request comes in, the system works out what it is, looks up what it needs, acts in the right tools and reports back. We automate the full path for things like support tickets, invoices, onboarding and internal requests.
System integration
Agentic teams working inside the tools you already use.
We connect agentic teams to your in-house and third-party tools: CRM, ERP, helpdesk, website, WhatsApp and internal apps, through their APIs or, where there isn't one, through the database. You keep your stack.
Agentic teams
Agents organised the way your departments are
Each agent has one job and the permissions for that job. They pass work to each other the way colleagues do.
We usually start with one team and one process, run it with a person approving every action, and loosen that as the agents prove themselves.
The stack
Seven layers between your systems and your people
Requests come in at the bottom, from staff and customers. Work happens in the applications at the top. Everything in between is what we build and maintain.
- L01
Your applications
CRM, ERP, HRMS, website, databases, APIs
- L02
Integration
Connectors, credentials, events, rate limits
- L03
Data and knowledge
Pipelines, vector search, metadata, access rules
- L04
Models
Hosted or private LLMs, fine-tunes, retrieval, evaluation
- L05
Agents
Narrow roles, tool permissions, hand-offs
- L06
Workflows
Steps, approvals, retries, audit log
- L07
People
Staff, customers and managers, in the tools they already use
How a project runs
Small first, then wider
The first build is usually one process for one team. Once that runs reliably, we widen it.
Map
We sit with the people who do the work and trace one process end to end, including the systems and data it touches.
Choose
We pick the one or two places where automation would save the most time with the least risk.
Design
Data sources, model choice, integrations, agent roles and approval points, written down and agreed.
Build
Pipelines, connectors, agents and the workflow itself, shipped in small pieces you can try.
Test
Against real past cases, including the awkward ones, before anything touches production.
Launch
Usually with a person approving every action at first, then fewer as it earns trust.
Improve
We review failures and edge cases, adjust and retest.
How we work
What you can hold us to
Most of the ways AI projects fail are known in advance. These are the working rules we use to avoid them.
You own what we build
Code, prompts, evaluation sets, pipelines and any fine-tuned weights are yours, in accounts you control. We document everything and plan the handover from the start, so your team can run it without us.
Evaluations ship with every system
Before we write prompts we write tests: real past cases with known right answers. They give a baseline, catch regressions when a model or prompt changes, and tell you plainly how accurate the system is.
Autonomy is earned, step by step
Each action an agent can take gets a risk tier. High-risk actions wait for a person; low-risk ones run alone once they have a track record. Tiers are written down and reviewed, not left to defaults.
Security against named threats
We design against the OWASP Top 10 for LLM applications and its agentic counterpart: prompt injection, excessive agency, tool misuse and supply-chain risk from connectors and MCP servers.
Model choice stays open
Systems are built so the model behind them can be swapped, hosted or open-weight, with the evaluation set deciding whether a change is safe. Providers change their offerings; your system shouldn't break when they do.
Regulation, dated
Compliance notes carry the date they were last checked. Rules such as the EU AI Act have moved deadlines more than once, so we design to the current text and say when we last read it.
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