AI & Agents
An agent is AI that can act: read an order, draft a refund, match an invoice, update stock. We build agents that act inside the systems you already run, with spending limits, approval steps and a log of every action.
Spend today
$12.40
Budget
$50
Confidence
0.92
Sources linked: 3 · Logged to audit trail · Reversible
What we hear
We tried a chatbot. Nobody trusts its answers because it can't show where they came from.
Two people spend every Monday matching supplier invoices to purchase orders.
Support tickets wait a day because someone has to look up the order in three systems.
Leadership wants an AI plan, but nobody knows where to start or what it will cost.
What we do
Pick one, or let us handle all of it.
Agents that handle one process end to end, such as order exceptions, invoice matching or support triage, and hand risky steps to a person.
Assistants and automations in Odoo, ERPNext and Shopify that use your real records, not a copy pasted into a chat window.
Secure Model Context Protocol servers so Claude, ChatGPT and other assistants can read and act on your systems within rules you set.
Turn invoices, purchase orders and emails into clean records, with confidence scores and review queues.
Answers grounded in your records and documents, with links to the sources behind every answer.
Budgets per agent, approval rules, audit trails and alerts, so you can see what AI did and what it cost.
Proof, not promises
Every delivery ends with evidence you can check yourself, not a slide that says it went well.
A typical project
Week 1
Pick one process
We choose the process with the clearest payoff and measure today's baseline: time spent, error rate, cost.
Weeks 2–3
Build the agent
Tools, approval rules and spend limits, built and tested on a staging copy of your systems.
Week 4
Shadow run
The agent works alongside your team without acting. We compare its decisions with theirs.
Weeks 5–6
Go live and report
Production with limits switched on, then a before/after report so you can decide what's next.
How we let AI touch your data
AI that can act needs limits. These are built into the code, not written in a policy nobody reads.
AI explains, code acts
The model suggests and explains. Tested code makes the change, so every action is predictable.
Always show the evidence
Every AI answer links to the records it used. If it can't show its sources, it doesn't act.
People approve risky actions
Refunds, price changes, deletions and payments wait for a named person to approve them.
Everything is logged or reversible
Each action is written to an audit trail, with budgets and limits per agent. Most can be undone in one step.
Say when unsure
Low-confidence answers are flagged and handed to a person instead of guessed.
Pricing
Fixed scope, fixed price, a clear result. A good way to start before a larger project.
One AI agent working inside one real process, with approvals, budgets and an audit trail.
Evidence · AI action log and a before/after report on time saved and error rate
Free · reply in 3 business days
Tell us about your systems and processes. Our engineers review them and send you a short, honest assessment: the best places to start, the risks, and what your data needs first.
FAQ
The AI doesn't only answer questions; it takes steps in your systems, like drafting a credit note or updating a delivery date. Every step runs through tested code, and risky ones wait for a person to approve.
Claude, OpenAI models and open models, chosen per task on accuracy, cost and where your data may go. You are not locked to one provider.
Only what a task needs, sent to providers you approve. Where data must stay in-house, we use self-hosted open models.
With the AI pilot: one agent, one process, 4–6 weeks, and a before/after report so you can decide whether to go further.
A 30-minute call with an engineer. You'll leave with a clear next step, even if it isn't us.