AI agents that do the work — and ask before they act.
We design and build agentic AI: assistants that answer from your own data, and agents that prepare real work — matches, quotes, replies, reports — for a person to approve. Built on top of the ERP, CRM and documents you already run.
Every action proposed, approved and logged
ReadsProposesYou approveYour ERP postsLogged
Bank line · 12,480.00 · “INV 2291 2294 2301”
Match to 3 open invoices
INV 2291 · 4,160.00INV 2294 · 4,160.00INV 2301 · 4,160.00
Checked against the ledger: amounts and customer agree
Where agents earn their keep
Start where the work is repetitive and the rules are clear.
Good first agents take on work your team already does by hand, where a mistake is easy to spot and cheap to fix.
Finance operations
Match payments to invoices, explain the exceptions and prepare the month-end close. The agent proposes; your team approves; your ERP posts.
- Reconciliation
- Payables
- Month-end
Orders and customers
Read incoming orders and emails, check stock and prices, and draft quotes and replies for review.
- Order intake
- Quotes
- Replies
Questions about the business
Ask in plain language — “which orders are at risk this week?” — and get answers computed from your own data, with the sources shown.
- Reporting
- Ask your data
Documents and knowledge
Search and answer across contracts, specifications and policies, quoting the passage each answer relies on.
- Search
- Citations
Inboxes and paperwork
Classify, extract and route invoices, delivery notes and forms — and flag the ones that don’t fit.
- Extraction
- Routing
Systems that don’t talk
Read across systems that are hard to integrate, map their data to each other and show where they disagree.
- Connectors
- Data mapping
How we build agents
Built to be trusted with real records.
The model plans; your systems calculate.
Numbers come from your ERP, your database and your rules — never from the model’s memory. The AI chooses which query or calculation to run; tested code runs it.
Read broadly, write narrowly.
Agents can read widely. They change records only by proposing: a person, or code you have approved, commits the change.
Evaluated before launch.
We agree how to measure the agent — accuracy on your real cases, cost per task, the failures that matter — and test against that set on every change.
Every step on the record.
Who asked, what the agent read, what it proposed and who approved it: logged, and open to review.
Untrusted text stays data.
Emails, documents and bank lines are treated as data, never as instructions. Personal details are masked before they reach a model unless the task needs them.
No lock-in.
Commercial models, or open models on your own infrastructure, chosen per task. The prompts, evaluations and tools are yours.
Start small
Climb the trust ladder one step at a time.
An agent becomes more valuable as it does more — and so does the cost of a mistake. We move up one step at a time, process by process, when the evidence says it’s safe.
- 1
Answer
A read-only assistant over your data. It answers questions and shows its sources. Low risk, quick to prove.
- 2
Recommend
The agent suggests what to do next, with its reasoning and evidence. People decide.
- 3
Prepare
The agent drafts the work — matches, quotes, replies, postings — ready for one-click approval.
- 4
Act within limits
Named, low-risk actions run on their own, inside thresholds, with a stop switch and a full audit trail.
Most businesses should start at step one or two. We’ll tell you honestly where yours is.
How an engagement runs
From one process to an agent your team relies on.
Pick one process
A working session to choose the process where an agent pays for itself. You leave with a one-page memo, whatever you decide.
Prove it on your data
A short, fixed-scope pilot on your real records, scored against an evaluation set agreed up front. If it doesn’t clear the bar, we say so in writing.
Build it properly
Connectors, permissions, approvals, audit trail, monitoring and cost controls — the parts that make a demo safe to run.
Hand it over
Runbooks, evaluations and dashboards your team can run. Change a prompt, add a test case or swap a model without calling us.
Honest about the limits
What agents don’t do.
They don’t remove the plumbing.
If a system has no way in, an agent can’t read it. We still build the connector — the agent saves the mapping work, not the access work.
They don’t settle disagreements on their own.
If your ERP says 100 and a spreadsheet says 110, the agent shows the conflict. A rule or a person decides which is right.
They aren’t free to run.
Models, evaluations and monitoring cost money every month. We estimate the cost per task before you commit, and track it after launch.
In our own product
We build the same way into yarn.ai.
yarn.ai, our operations platform for textile and garment makers, follows this pattern: AI reads and explains, deterministic engines do the calculations, and a person approves every write.
It’s in beta. We mention it because it’s where we hold ourselves to the same rules we propose to you.
About yarn.aiQuestions
Questions about AI agents
What is agentic AI?
Agentic AI is software that uses a language model to plan and carry out multi-step tasks — reading data, calling tools and preparing results — rather than only answering a single question. In the systems we build, agents propose and people approve anything that changes a record.
How is an AI agent different from a chatbot?
A chatbot answers in text. An agent works with your systems: it looks things up, runs calculations through your tools and prepares actions such as a match, a quote or a reply, ready for approval.
Is it safe to let an AI agent near our ERP or accounting data?
It can be, if the agent can’t post on its own. We give agents read access and a way to propose; your ERP’s own rules and a person’s approval still decide what is posted, and every step is logged.
Which systems can you connect to?
Any system with an API or a database we can reach; where there is none, we build a connector. In our own product we are building connectors for Microsoft Dynamics 365 Business Central and Tally.
Which AI models do you use?
We choose per task: commercial models through their APIs, or open models on your own infrastructure when data must stay in-house. Everything is designed so the model can be swapped.
What does an agent cost to run?
It depends on volume and on the model. We estimate the running cost per task during the pilot, so you know the monthly bill before you commit.
Do we need clean data first?
No. A read-only assistant is often the quickest way to find out where your data disagrees. Fixing the sources that matter becomes part of the plan.
Will agents replace our staff?
Our agents prepare work for people to check and approve. The aim is to take away the copying, matching and chasing — not the judgement.
Bring us the process that eats your team’s week.
Tell us where the hours go. In a free working call we’ll look at whether an agent can take it on, what it would need and what it would cost to run — and send you a one-page memo.