AI that reaches your real systems

Most enterprise AI stalls in the same place: the model is fine, but it cannot see anything that matters. We do the two things that fix that, and we have both running in production rather than in a pilot.

Book a 30-minute walkthrough Which one do we need?

Everyone is demonstrating AI this year. Fewer are running it against live systems with the audit trail to show for it.

Two offers, not a programme. Both already in production.

They are frequently bought together and in a fixed order: get the documents into data first, then let people ask questions of it. Either one is a useful thing on its own.

Offer 01

Assistants over your live data

Someone asks a question in plain words and gets the real number back, from the record that exists right now. We build and operate the governed server in the middle: a fixed list of named actions, reads and writes separated, every call logged, nothing changed until a person approves it.

Five servers in production, 200+ governed tools, four industries. Works with Claude, Microsoft Copilot and ChatGPT.

MCP servers and AI assistants Offer 02

Document AI on Azure

Invoices, contracts, forms and scans read into structured data. The extraction is the easy half; the validation layer is what decides whether anyone trusts the output, so doubtful fields go to a person rather than silently into your database.

95%+ extraction accuracy across thousands of real production documents. One team went from 20 hours a week to 2.

Document AI on Azure

Telling them apart

The question is where the answer is stuck. Not which model is best.

If your people already know the answer exists somewhere, and finding it means opening three systems and a spreadsheet, that is an assistant problem. The data is fine; the reach is missing.

If the answer is sitting in PDFs, scans or paper that nobody has turned into data yet, no assistant can help until that changes. That is a document problem, and it comes first. Plenty of companies have both, in that order.

Describe the problem, we will say which Or look at what we have built
No

We do not train models on your data

Nothing is copied into a model. Assistants read your systems live; extraction writes into your own Azure subscription. That is a different business with different risks, and it is not this one.

No

We do not build customer-facing chatbots

Both offers are for your own staff, inside tools they already have. If you need a public support bot we are the wrong studio, and we will say so on the first call rather than the third.

No

We do not start with a strategy phase

One question your team asks every week, in production, in weeks. It widens because people keep using it, not because a roadmap said it would.

The questions that come before the demo. Answered plainly.

Which AI problem do we actually have?
If people already know the answer exists somewhere but opening three systems to find it takes twenty minutes, that is an assistant problem and an MCP server solves it. If the answer is trapped in PDFs, scans or paper that nobody has turned into data yet, that is a document problem and extraction comes first. Many companies have both, in that order: extract, then ask.
Do you train a model on our data?
No. Nothing is copied into a model. Assistants reach your systems live through a governed server that publishes a fixed list of named actions, and document extraction writes structured data into your own Azure subscription. Training on customer records is a different business with different risks, and it is not this one.
Do you build customer-facing chatbots?
No. Both offers are for your own staff, inside tools they already have. If you need a public support bot, we are the wrong studio and will say so on the first call rather than the third.
Which AI assistants does this work with?
Claude, Microsoft Copilot and ChatGPT. The connection layer is MCP, an open standard rather than a vendor feature, so the same server serves whichever assistant your organisation has settled on and keeps working if that choice changes.
Fund compliance · Cygnum Capital
120+ hrs/quarter 5 hrs
−96%

Compliance admin per quarter, across 7 funds and 191+ borrowers, now carrying a 67-tool assistant layer on top.

The honest test is your own systems and one question you actually care about. In half an hour we can tell you which of the two offers it is, whether it is worth doing, and whether the data behind it is in a fit state to be asked.

Sometimes the answer is that the reporting needs fixing first. We would rather say so than sell you a layer on top of a problem.

Related

Planning and forecasting

Budgets, forecasts and write-back where finance already lives: Power BI and Microsoft Fabric, on Acterys. The assistant layer reaches these models too.

CapitalBridge

Our own platform, and the system carrying the 67-tool assistant layer above.

Case studies

Named clients and the outcomes we can publish.