AI, AGENTS & WORKFLOWS

AI & Automation

Practical AI inside real business processes — and an honest answer about when you don't need it.

Most businesses asking for AI don't need a chatbot. They need one specific thing to happen faster.

Usually it's a process where somebody reads a document and types its contents somewhere else. Or answers the same question forty times a week. Or moves data between two systems by hand because the two systems have never been introduced. Those are worth automating, and the return is easy to measure. A general-purpose assistant bolted onto your homepage usually isn't, and it's what gets asked for first.

So we start by finding the thing worth doing, and we'll tell you if the honest answer is a well-written integration rather than a model.

Document search that cites its sources. Answers drawn from your own material — contracts, policies, product catalogues, support history — with a link back to the page it came from. Built so that when it doesn't know, it says so, rather than inventing something plausible. For most businesses this is the highest-value AI project available and almost nobody asks for it by name.

Agents that complete work. Systems that handle a task across several steps and several tools rather than answering one question. Reading an incoming email, extracting the order, checking stock, drafting the reply. These need real guardrails — permissions, spend limits, and human approval at the points that matter — and we build those in from the start rather than after the first expensive mistake.

AI inside what you already run. Summarising, classifying, extracting structured data from messy input, drafting, translating. Usually a few well-placed features inside existing software, not a new product.

Automation without the AI. The unglamorous half, and often the profitable one. Connecting systems that don't talk, processing documents on arrival, routing work to the right person. Some of this needs a model. A lot of it needs someone to sit down and write the integration.

What we'll tell you up front

Running a model costs money per request, forever, and that cost scales with success. Accuracy is a range rather than a guarantee. Anything customer-facing needs a plan for being wrong. And some of what gets sold as AI is a scheduled script with better marketing.

We'd rather have that conversation at the quoting stage than after launch.

What's included

What's included

  • A process audit to find where AI is actually worth the money
  • Retrieval systems over your own documents, with citations
  • AI agents with permissions, spend caps and approval steps
  • Integration with OpenAI, Anthropic and open models — chosen per task, not per preference
  • Workflow automation between the systems you already use
  • Prompt and output evaluation, so quality is measured rather than assumed
  • Cost monitoring and per-request budgeting
  • A fallback plan for when the model is wrong — because it will be

Fit check

Is this the right service for you?

This is for you if

  • Someone on your team spends hours a week on something that feels mechanical
  • Your knowledge lives in hundreds of documents nobody can search properly
  • You handle high volumes of unstructured input — emails, forms, PDFs, applications
  • You tried an AI pilot last year and it quietly went nowhere
  • You want AI features in a product you already sell

This probably isn't for you if

  • The main goal is being able to say you use AI
  • The process you want automated isn't written down anywhere yet — fix that first, it's cheaper
  • You need guaranteed accuracy with no human review, in a context where being wrong is unacceptable
  • Your data is in a state where nobody trusts it — AI will amplify that, not fix it

How it works

How it works

  1. Audit

    We map where the manual effort actually goes. Often it isn't where you think.

  2. Pick one

    We choose a single process with a measurable before-and-after. Narrow and provable beats broad and vague.

  3. Pilot

    Four to six weeks, built to prove or disprove the value. A pilot that fails fast is a good outcome and costs you a fraction of the alternative.

  4. Harden

    If it works: guardrails, evaluation, monitoring, cost controls, and the human-approval points.

  5. Extend

    Next process, with everything learned from the first.

FAQ

FAQ

Will our data be used to train someone's model?

Not on the enterprise API tiers we use, and we'll show you the specific terms for whichever provider your project uses before you commit.

What does it cost to run?

It depends on volume and model, and we'll model it with your real numbers during scoping rather than after. Ongoing cost is part of the quote, not a surprise in month two.

Can it run on our own infrastructure?

Yes, with open models, where the data genuinely can't leave your environment. It costs more and performs somewhat below the frontier hosted models — we'll be straight about that trade.

How do we know if it's actually working?

We agree the measure before building: time saved, volume handled, error rate. If we can't define one, that's a strong signal the project isn't ready.

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Start with one process.

Tell us about the most repetitive thing your team does. We'll tell you honestly whether AI is the answer.

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