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AI Workflows

The manual work, handled by AI you own

We build AI workflows into your own stack, code you own. They read the documents and data that still arrive messy, pull out the fields you need, and drive the next action automatically, with your team approving only the exceptions.

See our work

Challenge

The work doesn't scale, because a person has to do it

The information you run on arrives messy. It lands in an inbox, as PDFs, as documents where every sender uses a different format, and someone has to read each one, type it into your systems, and check it by hand.

It is slow and it is fragile. Month-end drags, only one person really knows how it works, and you check everything twice and still find errors. You can't hire your way out of it, because the volume only grows.

So the work that should just flow, from document to system to the next action, waits on a person every time.

What we do

We build the system that reads, joins, and acts

Let's find out

Not sure what in your process is worth automating?

Read whatever comes in

Invoices, supplier documents, contracts, statements, forms, notes, in any format they arrive, extracted into clean, structured data your systems can actually use.

Join what never connected

Information pulled together across the tools and records that were never built to talk to each other, so the full picture finally lives in one place.

Drive the next action

The workflow does not just read, it acts. It matches invoices, updates records, routes cases, flags risks, so the routine runs straight through instead of sitting in a queue.

Keep a human in the loop

It handles the routine on its own and escalates the exceptions to your team, and every step it takes leaves an audit trail.

We build into the stack you already run, your cloud, your systems, and your data.

  • AWS
  • Azure
  • Google Cloud
  • HubSpot
  • Microsoft 365
  • Shopify
  • Snowflake
  • Databricks
  • Your Stack

How we build it

Anyone can demo AI. Making it reliable on your documents is the job

The hard part isn't the model. Everyone runs the same ones now. It is making the output reliable on your documents, and safe to trust with real decisions. We practise eval-driven development, the standard the best AI teams hold to. Here is what that means.

Measured

Proven on your documents first

Before we build, we measure accuracy field by field on an evaluation set of your real documents. You see the worst cases up front, not a demo.

  • Field by field
  • Your real documents
  • Worst cases first

The model reads, code checks

Extraction is forced into a schema, then deterministic code checks it against your rules. A guess that breaks them is caught by logic you can inspect, not posted.

Low confidence goes to a human

Every field carries a confidence score calibrated against your evaluation set. Clear cases pass straight through, uncertain ones reach your team, and their fixes become new test cases.

It runs in your cloud

The whole workflow runs inside your environment. Your documents never leave your cloud, nothing you process trains anyone's models, and re-running a batch never double-posts.

We watch it after launch

Accuracy is tracked in production and sampled for review, no model or prompt change ships unless it beats your evaluation set, and every run leaves an audit trail, traceable to the source document.

You own all of it, outright

The code, the pipelines, and the evaluation sets live in your stack, documented and handed to your team. No per-document tax, and nothing that walks out when a vendor is acquired.

How we engage

You see the accuracy before you commit to a build

Start with Proof Week

  1. Proof Week

    One week, fixed fee. We scope and run an evaluation on a sample of your real documents.

  2. The accuracy report

    Measured accuracy on your hardest cases, and a straight go or no-go, before any build.

  3. Build

    We build the workflow into your stack, with the checks and the human-in-the-loop above.

  4. Production

    Monitored, owned by you, your team set up to run it.

Proof

A sample of what we've shipped

All case studies

DTC E-commerce7 min read

A Support Agent That Resolves Tickets in Your Voice and Never Invents a Policy

A governed AI agent that resolves the common tickets end to end using facts pulled from Shopify, takes only the actions your policy allows, escalates everything else, and states zero facts it did not verify in code.

60%Of tickets resolved end to end
  • AI Agent
  • Governed
  • Facts-of-Record
  • Shopify
Read

Dental7 min read

A 24/7 Front Desk That Books Patients and Refuses to Play Doctor

A governed AI assistant that answers admin enquiries from approved practice information and books through Dentally, escalates anything clinical with a hard safety gate, and never gives clinical advice, with staff reviewing every non-templated reply.

70%Of enquiries resolved without the front desk (booking, FAQs, hours)
  • AI Workflow
  • Governed
  • Clinical-Safety Gate
  • Dentally
Read

Off-the-shelf tools give everyone the same thing. The advantage is a system built around how you actually work.

85%

85% of companies expect to customise AI agents to fit the unique needs of their business.

Deloitte, State of AI in the Enterprise, 2026

Engineering edge,
without hiring a team.

Numbers you can trust, operations that run themselves, all built into your stack and owned by you. Tell us where you want the edge, and we'll build it.

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  • Quick response

    You'll hear from an engineer the same working day, not a sales sequence.

  • Clear next steps

    A short call, then a plan, and we start with Proof Week or a full build.

  • Fixed fee first

    A fixed fee agreed before anything starts, for Proof Week or the full build.