AI agents that take the keying and chasing off your team

We build AI agents that read the paperwork, sort the inbox and watch the line, then hand the result to a person to approve. Each one is scoped to a single job with a measurable outcome, so it has to earn its place on your P&L. Where AI is the wrong tool, we say so and use plain automation instead.

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Sound familiar?

Situations this work is built for. If a few of them describe your operation, you are on the right page.

  • We key every scale ticket in twice.

  • Bills of lading come in as photos and PDFs and someone retypes them.

  • Our quote requests sit in a shared inbox until somebody has time.

  • We bought an AI tool last year and nobody trusts what it says.

What we build

Document intake
Bills of lading, scale tickets, invoices, packing lists and purchase orders are read, matched against your records and posted to your system. Low-confidence fields go to a reviewer with the source image beside them.
Inbox and email triage agents
Agents that sort incoming mail, pull out order details, draft replies and route exceptions to the right person. Nothing goes out to a customer without the rules you set.
Quoting assistants
Assistants that assemble a quote from a request, your price lists and commodity pricing, and show the working so a salesperson can check it in seconds.
Computer vision on lines and conveyors
Cameras on sorting lines, conveyors and weigh stations to count, classify or flag contamination and defects. We are direct about lighting, camera placement and the accuracy you can realistically expect.
Forecasting
Demand, inbound volume and downtime forecasts built on your own history, shown next to a simple baseline so you can see whether the model beats it.
Review, guardrails and cost control
Human-in-the-loop review queues, evaluation sets built from your real documents, audit trails, and usage budgets so the monthly model bill is never a surprise.

How the work runs

  1. Assessment

    2–3 weeks

    We interview the people who do the work, map the documents and systems involved, and test a sample of your real paperwork. You get a written findings report with a prioritized roadmap that separates good AI candidates from jobs better done with plain rules.

  2. Build

    6–12 weeks

    We build the first agent against an evaluation set drawn from your own data. Working software is shown every week, running in shadow mode beside your current process before it touches anything live.

  3. Rollout

    2–4 weeks

    The agent goes live with a review queue, confidence thresholds and clear escalation paths. Your team keeps the final say until the numbers justify loosening it.

  4. Run

    Monthly

    Ongoing monitoring, evaluation against fresh documents, model updates and cost reporting on a monthly retainer, with new document types added as you need them.

Tools we commonly use

  • Claude
  • OpenAI
  • Python
  • TypeScript
  • PostgreSQL
  • pgvector
  • OpenCV
  • PyTorch
  • Temporal
  • Cloudflare Workers

We pick tools to fit what you already run and what your team can maintain.

Where this matters most

Questions buyers ask

How do you price AI projects?

Assessments are a fixed fee and builds are scoped as fixed-scope or phased work, with an ongoing retainer for run and support. We do not publish prices because the right shape depends on document volume, systems and review needs. The assessment ends with a written scope you can take to anyone.

What happens when the AI gets something wrong?

It is routed to a person before it can do harm. Every agent has confidence thresholds, a review queue that shows the source document next to the extracted values, and an audit trail of who approved what. We also measure error rates on your real documents before go-live, so you know what to expect.

Will this replace our people?

The goal is to remove retyping and chasing, not to remove the people who understand the business. Staff move from keying data to reviewing exceptions, which is the part that needs judgment. We design the review step with the people who will use it.

Is our data used to train public models?

No, we configure model providers so your documents are not used for training, and we tell you which providers touch which data. Where a document should never leave your network, we can run open models inside your own environment. You own the prompts, evaluation sets and code we build.

Other services

Further reading