AI Development & Automation
AI that removes work, not AI for its own sake.
Most of what gets called an AI project is really a repeated task nobody has had time to fix. We find that task first, decide honestly whether a model is the right answer, and build the narrowest thing that settles it.
What people come to us with
The situation before the solution
The same forty questions, every week
Your team answers the same queries about stock, pricing, lead times or policy from documents only two people know how to navigate. The knowledge exists; finding it is the job.
Information arrives as paper and PDFs
Purchase orders, invoices and specs come in as scans and email attachments, and somebody retypes them into a system. The retyping is where the errors enter.
A pilot that never reached production
Someone built a promising demo on a laptop. It had no access control, no evaluation, no path into the systems people actually use, so it stayed a demo.
Capabilities
What the work covers
Assistants grounded in your documents
Retrieval over your own manuals, contracts, catalogues and records, with every answer showing the source it came from so a person can check it.
Intelligent document processing
Extracting structured fields from invoices, purchase orders and forms, with a review step for anything the model is not confident about.
Workflow automation
Routing, triage, drafting and classification inside existing processes — the model does a step, your system keeps the record.
AI inside the software you already have
Added to your ERP, portal or admin tool as a feature, rather than as a separate chat window your team has to remember to open.
Evaluation and guardrails
A test set drawn from your real questions, measured before launch and re-run on every change, plus limits on what the system is allowed to do unsupervised.
How we approach it
The order we do things in, and why
- 01
Find the repeated task
We watch the work before proposing anything. The candidates are almost always tasks done many times a week by people who would rather be doing something else.
- 02
Decide whether AI is the answer
A lookup table, a form or a report is often cheaper, faster and more reliable. We will tell you when that is the case rather than build the more interesting thing.
- 03
Build the narrowest useful version
One task, one document set, one clear boundary. Narrow systems can be evaluated; broad ones can only be demoed.
- 04
Measure against a real sample
We assemble questions from your team, score the answers, and show you where it fails before anyone depends on it.
- 05
Integrate and hand over
Into the system where the work happens, with the costs, limits and failure modes written down.
Technologies
What we typically build this with
Questions
Things people ask first
Not without your explicit instruction. We select providers and settings so your content is used to answer your questions and nothing else, and we write the data path into the handover so you can verify it independently.
It will, sometimes. That is why answers cite their sources, why confidence thresholds route uncertain cases to a person, and why we agree up front which decisions the system is never allowed to take on its own.
A general assistant does not know your stock levels, your contracts or your part numbers, and it cannot write back into your systems. The work here is the grounding and the integration, not the model.
It depends on the data and on what you are comfortable with. We will set out the options and their trade-offs, including keeping sensitive records on infrastructure you control, before anything is built.
Usage-based model costs plus hosting. We estimate this from your real volumes during the build and show the figure before launch, because an automation that costs more than the work it replaces is not worth shipping.
Start a project
Have a problem that looks like this?
Tell us what it costs you today.
Two fields to start. We will come back with an approach and an honest view of what it takes — not an instant quote.