Every operation we walk into has the same job hiding in it: a person reading a document and typing what it says into a system. Invoices, BOLs, POs, emails. That job is now automatable — reliably, affordably, and if it has to be, entirely on your own hardware. That's the AI we build. The rest is mostly demos.
None of these need a moonshot. Most of them need a model, a review queue, and some plumbing.
Somebody's whole job is reading a document and typing it into a system. That job can now be checked by a person instead of done by one.
The PO arrives as a PDF, gets keyed into the ERP, then keyed again into a portal. Every retype is another chance to be wrong.
The answer exists — in a 2019 email thread, a shared drive, or the head of the one person who has been here twenty years. Nobody can search any of it.
Contracts, compliance, or common sense say your documents stay in-house. That rules out the SaaS tools — not AI itself.
Copy-paste into a chat window proved the concept. Wiring it into your real systems, with controls and an audit trail, is the part that pays.
The board wants an answer and vendors want a signature — before anyone has checked whether the data can support any of it. Start there instead.
Every build ships with validation rules, a human veto where it matters, and documentation your team can run without us.
LLMs reading the paperwork your team currently retypes.
Self-hosted open-weight models for data that can't leave the building.
Copilots and answers wired into the ERP, WMS, and CRM you already run.
Automation where the model does the reading and a human keeps the veto.
What Lean Six Sigma is to process, this is to your data.
It won't fix a broken process — automating a mess just gives you a faster mess, which is why we sometimes recommend a process improvement pass before any model gets involved. It won't be right 100% of the time, which is why everything we ship has validation rules and a human veto on the calls that matter. And it won't replace your team: the goal is that people stop retyping and start reviewing.
It also isn't a strategy. "We need AI" is not a project; "stop hand-keying 400 invoices a month" is. We scope the second kind — small, measurable, and honest about the cases where the right answer is a boring script rather than a model.
One document type, one queue, a few weeks. A measured pilot beats a platform contract you're stuck with for three years.
AI Builder and Power Automate can carry the first workload inside the Power Platform licences you already pay for.
Review screens, queues, and audit trails are custom software — we build that side too, so the model isn't an orphan.
BOLs, PODs, rate confirmations, and status emails all day long — 3PLs and distributors are where this earns its keep first.
No — it rules out the SaaS tools, which is a different thing. We deploy open-weight models like Llama and Qwen on your own hardware via Ollama: no per-token fees, no data crossing your network boundary, no vendor terms to renegotiate. We run local models in our own operation, so this is a setup we maintain daily, not a slide.
Start with a pilot: one document type or one queue, fixed scope, a few weeks, and a measured baseline so you can judge it honestly. That is a fraction of what a platform contract costs, and it tells you whether the bigger build is worth doing before you commit to it.
A document-extraction pilot typically shows real results in two to four weeks. The model is rarely the slow part — the plumbing into your systems, the validation rules, and the review workflow are where the engineering lives, and that is ordinary software work with ordinary timelines.
No. Practical AI in 2026 is systems integration, not research — the models are built; the work is wiring them safely into your processes. What you keep is normal software: readable code, documentation, and monitoring your existing people or a modest support arrangement can run.
Old systems are our home turf. AI usually sits alongside the legacy core rather than inside it — reading the documents, files, and emails around it, and talking to it through the database, an API layer we add, or the same interfaces your staff use today. No rip-and-replace required.
Real, and managed like any other failure mode. We constrain the model to extraction and drafting rather than open-ended answers, validate output against your business rules, route low-confidence cases to a person, and measure accuracy over time. Nothing we ship answers your customers unsupervised on day one.
We'll tell you whether AI can read it reliably, what a pilot costs, and when the honest answer is a plain script.
