Forward-deployed engineers.For industrial operators.
We build one queryable model of how your company runs, use it to surface hidden revenue, then turn trusted answers into tools and automation.
Ingest → Query → Discover → Build → Automate
01 · Ingest
We gather your data, then make it agree.
It starts with a series of analyses, assessments, and — where it helps — on-site workshops, to pull in your data and the streams that feed it. The assessment runs on our own tool, wheredoiuseai.com.
We connect the systems you already run and resolve the same real-world customers, products, locations, and transactions across all of them — your source systems left intact. From that we build an ontology and a knowledge graph: one queryable model of how your company actually operates.
Your systems
- ERP
- CRM
- Service
- Ecommerce
- Inventory
- Spreadsheets
- Product data
Everything in one place
Pulled from every system you run, left exactly as it was.
Made to agree
The same names, dates, and part numbers everywhere.
One record per real thing
“Acme Mfg” across five systems resolves to a single customer.
The Hub
An ontology and knowledge graph — one queryable model of how your whole company operates.
02 · Query
Once your company is a graph, a question that used to mean a day of cross-referencing spreadsheets becomes one query:
“Which quotes went out under our floor margin last quarter, who signed off on each, and which of those deals we won?”
You get a concrete answer — and it runs the same across ten thousand records as across ten.
Who did what, under which constraint, in what window. At scale.
03 · Discover
Start with hidden revenue.
Revenue is the fastest way to prove the model is worth it — the results are measurable, and easy for your own team to validate. So the first questions we point it at are the ones that find money already sitting in your accounts.
- Which customers buy one category from us, but almost none of the next?
- Which historically strong accounts are materially down over the last six months?
- Which accounts show category usage gaps that look abnormal?
- Which customers may be due for a repair, a replacement, or an adjacent product?
Illustrative example · not real client data
NorthStar Fabrication
Cutting-tool spend up 18% year over year, but abrasive purchasing remains near zero — cross-sell abrasives.
Once the model is trusted, the same analysis runs every week, across every account — consistently.
04 · Build
Revenue is the first lever, not the end state.
Once the model exists, every new tool is built on the same foundation instead of a new integration project every time. Same model, new question, new tool, new workflow — across sales intelligence, quoting and orders, vending and inventory, and service and technical knowledge.
The foundation is the hard part, and it is the part we do best. We build the first tools with you, then embed a forward-deployed engineer who builds whatever comes next, right alongside your team. Every tool bolted on is one more thing the model can do.
Start with an assessment, bring on a fractional engineer, or hand us one thing to automate — three ways to work with us.
05 · Automate
Then we turn answers into action.
The first version stays completely human-in-the-loop. Once the data and the logic have earned your trust, you automate only the steps you are comfortable automating — nothing before then.
- 01
Detect
The model flags the opportunity or the exception.
- 02
Explain
It shows the evidence underneath, so the call is reviewable.
- 03
Recommend
It proposes the next-best action.
- 04
Approve
A rep or manager reviews and signs off.
- 05
Execute
A CRM task, an email, or a workflow goes out.
Trust first. You automate on your terms, one step at a time.
Selected engagement
“It feels like a cheat code. We had no idea we could have tools like this built this fast — now we can’t imagine running without them.”
Owner, HUB MFG
Precision manufacturing · client since 2025
We embedded in week one with a site visit and ops shadowing. The quoting system was in production by week two, the rebuilt site and automated vendor pricing by week four.
- 3D quoting system2 weeks
- Website redesign1 week
- Automated deal pricing1 week
- Ongoing maintenanceweekly
And on a separate engagement with another mid-market operator: $500K+ in annualized labor savings in the first three months.
Let’s see what’s hiding in your data.
A 15-minute conversation is enough to decide whether a small data assessment is worth doing.