Your team has a process that takes too long. We automate it.
Daily reports built by hand. Tickets routed by a person. Reconciliation across five systems every Friday. The work is real and the people doing it are good at their jobs; they're just stuck on plumbing. We rebuild that plumbing with AI in the loop and hand it back as software your team owns.
Where this kind of work has shipped
Operations and data work for companies you've heard of, before Monument Labs picked up the same playbook for the mid-market.
Pricing-model and inventory data work across a national auto retailer.
Student-engagement reporting that used to take a team a week, automated end-to-end.
Applied-AI coursework and operational tooling alongside the Robins School.
What kinds of problems we solve
Four concrete shapes we see come up over and over. If your team is doing one of these by hand, there's a real automation underneath.
The 3-hour daily report
Problem
Someone on your team starts every morning pulling data from five different systems to build the same operations report.
Outcome
One automated pipeline, a clean dashboard, and the analyst gets three hours a day back to do real analysis.
Typical engagement: MVP tier ($20K–$25K), 4–6 weeks
Tickets routed by a person
Problem
A support lead spends part of every day reading inbound tickets and assigning them to the right team. Volume keeps going up; the lead does not scale.
Outcome
AI classifier on top of your help-desk that tags, prioritizes, and routes inbound tickets, with confidence scores and a human override on the edge cases.
Typical engagement: MVP tier ($20K–$30K), 4–6 weeks
Weekly inventory reconciliation across 5 systems
Problem
Every Friday, two analysts spend half a day cross-checking your ERP, warehouse system, and vendor portals to catch what disagrees.
Outcome
A single reconciliation dashboard that pulls all five sources, flags the mismatches automatically, and turns the half-day audit into a five-minute review.
Typical engagement: MVP / Studio tier ($25K–$50K), 6–10 weeks
Contracts and PDFs read by hand
Problem
Operations or legal staff manually extract dates, parties, and renewal terms from inbound contracts to keep a spreadsheet up to date.
Outcome
Document-extraction pipeline that pulls structured fields from every PDF, drops them into your system of record, and surfaces the ones that need a human.
Typical engagement: MVP tier ($20K–$30K), 4–6 weeks