A contract is a stack of promises: rebates, penalties, price commitments, service credits. Across a very large contract base nobody can watch them all, so missed obligations leak value quietly, year after year. The demand we keep hearing: track the promises against what actually happened.
Take a very large contract base - and the rebates, penalties, price commitments and service credits negotiated into it - and track those obligations against execution: the orders, invoices and deliveries where performance either shows up or does not. Every obligation nobody is watching is a place where value leaks, silently, on both sides of the deal.
The market has noticed. Point tools aimed at exactly this are already being trialed - and when vendors start circling a problem that precisely, you can take the pain as real.
Heard from a senior procurement-solutions leader at a global consumer-goods manufacturer. Paraphrased, like everything on this map.
An obligation clause is written for lawyers, not for systems. The rebate hides in a paragraph: conditions, thresholds, dates and exclusions, phrased a little differently in every contract. Before anything can be tracked at all, every one of those clauses has to be extracted and structured - and that step is where most attempts quietly end.
Directional only - we do not attach figures to demand signals. These pools are expensive enough without decoration.
Rebates, penalties, price commitments and service credits get negotiated, earned and never enforced. The contract already won the money; collection is the step that quietly fails.
Obligations are matched against the orders, invoices and deliveries that prove them, so an earned rebate stops depending on somebody remembering the clause exists.
Obligations run both ways, and the commitments you owe surface when the counterparty finds them - after they have compounded into a claim.
Your own checks find them first. Exposure surfaces while it is still a correction, not yet a dispute.
Contract managers sample a handful of high-value agreements and hope about the rest. Across a very large contract base, most promises go unwatched by design.
Watching every obligation stops being a staffing question - and the hoping retires along with the sampling.
Two engines run in production today - Analytical Lab Reports and account Knowledge Twins. Everything else on this map is an extension on the same foundation.
This entry extends the first of the two. The Intelligent Document Agent mechanism behind Analytical Lab Reports exists to pull reliable structure out of documents that mix prose and tables - the same parsing moat, pointed at obligation clauses instead of test results. Extraction is the reasoning half. The matching half is deliberately boring: structured obligations checked against orders, invoices and deliveries as deterministic rules with an audit trail, not as a model's opinion.
See the parsing mechanism in productionAll of it runs inside your infrastructure, within your boundary. Commercial terms are among the most sensitive text a company holds, and keeping them under your control keeps your freedom of action too: which models read them, where the workloads run, what the economics look like.
Back to the AI ROI MapThe same structure-from-documents mechanism runs in production on lab reports. Take the sample dataset, run the queries in the AI tools you already use, and try to prove us wrong. No form, no gate.
On-prem. Your data never leaves your boundary.