A merchant payment journey is assessed end to end with evidence, and without a questionnaire.
Seekr Contract Intelligence Agents
The initial deployment identified a multi-million-dollar revenue recovery opportunity across about 5% of North American locations.
Structured extraction across more than 7,000 customer contracts, joined to operational data, with every flagged discrepancy traceable to its clause.
What the agent does
Extracts pricing, service frequency, and escalation terms from each contract, joins them to operational records, and flags gaps with clause-level citations
- Contributed by
- Seekr (Explainable AI platform and the data engine behind the contract extraction)
- Industry
- Financial Services
- Audience
- B2B
01
The problem
Why this exists
The customer provides secure logistics and cash management services in more than 100 countries and handles billions of dollars in customer assets every day. Its operational data was modern: service records for every stop, pickup, and change order flowed cleanly into a cloud data warehouse.
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Its contracts were not. More than 7,000 of them, many dating to the 1990s, sat as PDFs in a document archive. They governed service frequency, pricing escalation, change orders, and invoicing, but none of it was structured or queryable. If a contract specified five stops a month and the team made seven, or pricing escalated under a clause the billing system missed, the company under-collected.
Industry research from Sirion puts typical revenue leakage at 2% to 9% of annual revenue. Leadership suspected the company was in that range and needed to know where, by how much, and with evidence finance could act on.
02
Outcomes
What changes when this is in place
- An initial deployment identified a multi-million-dollar revenue recovery opportunity across about 5% of North American locations.
- A queryable dataset now covers the full contract portfolio and joins to operational records in the warehouse.
- The pipeline keeps processing new contracts and amendments as they are executed, so the comparison stays current.
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- Every flagged discrepancy carries its contract, page, and clause, so finance reviews evidence rather than a claim.
- The deployment expanded across five business areas under a multi-year enterprise license.
03
How it works
Architecture
SeekrFlow supplies the working parts: an extraction pipeline with explainability built in, so every result traces back to the data behind it.
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Before any extraction ran, Seekr worked with the customer's data and commercial teams to define the schema: which contract fields mattered, how they mapped to the operational data model, and which edge cases the pipeline had to handle. That schema governs every extraction decision.
The pipeline ingested the full contract corpus and extracted pricing terms, service frequency, location identifiers, change order history, escalation clauses, and renewal dates. Each value carries a pointer to its source contract, page, and clause. The structured data lands in the warehouse beside service records, where gaps between what contracts say and what operations show are flagged for finance review, and a reviewer can click through to the underlying language.
The pipeline keeps running, processing new contracts and amendments as they are executed, so the comparison stays current. The platform runs wherever the contract archive has to stay, air-gapped included, so the archive never leaves the boundary the business requires.
Tech stack
SeekrFlow AI operating systemAI-ready Data EngineSchema governance agreed with the customer's data and commercial teamsClause-level provenance on every extracted valueOpen-weight modelsCloud, on-premises, edge, and air-gapped deployment
Next step
Request an intro
Seekr is behind this solution. Ask about finding revenue leakage hidden in your contract portfolio, and the MACH Alliance will put you in touch with the person who can answer.
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Everyone credited on this solution
- SeekrExplainable AI platform and the data engine behind the contract extraction
Every organization named here has a page listing the rest of its work on the Hub.
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Verification
Contributor submission
The detail on this page comes from the contributor's own account of their own work. The MACH Alliance reviews a submission before publishing it and does not independently verify the figures in it.
What this page does not establish
- The source gives the size of the opportunity, not revenue actually recovered; no invoiced or collected amount is reported.
- The customer is named only as a Fortune 500 provider of secure logistics and cash management services, so the deployment cannot be verified independently.
- The source reports no extraction accuracy, error rate, or share of flagged discrepancies that finance confirmed.
- No product URL, pricing, contract value, or license term is given for the multi-year enterprise agreement.
- The source does not say how the roughly 5% of North American locations in the initial deployment were selected, or what the expansion covers.
Where this came from
Drawn from Seekr's submission to the hub, titled Secure Logistics: a Fortune 500 secure logistics leader turns decades of contract data into auditable intelligence, dated Oct 27, 2026. The document names no customer, describing it only as a Fortune 500 provider of secure logistics and cash management services.
Status: Live in productionAdded Oct 29, 2026A listing is an editorial decision, not a MACH Alliance certification.
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