Field associates find and correct shelf problems on the first visit, and every visit leaves an auditable record for the brands served.
Seekr Bill of Lading Invoicing Agents
Every completed load invoices itself from its own paperwork, at 10,000 to 15,000 bills of lading a month in production.
Vision AI reads every bill of lading, the extracted data generates the invoice, and the source document stays attached to every transaction.
What the agents do
Reads each bill of lading, extracts the billing fields, generates the invoice, and reasons over a knowledge layer that keeps the source document attached.
- Contributed by
- Seekr (Explainable AI platform for decision-critical environments)
- Industry
- Energy & Utilities
- Audience
- B2B
01
The problem
Why this exists
Every physical movement in liquid fuels generates a bill of lading recording what shipped, from where, in what quantity, and under whose custody. A load cannot be invoiced until someone captures that information. This company moves ethanol, crude oil, diesel, and low carbon fuels by truck, barge, vessel, and rail, and trades regulatory compliance credits such as RINs and LCFS credits. Bills of lading arrive from dozens of terminals and carriers as scans, photos, and faxes, in formats that vary by origin and mode, and people read them by hand. At 10,000 to 15,000 movements a month, that manual capture became the bottleneck between finishing a load and getting paid.
Read more
Speed alone was not enough. Because the company handles regulated fuels and credits, finance could not accept a number it could not trace to its source. And after two and a half years evaluating the market, it did not want to lock itself into one supplier's technology.
02
Outcomes
What changes when this is in place
- Runs the revenue cycle in production on 10,000 to 15,000 bills of lading a month across truck, barge, vessel, and rail.
- Invoices generate automatically from the extracted document data, so a completed load no longer waits on manual paperwork.
- Movements reconciled against contracts, with each discrepancy traced back to the document that caused it.
Read more
- Counterparty questions answered from the underlying documents instead of manual searches.
- The customer estimates the change saves $1.5M to $2.5M a year in costs it no longer incurs.
- In place in a matter of weeks from early conversations, against the six-to-nine-month roadmaps the other options proposed.
03
How it works
Architecture
SeekrFlow supplies the working parts: document ingestion, an agent builder, explainability that traces every output to its source, and deployment wherever the data has to stay.
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For this customer the platform does three jobs. Vision language models ingest each bill of lading, whatever its format, scan, photo, or fax, and extract the fields billing needs. That data generates invoices automatically, so a load no longer waits on paperwork. Every document also enters a searchable, natural language knowledge layer with its source context attached, and AI agents reason over it to reconcile movements against contracts, trace a discrepancy to the document that caused it, and answer counterparty questions from the paperwork. The company can adopt better models as they mature without rebuilding the workflow, and SeekrGuard can evaluate a candidate replacement against its own bills of lading before a swap, leaving an auditable record of what was tested and approved. SeekrGuard is an available extension, not part of this deployment.
Tech stack
SeekrFlow, the AI operating system for decision-critical environmentsAI-ready Data Engine turning unstructured documents, images, and records into usable inputVision language models reading scanned, photographed, and faxed paperworkSearchable natural language knowledge layer with source context attachedAgent builder configuring the model, the instructions, and the tools each workflow needsBuilt-in explainability tying every extracted value to its sourceModel-agnostic layer supporting open-weight and fine-tuned modelsSeekrGuard, an available extension that evaluates replacement models before a swap
Next step
Request an intro
Seekr is behind this solution. Ask about invoicing bills of lading at volume, 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 for decision-critical environments
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 saving range is the customer's own estimate, and the submission reports no measured baseline behind it.
- The customer is anonymous, so no figure on this page is independently attributable.
- The submission reports no accuracy or exception rate for the extracted billing fields.
- SeekrGuard is an available extension, and the submission does not place it in this deployment.
Where this came from
Solution detail is drawn from Seekr's submission to the hub. The customer is anonymous, and the savings range is its own estimate of annual costs it no longer incurs.
Status: Live in productionAdded Oct 28, 2026A listing is an editorial decision, not a MACH Alliance certification.
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