The initial deployment identified a multi-million-dollar revenue recovery opportunity across about 5% of North American locations.
Seekr Benefit Plan Audit Agents
Manual extraction and reconciliation fall from weeks to days, and days to hours.
Agents that ingest, validate, and structure the documents behind every 401(k) plan audit, with each finding traceable to its source.
What the agents do
Ingests complex financial documents in whatever format they arrive, validates the extracted figures, and structures them for audit procedures.
- Contributed by
- Seekr (Explainable AI platform for decision-critical work)
- Industry
- Professional Services
- Audience
- B2B
01
The problem
Why this exists
Under ERISA, most 401(k) plans with 100 or more participants need an annual audit by an independent qualified public accountant, filed with Form 5500. Before substantive audit work can begin, someone has to pull the figures out of dense financial documents, then check and reconcile them.
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At this firm, a fast-growing, private-equity-backed accounting and advisory practice serving middle-market clients, that step was slow, manual work. The people best qualified to judge whether a plan was operating as written spent their time finding and copying facts. The work is seasonal and deadline-driven, and growth made it worse: every new engagement brought the same hours with it.
02
Outcomes
What changes when this is in place
- Manual extraction and reconciliation fall from weeks to days, and days to hours.
- Annual audits run faster while still being held to the firm's accuracy standards.
- Audit coverage and governance broaden across the plans in scope.
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- Capacity returned to the audit team for judgment, client service, and advisory work, which shortens the revenue cycle.
- Every finding traces back to its source document, so auditors can review it the way they review staff work.
03
How it works
Architecture
The firm runs agents built on SeekrFlow, which supplies an ingestion layer that turns unstructured files into structured input, an agent builder that sets the model, instructions, and tools per task, and explainability that traces every finding back to its source.
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For this engagement the agents take plan documentation in whatever format it arrives, validate the extracted figures, and structure them for the firm's audit procedures. The agents do the document work; auditors keep the judgment. Open-weight models leave the firm free of a single inference vendor.
Explainability is the reason the firm could proceed at all. It set one condition at the outset: AI had to meet its professional and regulatory standards. Every finding carries provenance back to the document it came from, so a reviewer can see how a result was produced and review it to the same standard as anything a staff auditor prepares.
Deployment follows the data: the platform runs wherever the firm's regulated plan documentation has to stay, air-gapped environments included. The engagement began with 401(k) auditing and is expected to extend to the firm's core advisory and audit workflows.
Tech stack
SeekrFlow AI platformAI-ready ingestion of unstructured files, images, and recordsAgent builder for model, instructions, and toolsOpen-weight and fine-tuned model supportBuilt-in explainability and provenance on each outputSearch across the organization's own files and custom connections to internal systemsDeployment in cloud, on-premises, at the edge, or air-gapped
Next step
Request an intro
Seekr is behind this solution. Ask about putting benefit plan audit document work behind agents, 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 work
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 names no customer, so the firm's identity, size, and plan volume cannot be verified independently.
- The time saving is stated as a range, from weeks to days and days to hours; the source publishes no baseline, sample size, or per-audit figure.
- The source does not say how many agents the firm runs or how the work is divided among them.
- No error rate, accuracy metric, or reviewer rejection rate is reported for the structured output.
- The source gives no product URL, pricing, or contract value for the deployment.
Where this came from
Solution detail is drawn from Seekr's submission to the hub. The source does not name the firm, which it describes only as a fast-growing, private-equity-backed accounting and advisory practice serving middle-market clients.
Status: Live in productionAdded Oct 28, 2026A listing is an editorial decision, not a MACH Alliance certification.
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