Voyantt builds analytics and workflow systems for communication- and document-heavy operations. For insurers and claims organizations, our contribution is making information easier to structure, search, review, and route—without asking AI to own the decision.
Claims teams handle conversations with policyholders, adjusters, vendors, and internal specialists. Recording those calls creates an archive. It does not create a working view of recurring issues, complaint severity, unresolved actions, or changes over time.
In our Dialpad analytics work, eight production workflows ingest and enrich calls with summaries, topics, sentiment, named entities, complaints, and actionable issues. Managers can query the resulting data in plain language through a validated natural-language-to-SQL layer.
That published case study is a contact-center analytics project. Its relevance to insurance is the system pattern: high-volume communication, structured enrichment, controlled search, and evidence available to a human operator.
Read: Dialpad call analytics →Policies, endorsements, reports, invoices, estimates, medical attachments, and correspondence arrive in different formats and qualities. We can combine direct parsing, OCR fallback, classification, structured extraction, confidence, and deterministic matching to reduce manual intake.
The product still has to show uncertain fields, let an authorised person correct them, and retain the relationship between the structured case and the source document. That review experience is part of the engineering, not an exception to it.
Our document-intelligence portfolio includes prototypes and cross-industry systems. We would validate an insurance implementation against the client’s actual forms, policy language, claims process, and downstream platform.
Hybrid retrieval can help teams locate related documents, clauses, prior interactions, and case notes across large collections. The useful result includes source context, access controls, and the filters an adjuster or manager actually needs.
Our production search experience reaches 330M+ records at 50–200ms. We present that as search engineering evidence—not as a completed insurance fraud platform.
Where We Can Contribute
Pattern detection and search can support investigation. They are not, by themselves, a production fraud-detection solution. A genuine fraud system would require labeled data, outcome definitions, false-positive analysis, investigator workflow, monitoring, and continuing validation. We would scope that work with the client rather than add “fraud AI” to a capability list.
First Steps
Intake, triage, and settlement each demand a different kind of scrutiny. We find the stage where manual review actually piles up, since that’s where automation or better tooling pays off first — not necessarily the stage that looks the most complex on paper.
A claim rarely arrives as one clean record — it’s a call, a scanned form, an email attachment, spread across formats and channels. We map where the information actually enters before designing how it gets structured and searched.
If the claims platform, the document store, and the call analytics tool each hold a partial view, no one has the full picture. We establish which system is the source of truth for the case record before connecting anything else to it.
An extracted field or a flagged pattern is only useful if the adjuster can quickly confirm it against the source. We design the review interface around what they actually need to check, not just what the model produced.
A denial, a high-value payout, or an escalation usually needs a documented reason and, often, a second sign-off. We build those approval and explanation requirements into the workflow rather than treating them as a compliance afterthought.
Flagging too aggressively wastes adjuster time; missing a real signal costs more later. We size the detection thresholds around the actual cost of each type of error for this specific workflow, not a generic accuracy target.
A model or extraction pipeline can drift as claim types and documents change. We agree on what gets sampled, reviewed, and re-measured after go-live, so quality is tracked on an ongoing basis, not just validated once at launch.
Start a conversation
No pitch deck. We will ask about the users, the current system, the constraints, and what cannot go wrong. If there is a fit, the next step is a written proposal covering the approach, scope, risk, timeline, and cost.