Underwriting and claims still run on PDFs, email attachments and scanned forms. Insurance AI from Zenovah means one thing: turn that mess into structured data your systems can use — extraction, triage, summaries and handoff — with audit trails and confidence scores, not a generic chatbot on your website.
The same pipeline patterns we ship for bank statement → Excel apply here: ingest the file, extract fields, route low-confidence extractions to a human queue. Insurance layers wording sensitivity, POPIA-aware handling and your approval rules — we design those in discovery, not as bolt-ons.
What we build (the offer)
- Document extraction — PDFs, scans and attachments → tables, JSON or fields pushed to your CRM / claims system / data store
- Claims automation — intake, FNOL structuring, attachment indexing, triage queues for adjusters
- Underwriting automation — prep packs for underwriters: extracted facts, gaps flagged, draft briefs grounded in submissions
- Custom integration — APIs, webhooks, human-in-the-loop review UI; not an off-the-shelf insurance SaaS SKU
Below: how that shows up in underwriting and claims, with screenshots. Then what Zenovah delivers end-to-end and a direct CTA.
Underwriting & risk documents
Underwriting AI here is not a black-box risk score sold as magic. It is automation around the paperwork: pull fields from applications and attachments, cross-check for gaps, and draft a short brief for a human underwriter. Outputs: structured tables, exception lists, and summaries tied to the source documents — so your team can defend decisions.
- Extract from unstructured PDFs (financials, medical reports, schedules)
- Flag inconsistencies or missing items before pricing
- Triage by complexity — specialist underwriters get clean prep, not raw PDFs only
Example: underwriting document extraction
Claims intake & triage
Claims AI here means less re-keying and fewer handoffs without context: natural-language first notice of loss where you want it, structured extraction from estimates and reports, and routing by complexity. Fraud and SIU stay on your existing tools — we wire in your signals, not a black-box vendor score.
- Intake: Chat or form → structured claim record + attachments indexed
- Documents: Summaries and field extraction from repair invoices, medical bills, police reports
- Triage: Straight-through where you define rules; queue the rest with context for adjusters
Example: claims or service interface
What Zenovah builds
We do not sell a generic “AI for insurers” product. We scope a custom pipeline: your document types, your approval rules, your downstream systems (core admin, CRM, data warehouse, or Excel handoff). Same engineering discipline as our bank statement automation and invoice extraction — evaluation, logging, versioned prompts, and explicit human review where the business requires it.
Typical extensions on the same stack: policy Q&A grounded in your approved wording; compliance checks on draft policy language before filing; long-form clinical or loss reports summarised for underwriter or adjuster review. All scoped to South African and cross-border data handling requirements in the brief (e.g. POPIA-sensitive flows).
Delivery is AI development — not slides: APIs, review screens, monitoring, and handoff UX your operations team can run. Need a roadmap first? AI agency covers discovery; this page is for when you are ready to ship something concrete.
AI augments underwriters and claims staff; it does not replace sign-off on regulated outcomes. We design that split from day one.
Scope your insurance automation
Send what you process today (claims, underwriting packs, policies) and where extracted data must land. We reply with a feasible scope — not a generic demo. Contact Zenovah.
Get in touch