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.
Insurance AI in Cape Town
Document pipelines and claims intake for Western Cape regional offices and underwriting teams — useful when your operations team is clustered in the Cape Town metro and wants review UIs close to day-to-day users.
Insurance AI in Johannesburg
Large-volume FNOL, policy-pack extraction and integration work for Gauteng head offices and national programmes — scale, stakeholder sign-off and audit trails structured for insurer-grade governance.
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