AI-OCR loyalty platform validates receipts for a supermarket chain
- ~99%
- AI validation accuracy, up from ~80%
Insurance Process Automation Software
Claims adjusters at most insurers spend more time chasing documents, re-keying data, and formatting compliance reports than they do reviewing actual claims. That's not a staffing problem, it's a process problem. We build automation that handles FNOL intake, policy data extraction, renewal workflows, and commission tracking without adding headcount.
Claims cycle time cut by automating FNOL intake, triage, and document collection
Policy renewals, cross-sell triggers, and lapse notices sent automatically at the right time
Underwriting data collection and compliance reports generated without manual intervention
Agent commission calculations run error-free at close of each period
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
Is your claims team manually entering the same data into three different systems?
Are renewals falling through the cracks because nobody has time to follow up consistently?
Short answer
RaftLabs builds insurance automation for carriers and brokers across the US, UK, Europe, Canada, and the UAE: FNOL intake, claims document OCR, renewal workflows, and commission tracking. A focused first automation launches as a validated v1 in 4 to 6 weeks; end-to-end claims automation runs 10 to 14 weeks. Fixed price, scoped before any code.
Key takeaways
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Proof
Most insurers know their claims team is doing work that shouldn't require humans. The problem isn't awareness, it's that the automation conversation usually stalls at "our systems are too old" or "it'll take two years to implement." Neither is true. The processes that drain the most time are exactly the ones that automate cleanest: structured intake, rule-based routing, document extraction, scheduled communications, and period-end calculations.
The operations teams we work with aren't failing. They're doing high-volume, high-stakes work with tools that weren't built to handle it at scale. We fix the tool problem.
McKinsey's Insurance 2030 analysis projects that a large share of underwriting and pricing tasks for personal and small-commercial lines will be automated by 2030 (McKinsey, Insurance 2030). In our own work the blockers are rarely the technology. They are integration complexity, dirty source data, and the internal conviction to start on one workflow instead of waiting for a full core replacement.
The table below maps each stage of a claim to where the manual work costs you and what an automated workflow changes. Use it to find your own biggest leak before you scope a build.
| Claims stage | What goes wrong manually | What automation changes | Where the saving comes from |
|---|---|---|---|
| FNOL intake | Loss details captured by phone or email, re-keyed into the claims system, often with gaps | Structured intake across web, email, and phone transcripts, validated against the policy before it lands | Fewer re-work loops and cleaner files at the first touch |
| Document handling | Adjusters read and transcribe PDFs, scans, and forms by hand | Layout-aware OCR extracts fields and cross-checks names, dates, and amounts against policy records | Lower cost per document and fewer transcription errors |
| Coverage check and triage | Simple claims wait in the same queue as complex ones | Rule-based routing by loss type, territory, and complexity, with a pre-populated file | Shorter cycle time and adjusters focused on judgment work |
| Compliance reporting | Data pulled and reconciled by hand to state, NAIC, or Lloyd's formats near deadline | Extraction, reconciliation, and format transformation with every step logged for the audit trail | Fewer late or restated filings and a defensible examiner record |
Capabilities
First notice of loss is where claims get delayed or misrouted before a human adjuster ever touches the file. We automate intake across web forms, email, and phone transcripts, then validate coverage against the policy database. Each claim routes to the right adjuster queue by loss type, territory, and complexity, so adjusters open a pre-populated file, not an empty record.
Insurance documents arrive in every format: web submissions, scanned PDFs, Word reports, spreadsheets, email attachments. Our pipelines run layout-aware OCR, extract fields, and cross-validate against policy records so mismatched names, dates, or amounts get flagged, then write clean structured data to your claims or policy admin system.
Policy lapse from a missed renewal is preventable revenue loss. We automate renewal sequences that trigger at configurable intervals before expiry, each pulling the policyholder's actual premium, quote, and coverage rather than a generic reminder, with lapse-prevention and win-back sequences that fire automatically on failed payments or unactioned renewals.
Underwriters routinely spend a large share of their time gathering data that already exists in connected systems. We build aggregation pipelines that run on every submission, credit lookups, property data, prior claims history, inspection reports, and financials, normalized into a complete underwriting file with missing data flagged rather than blocking the workflow.
Insurance regulatory reporting means pulling data from claims, policy admin, and finance systems, reconciling it, and formatting it to state or Lloyd's specifications. We automate the extraction, reconciliation, and format transformation so the compliance team receives a pre-built report with exceptions flagged and every step logged for the audit trail examiners request. For Lloyd's managing agents, bordereaux reporting generates in the required format on the submission schedule. For EU and UK insurers, Solvency II quantitative reporting templates (QRTs) map from your reserving and financial systems. For US carriers, NAIC XBRL data tagging runs against the NAIC taxonomy before submission.
Commission calculations that live in spreadsheets create errors, disputes, and month-end delays. We build systems that pull policy transactions at period close, apply your commission rule set, and generate agent statements automatically, routing exceptions to a review queue instead of silently producing a wrong number.
We lead with the smallest automation that proves value, then expand one workflow at a time. You never buy a full platform on day one.
How we work
Every automation project follows the same four phases. Scope is locked and price is fixed before development starts.
We map your highest-cost manual processes, measure the volume and frequency, and identify the automation candidates with the clearest ROI. You leave week 1 with a written scope document and a fixed-price quote. No development starts without your sign-off.
We design the integration approach, data flow, and business rule logic before writing a line of code. The spec is locked before the build starts. Design decisions made here cost ten times less than the same decisions made in week 8.
Working automation at a staging environment by the end of sprint one. Bi-weekly demos. QA runs in parallel with every sprint, not as a phase at the end. Integration with your policy admin system, claims platform, or CRM is scoped and tested throughout.
Production deployment with monitoring activated on launch day. 8 weeks of post-launch support included in every project. Exception queues and alerting are configured before handoff so your team can run the system without us.
Why us
The engineers who assess your automation problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
We have shipped production software since 2015 across AI, SaaS, mobile, automation, and enterprise platforms in healthcare, fintech, logistics, and hospitality. Client relationships include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin.
Insurance runs on regulated data, so we scope the compliance regime in week 1, not before launch. That means PII handling under GDPR and US state privacy law, plus an immutable audit trail on every automated decision. Claims-handling logic respects fair-settlement rules: the NAIC Unfair Claims Settlement Practices model in the US, FCA conduct rules in the UK. We have shipped HIPAA-compliant systems for US healthcare clients and GDPR-compliant products for European markets. Insurance-specific reporting such as state DOI filings, NAIC XBRL tagging, and Lloyd's bordereaux is handled in the build, not bolted on after.
We model the ROI case using your actual claim volumes, labor costs, and error rates before you commit. The investment decision is grounded in your numbers, not an industry benchmark.
Most automation rollouts in insurance fail on the same few things. Here is where they go wrong and how we design against each one before the build starts.
We scope automation projects at a fixed cost. Tell us where the manual work is heaviest and we'll show you what's automatable.
We have not shipped a public, named insurance claims platform we can point to yet. The honest proof is adjacent document-automation work: the same disciplines a claims pipeline needs, layout-aware OCR, field extraction, cross-validation against a system of record, and exception handling that holds up under real transaction volume. On the gas-station build we ran a Rust sync utility against a live Gilbarco Passport POS and processed 20,000-plus transactions in a single day during real-world testing. On the receipt-validation platform we pushed AI validation accuracy from roughly 80% to near 99% on Google Vertex AI. Those are the mechanics of claims document automation, applied to a neighbouring problem.
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Read moreMore than most operations leaders expect. The clearest wins are in document-heavy, rule-driven processes: FNOL intake (capturing loss details, routing to the right adjuster, triggering document requests automatically), OCR-based extraction from policy documents and claims forms, renewal reminders sent at configurable intervals before expiry, lapse-prevention sequences, cross-sell triggers based on policy anniversary or life event data, underwriting data aggregation from multiple sources, and compliance report generation pulled from your live policy data. Agent commission tracking and period-end reconciliation is another area where manual calculation creates expensive errors. None of these require replacing your core policy admin system, automation wraps around what you already use and handles the repetitive work that's eating your team's time.
Yes, that's the standard model. We don't replace your policy admin system, claims platform, or CRM. We connect to them. Most insurance operations run on a mix of older core systems, spreadsheets, and newer point tools that don't talk to each other. The automation layer sits between them: pulling data from system A, applying your business rules, writing results to system B, and triggering the next action. We've built integrations with platforms like Guidewire, Duck Creek, Salesforce Financial Services Cloud, and several proprietary insurer systems. If your system has an API or an accessible database, we can connect to it. If it doesn't, we work with the data exports it produces. The integration approach is scoped in the first two weeks and agreed before development starts.
A focused automation, for example FNOL intake routing or a renewal reminder sequence, launches as a validated v1 in 4 to 6 weeks from scoping to live, so you can put it in front of real adjusters and iterate. End-to-end claims processing automation, including document OCR, adjuster assignment, reserve calculation triggers, and compliance reporting, is closer to 10 to 14 weeks. The range depends on how many systems need to be connected, how complex your business rules are, and how much data cleaning is required before the automation can run reliably. We scope the work at a fixed cost before a line of code is written, so there are no mid-project cost surprises. Timelines are confirmed at proposal stage.
A focused first automation, such as FNOL intake routing or a renewal reminder sequence, starts around $25,000 to $45,000, delivered as a validated v1. A full multi-workflow platform, adding document OCR, underwriting data aggregation, compliance reporting, and commission reconciliation across your core systems, grows to roughly $90,000 to $180,000 over time. Most insurers start with one high-cost workflow, prove the saving, then expand. Every engagement is fixed price, scoped in writing before any development starts. We model the ROI against your own claim volumes and labor costs before you commit.
The pattern is consistent across insurers. A claims team handling 500 claims per month, where each claim requires 45 minutes of manual data entry, spends roughly 375 labor hours per month on work automation handles in seconds. At a fully-loaded cost of $50 per hour, that's $225,000 per year. Automation typically costs a fraction of that in the first year, and the saving repeats every year after. Beyond labor cost: faster claims cycle time improves customer satisfaction; renewal automation reduces lapse rates; commission accuracy reduces disputes. We model the ROI case before the project starts so the investment decision is grounded in your actual numbers.
Yes. We sign NDAs before any scoping call where you share proprietary process details, policy data structures, or system architecture. Confidentiality agreements are standard for all insurance work, where data sensitivity and regulatory exposure make NDAs a baseline requirement rather than a negotiation point.
The stack depends on what your existing systems can connect to. For document OCR and extraction we use Azure Document Intelligence or LayoutLM depending on document variability. For workflow orchestration we use n8n, Temporal, or custom Python services. Integration layers connect to Guidewire, Duck Creek, Applied Epic, Salesforce Financial Services Cloud, and proprietary insurer platforms via API or database. For scheduled communications we use SendGrid, Twilio, or your existing email infrastructure. We do not impose a fixed stack, we select tools based on what connects cleanly to your systems and what your team can maintain after handoff.
Work with us
We scope Insurance Process Automation in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.