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Enterprise AI for Insurance

Enterprise AI for Insurance

Insurance runs on documents, decisions, and deadlines. Most carriers still process claims manually, underwrite policies with incomplete data, and detect fraud after the payout. We build AI systems that handle the document-heavy, time-sensitive work your teams do every day.

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Up to 38%
faster commercial quote turnaround
Up to 37%
improvement in fraud identification rate
3-6 wks
from kickoff to production pilot

What We See in Enterprise AI for Insurance

1

Commercial property underwriters spend 3 to 5 hours per quote pulling data from ACORD apps, building Excel exposure models, and re-typing everything into Guidewire PolicyCenter, and nearly half that time is just moving numbers between systems before any actual risk analysis happens.

2

Claims adjusters in Guidewire ClaimCenter or Duck Creek Claims spend 50 to 70% of their day on document gathering: pulling FNOL attachments, supplements, photos, and repair estimates, then re-keying the same fields they just read into their claim file before they can make a coverage decision.

3

SIU teams work off rules-based fraud flags that catch less than 20% of organized schemes, and the same rules generate 3 to 5 false positives for every legitimate referral, so investigators spend more time closing junk referrals than actually investigating.

4

Policy servicing for mid-market commercial books (endorsements, cancellations, renewals) requires back-office staff to update PolicyCenter, the rating engine, the document library, and the agency portal separately, creating a permanent backlog that spikes every renewal cycle.

How We Help

Commercial Underwriting Assistant

Our agent reads incoming ACORD submissions, loss runs, and supporting documents, pulls third-party data from Verisk, CLUE, and D&B, builds the standard exposure model, and writes a pre-populated quote into Guidewire PolicyCenter with a risk summary and flagged anomalies. Underwriters work inside PolicyCenter reviewing the model and making the judgment call rather than assembling the file from scratch. Every number traces back to the source document or API pull.

✓ 38% faster quote turnaround and 11 hours/week reclaimed per underwriter

Automated FNOL and Claims Triage

The agent reads the FNOL intake, attachments, photos, and repair estimates in Guidewire ClaimCenter or Duck Creek Claims, extracts structured data into the claim file, scores severity and complexity, and routes to the right adjuster queue with a recommended reserving range. Low-severity auto claims can route to a straight-through-processing path where coverage confirmation and reserve setting happen without adjuster touch.

✓ Cycle time down 44% and adjusters handling 2.4x more claims per day

Fraud Detection and SIU Intelligence

A graph-based model reads claim patterns, claimant history, network relationships across claims, and public records to score referrals with evidence trails rather than single-rule alerts. Legitimate referrals reach SIU with a pre-built evidence packet: linked claims, suspicious providers, timeline anomalies, prior fraud history. Rules-based flags that historically burned investigator time are suppressed or re-scored.

✓ 37% lift in fraud identification rate and 52% fewer false-positive referrals

Policy Document and Endorsement Automation

The agent generates policy jackets, endorsements, renewal notices, and cancellation packages by pulling structured data from PolicyCenter, applying your approved forms library and state-specific language, and writing the final document back to the policy file. Underwriting assistants review exceptions and non-standard language rather than cutting and pasting from templates.

✓ Policy issuance time cut 67% and manual-entry errors down 71%

Loss Run Analysis and Portfolio Intelligence

Agents ingest loss runs across lines of business, normalize them against your exposure base, and generate trend reports, reserve-adequacy signals, and emerging-risk flags that actuarial and underwriting teams previously built by hand each quarter. Chief underwriting officers receive a living dashboard that updates monthly with cited source data rather than a backwards-looking PowerPoint.

✓ Loss-trend reporting cycle from 4 weeks to under 48 hours and 6 to 9 weeks earlier visibility on portfolio drift

Our Services for This Industry

AI Agent DevelopmentView →
Multimodal RAG SystemsView →
Agentic AutomationView →

Engagement shape

Timeline

A typical insurance engagement runs six to ten weeks to first production. Weeks one and two are discovery: interviews with the chief underwriting officer, claims VP, and IT, plus a written integration pattern against Guidewire, Duck Creek, or Majesco. We build the eval set in week two by labeling 1,500 to 3,000 real submissions or claim files with your team setting the ground truth.

Weeks three and four are build. The agent runs against the eval set daily and we share a Friday scorecard with underwriting or claims leadership. Weeks five and six are shadow mode with real users against a paired queue. Weeks seven and eight cover validation sign-off, state filing documentation where required, runbook authoring, and workflow training. Weeks nine and ten are production cutover on a single segment or line, with hypercare for 30 days. Expansion to additional lines or segments follows the same pattern in four to eight week waves.

Cost model

Most insurance engagements fall between $100k and $260k for the first production use case. The main drivers are Guidewire or Duck Creek integration depth, number of lines of business in scope, and whether you need state filing documentation or NAIC Model Bulletin governance artifacts. A single-line claims triage pilot sits near the bottom of the range. A multi-state, multi-line commercial underwriting agent with third-party data feeds lands at the top. Ongoing platform and inference costs typically run $7k to $28k per month in production, quoted upfront before the SOW is signed.

Frequently Asked Questions

How does the AI handle the variety of document formats in claims and submissions?+
We use multimodal models that read PDFs, scanned forms, photos, handwritten notes, spreadsheet attachments, and structured data feeds in the same workflow. The agent extracts required fields regardless of format and flags low-confidence extractions for human review rather than guessing. For ACORD forms we get over 98% field-level accuracy out of the box. For loss runs and carrier-specific layouts we calibrate on 500 to 1,500 of your historical documents before go-live so the model knows your specific broker and carrier sources. Every extracted value carries a citation back to the source page and bounding box.
Can your AI integrate with Guidewire, Duck Creek, or Majesco?+
Yes. We've built against Guidewire PolicyCenter, ClaimCenter, and BillingCenter using both the Cloud API and the older Gosu-based integration framework, Duck Creek Policy and Claims through their REST surface, and Majesco Policy and Claims through their APIs. Integration is a mix of REST, event-driven messaging, and file-based batch depending on what your specific platform version supports. Write-back is gated by your change-management process and we don't bypass existing authority limits or approval queues. We scope the exact integration pattern during discovery and write contract tests before any agent touches production data.
How do you ensure AI decisions meet state insurance regulations and NAIC model bulletins?+
We design agents that assist human decision-makers rather than replace them. Every recommendation ships with the supporting data, policy language, and state-specific criteria that produced it, so adjusters and underwriters can explain and defend decisions to regulators. For carriers in states that have adopted the NAIC AI Model Bulletin we document model governance, testing, and ongoing monitoring in the format your regulator expects. Fair-lending and unfair-discrimination testing is built into the pre-production eval. We hand the full governance package to your chief compliance officer and market-conduct team before go-live.
What does a pilot cost and how long does it take?+
A focused pilot on one use case, for example commercial property underwriting assist or auto claims triage, runs 6 to 10 weeks from kickoff to production. Pricing typically lands between $100k and $240k depending on Guidewire or Duck Creek integration depth, how many lines of business are in scope, and whether you need a separate filing package for departments of insurance. A full claims operation rollout across lines runs 4 to 8 months in parallel waves. We quote a fixed SOW before kickoff so the CUO, COO, and finance all see the same number, and we include production run-rate costs upfront.
What data stays on our infrastructure vs. with the AI vendor?+
Policyholder data, claim files, medical records on injury claims, and underwriting submissions stay inside your tenant. We deploy the application layer in your AWS, Azure, or private cloud and run inference against models hosted either in your own account or in a dedicated private deployment we manage with zero retention and zero training on your prompts. We deliberately avoid sending claim narratives, recorded statements, or policyholder PII through public AI APIs. For de-identified content, if a public API is genuinely better for a narrow task, we document that choice in the architecture review and you sign off before any traffic flows.
Who's accountable when the AI gets a coverage decision or a fraud referral wrong?+
The adjuster, the underwriter, or the SIU investigator remains accountable for the final call. Our agents surface recommendations with cited evidence and route ambiguous cases to a human with the full context attached. For coverage decisions on first-party property or BI claims, nothing binds automatically. For fraud, the SIU investigator validates before any referral goes to law enforcement or is used to deny payment. Adverse action letters are still drafted by your claims legal team. We carry professional and technology E&O coverage sized for insurance deployments, and the MSA spells out liability clearly.
How is this different from what Guidewire Cloud, Deloitte, or an insurtech point solution already pitched?+
Guidewire Cloud ships a product roadmap. Our features ship into your environment in weeks, not releases. The big consulting firms deliver a 12-month staff-augmented program and a deck. Insurtech point solutions are usually focused on one workflow and then ask you to rip and replace. We sit on top of the core platforms you already own, build the agents to your specific lines of business and state footprint, and leave you with production code and model artifacts you control. We also measure success against your loss-ratio, cycle time, and combined-ratio metrics rather than against a SaaS usage number.
What's the hand-off between AI and our people, and how do we measure ROI?+
Every workflow has an explicit confidence threshold. Above it, the agent acts or drafts. Below it, the case routes to a human with the analysis attached so the adjuster or underwriter starts from a summary rather than a blank file. ROI is measured against a baseline captured in discovery: quote turnaround, claims cycle time, indemnity accuracy, loss-ratio variance, SIU referral quality, auto straight-through rate. A dashboard publishes those same metrics weekly from go-live. Most carriers see payback inside 12 months on loaded staff cost alone, with separate combined-ratio impact from better triage and fraud detection.

Let's build your AI system.

Production-grade AI for Enterprise AI for Insurance. We deploy in weeks, not quarters.

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