AI + Data Modernization That Fits Mid‑Market Insurance Budgets
Embedded tiger teams for AI, data, and modernization projects
JetBridge provides embedded engineering tiger teams that modernize data, automate workflows, and ship compliant AI fast—on budget and without adding
permanent headcount.
(not “resume spam”)
1) Who we are
Founder-engineers, not consultants. Our team built Five9 (public SaaS) and DoctorBase (scaled to ~9M U.S. users pre-acquisition). We run delivery like operators: clear scope, tight feedback loops, production-grade quality, and measurable outcomes.
2) Why we're different
Live pair-programming is mandatory. Every engineer is screened in real-time on applied problem-solving and code quality. It's expensive and time-consuming — which is exactly why most vendors don't do it.
3) How we work
University-anchored talent funnels. We partner with administrators and professors in Brazil, Poland, Ukraine, and Colombia to recruit top CS and applied-math talent (including PhD candidates), then train them on production AI/data and enterprise modernization patterns.
Layton Wedgeworth
Current: Anthropic (Former: Invitae, Path, Ebay)
4) Social proof
Teams we've built have delivered systems across Fortune 500 ecosystems (e.g., LabCorp) and tier-1 VC-backed startups (including a16z portfolios).
What you buy
- A small, senior team that plugs into your existing stack.
- Production increments every 1-2 weeks (no “big reveal” delivery).
- Security-by-design: audit trails, access control, and runbooks.
- Clean handoff: documentation, dashboards, and ownership transfer.
Engagement model
Start with a defined 6–10 week pilot (fixed scope, clear metrics). If it works, scale to a phased rollout. If it doesn't, stop—without carrying a permanent cost structure.
Next steps
Free 45-minute consult with an AI architect: proposed architecture + pilot scope + staffing plan + budget range.
Note: projected ROI depends on data quality, integration access, adoption, and vendor constraints. We validate assumptions in discovery and lock the pilot scorecard before build.
Case Study Snapshot: Claims + Underwriting Automation with a Governed Data Backbone
Project context
| Client | Regional P&C carrier • ~$650M GWP • ~1.2M active policies • Claims + underwriting across 3 primary systems |
| Starting point | Legacy PAS/claims + document-heavy workflows; siloed data; limited reporting; rising cyber + privacy requirements. |
| Goal | Reduce expense ratio by automating low-severity claims and accelerating underwriting intake while improving auditability and model governance. |
Constraints we designed for
- Lean IT team and narrow change windows; minimal disruption to adjusters and UW ops.
- Core/claims vendor release cycles; API limits and batch constraints.
- Security requirements: least-privilege, immutable logs, incident response readiness, and third‑party risk documentation.
- Messy identities and documents: duplicate parties, mismatched IDs, PDFs/emails, inconsistent attributes.
What we shipped (10-week pilot → 6-month rollout)
Data backbone
- Incremental ingestion from PAS/claims, billing, CRM, call transcripts
- Entity resolution + golden records
- Analytics marts for leakage, severity, and cycle-time
Workflow automation
- Doc intake: classify + extract fields from PDFs/emails
- Triage: severity routing + adjuster-ready summaries
- SIU assist: anomaly features + explainable scoring
Governance + security
- RBAC + immutable audit logs for data/model changes
- Monitoring: drift, approvals, rollback playbooks
- IaC, secrets mgmt, vuln scanning + runbooks
ROI snapshot (measured impact + financial model)
| Financial Line Item | Value |
|---|---|
| Tiger team cost (pilot + rollout) | $897,430 |
| Annualized run-rate savings | $3,182,670 |
| Annualized run-rate revenue lift | $1,028,540 |
| 12-month net benefit | $3,313,780 |
| Payback period | 15.4 weeks |
| 12-month ROI | 368.9% |
Method: savings were anchored to reductions in manual touches and cycle time, leakage capture, and lowered rework. Governance reduced audit prep time and improved repeatability of controls across releases.
Appendix A:
Hiring just one fullstack engineer (senior) requires over 500 candidates sourced, 100 initial interviews, and 14 two hour technical live pair programming to have one candidate pass our test.
Nobody else in our industry does this rigor.
