CX BPOs: Modernize Fast for the AI Era
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: “AI-Ready CX Platform” for a Mid-Market BPO
Project context
| Client | CX BPO • $102.4M ARR • 2,150 agents • multi-tenant client data + fragmented support stack |
| Starting point | Fragmented analytics across CRM, WFM, QA, and billing, inconsistent transcripts and QA tags, slow client onboarding, rising cloud and support costs. |
| Goal | Centralize data, automate support/ops workflows, and ship governed AI features while maintaining SOC 2-grade controls. |
Constraints we designed for
- Integration overload from multiple CRMs and QA tools.
- Data normalization required for GenAI readiness.
- Client-by-client data isolation + RBAC guardrails.
- PCI-safe call flows to block sensitive data capture.
- AI risk monitoring for voice fraud + model drift.
What we shipped (9-week pilot → 4.2-month rollout)
CX data foundation
- Real-time lakehouse for voice/chat/email
- Unified schema across CRM/WFM/QA/billing
- Cleaned transcripts
AI + workflow automation
- Agent-assist + auto-QA scoring
- Summarization + routing automation
- Billing anomaly + triage workflows
Governance + reliability
- PCI masking + tokenization
- Tenant-level encryption + RBAC
- MLOps observability + drift checks
ROI snapshot (measured impact + financial model)
| Financial Line Item | Value |
|---|---|
| Tiger team cost (pilot + rollout) | $1,476,230 |
| Annualized run-rate savings | $4,913,680 |
| Annualized run-rate revenue lift | $1,842,970 |
| 12-month net benefit | $5,280,420 |
| Payback period | 14.6 weeks |
| 12-month ROI | 357.6% |
Method: hard-dollar savings are anchored to labor minutes, throughput, leakage capture, and vendor spend. Revenue lift reflects conversion, cycle time, and retention improvements attributable to the shipped workflows.
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.
