Illustrative only. Case studies, client identities and metrics may be anonymized, aggregated, generalized or representative of engagements delivered by Pronix or its practitioners; results depend on many engagement-specific factors and are not a guarantee of future outcomes. See our Terms of Use for the full disclaimer.
What these programs changed in production.
Aggregated results across the enterprise AI, contact center and automation programs published on this page.
- 47
- Programs delivered
- 38%
- Average containment lift
- 27%
- Average handle-time reduction
- 4.6x
- Median first-year return
CX, enterprise AI and business automation
Voice and digital self-service
With agent assist and knowledge AI live
Against implementation and run cost
Illustrative only. Metrics may be anonymized, aggregated or generalized; results depend on engagement-specific factors and are not a guarantee of future outcomes.
Newest case studies
The latest enterprise AI and CX programs we've published, newest first.
- Financial Services· Jul 2026
Modernizing banking customer service: migrating a super-regional bank to Dynamics 365 Contact Center
Read case → - Retail & Ecommerce· Jul 2026
Scaling peak-season service with Dynamics 365 Contact Center and Copilot at a specialty retailer
Read case → - Health Payers· Jul 2026
Modernizing member services with Dynamics 365 Contact Center and Copilot at a regional health plan
Read case → - Healthcare Providers· Jul 2026
Rolling out Dynamics 365 Contact Center and Copilot for Service at a multi-hospital health system
Read case → - Healthcare Providers· Jul 2026
Nurse triage and discharge copilot cuts length of stay 0.6 days at a regional health system
Read case → - Healthcare Providers· Jul 2026
Early-out self-pay AI lifts collections 29% at a 900-provider physician group
Read case →
Cutting LLM spend 41% at a large national insurer
A large national insurer had 40+ AI workloads across Azure and Bedrock with no unified cost view, no routing controls and a spend curve that was going to cross eight figures inside 18 months. pronix.ai stood up an LLM FinOps program that cut spend 41% with no measurable quality regression.
1,800-agent contact center cutover in six months at a P&C insurer — zero claims disruption
A P&C insurer needed off legacy Avaya before CAT season and wanted agent assist, screen pop from Guidewire and callback orchestration on day one. pronix.ai cut over the full agent team to Amazon Connect in six months with zero major CAT-season disruption.
34% underwriting cycle-time reduction at a commercial insurer — agentic submissions triage
A large commercial insurer's underwriters spent 60% of their day sorting broker submissions instead of pricing risk. pronix.ai deployed an agentic submissions triage workflow with appetite-fit scoring — cycle time down 34%, underwriter throughput up 2.1x and every decision traceable to a broker-visible reason.
First-notice-of-loss in under 4 minutes at a life insurer — empathetic voice AI
First-notice-of-loss calls at a life insurer averaged 14 minutes and often required beneficiaries to repeat traumatic details across three teams. pronix.ai launched an empathetic voice AI for FNOL intake — 4-minute complete capture, warm hand-off on any distress signal and 100% policy-verified data downstream.
58% broker submission-to-quote lift at a specialty insurer — GenAI broker portal
A specialty insurer was losing broker mindshare because brokers couldn't tell what business the carrier wanted. pronix.ai built a GenAI broker portal with self-serve appetite guidance and instant preliminary indications — 58% lift in submission-to-quote conversion in two quarters.
Enterprise knowledge AI grounded on 2.4M policy documents at a national insurer
Underwriters and claims staff were working from conflicting versions of the same procedure. pronix.ai built a governed enterprise knowledge layer over 2.4M documents with ownership, freshness and citation enforced — 94% answer accuracy on the evaluation set and a citation on every single response.
Want a written case for your program?
We work with clients to publish anonymized case studies when the numbers deserve it — usually at the 12-month mark post-cutover.