(Our Playbook, Open-Sourced)

AI pilots are easy. Production deployments are hard.

Over the last two years, we’ve worked side-by-side with carriers, MGAs, and brokers to put AI into real underwriting, broker and claims workflows — not demos, not proofs-of-concept, but production systems that move the business.

We’ve learned what works, what doesn’t, and how to go live fast without breaking trust.

Here’s our playbook — open-sourced — so more insurance teams can adopt AI with confidence.

The Big Idea

Don’t sell “AI.” Ship workflow outcomes.

Start with one high-value process, wire it into your systems, keep humans in the loop, and measure ROI from day one. Then scale.

At Upland Capital Group, that meant: ingest every submission → extract what underwriting needs → enrich → clear — all with full auditability so underwriters can trust the output.

That first workflow proved the value. Then we expanded.

Most insurers are past the “AI exploration” phase. The question now isn’t if AI fits — it’s how to make it work in production. This playbook is our answer.

Architecture That Scales

Model Router, not Model Religion

We don’t train new foundation models. We route tasks to the best available ones — and switch as they evolve. That keeps us nimble and future-proof.

Insurance-Tuned Orchestration Layer

Our real IP: classify → extract → enrich → apply rules → triage → HIL → push to systems.

Configurable. Transparent. Built specifically for insurance data.

Humans Stay in the Loop

AI handles the repetitive work; humans keep their judgment. The result is speed and trust.

Forward-Deployed Engineer (FDE) Model

Instead of long integration cycles, we embed engineers who configure and deploy live workflows in weeks — not quarters.

What Makes This Approach Different

• From pilot to production in weeks
• Commercial-grade accuracy on complex docs — loss runs, SOVs, ACORDs, D&O financials
• Blend of deterministic + AI logic in one flow
• Full transparency — citations, confidence scores, audit logs
  
  

One underwriter told us:

“The first week, I didn’t trust it. By week three, I couldn’t imagine working without it.”

That shift — from skepticism to habit — is the real marker of success.

Change Management That Works

• Pick the right first users. Include believers, neutrals, and skeptics.
• Start small. Go live where you don’t need major integrations.
  Share wins weekly. Momentum builds belief faster than training decks.

Pricing Framework: Keep Incentives Aligned

Traditional software models penalize usage — charging per seat or per premium volume. That doesn’t fit AI.

Our philosophy: pay based on consumption, not on users or book size.

You should want as many people as possible using the platform, discovering new efficiencies every week.

When usage grows, everyone wins — your ROI scales, and our incentives stay aligned.

Build vs. Buy

Buy what’s proven. Build where your edge lies.

Let hyperscalers handle the model training. You own your rules, data, and customer experience.

Design everything to be reversible — so you can learn fast without locking in.

Where FurtherAI is Headed

• Adaptive sub-flows that pause for human review, then resume automatically
• Step-level model transparency — see which engine powered each task
• No-code builder evolution so analysts can design workflows in plain English
  

Closing Thought

AI shouldn’t replace judgment — it should accelerate understanding.

Insurance runs on information. The faster teams can process it, the better they can price, serve, and protect.

We’re building the infrastructure that makes that speed and accuracy possible.

If you’d like a working checklist of this 45-day playbook, reply “Send the kit.”

We’ll share the exact templates and frameworks we use to bring AI to production — one workflow at a time.

Because the next leap in insurance won’t come from more software — it’ll come from understanding, accelerated.