Insurance Q&A middle-platform
Terms, claims rules and case library inlined into the backbone; answers align with regulator wording.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- Banks / brokers / insurers
- Project duration
- 10–16 weeks
- Tech stack
- Knowledge / RAG
- Deployment
- Private deployment
2. Business Context & Pain Points
The customer is a Mid-market / Regional player in Finance, with the core pain concentrated in "Knowledge & Q&A". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "Decoupled Knowledge & Low-Cost Inference", Global AI DAO integrated the following capability stack into a single solution:
- Knowledge / RAG —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Private deployment, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| first-answer accuracy | 100% | 124% | +24pp |
| escalation | 100% | 62% | -38% |
Source metric: +24pp first-answer accuracy, -38% escalation.
5. Specs & Fit
| Architecture base | DeepSeek decoupled architecture |
| Backbone size | 7B / 32B / 70B tiers |
| Inference cost | -40% to -60% vs dense baselines |
| Knowledge injection | Continued training / LoRA / expert adapters |