You raised the round. You have the appetite, the capacity partner, and a pricing thesis your competitors don’t. Now the clock starts. Every week before your first bound policy is burned runway and lost credibility. Here’s the uncomfortable truth about what slows new digital MGAs down — and how to skip it.
The blocker is never the model
AI-native founders assume the hard part is the underwriting intelligence. It isn’t. Foundation models are commoditized, and your pricing logic is your own. What quietly eats your first months is the least glamorous piece: reliably getting clean, structured, enriched data out of the submissions brokers actually send.
Nobody funds an MGA to build a spreadsheet parser. But that’s what eats the first quarter if you build the data layer yourself.
Property submissions arrive as messy SOVs, ACORD forms, loss runs, and free-text emails, in every format imaginable. Before your agents can triage, price, or quote, that mess has to become trustworthy structured data. Build it in-house and you’ve committed engineers to an insurance-specific data problem for months. Rent it and you’re ingesting live submissions this week.
Day one: extraction-as-a-service
The first capability you need, on day one, is extraction. Point an API at any incoming submission and get back a normalized schema — every building with location, construction, occupancy, year built, square footage, stories, protection, and values — with per-field confidence scoring, so exceptions route to a human and everything else flows straight through.
- Ingest Excel, CSV, PDF, and scanned documents without a custom parser per broker.
- Normalize wildly different templates into one schema your models can consume.
- Flag low-confidence fields instead of silently mis-pricing them.
Day two: data enrichment
Extracted data tells you what the broker sent. Enrichment tells you what they didn’t. An enrichment layer takes thin submission data and builds a 360-degree view of the risk — automatically.
- Geocode every location and append CAT zone, flood zone, and fire protection class.
- Validate stated values against replacement-cost data to catch under- and over-valuation.
- Append building characteristics and neighboring-risk context for smarter appetite and pricing.
Extraction plus enrichment is the real foundation of “straight-through” and “agentic” underwriting. With both in place, your agents reason over complete, trustworthy inputs from the first submission — which, I think, is the only way autonomous decisions stay safe at volume.
The data layer stops being a build and becomes a switch you flip. Your team spends week one on what actually sets you apart — risk selection, pricing, distribution — not on OCR and geocoding.
Why Ping
Ping is insurance data infrastructure. We solve the problems that come from unstructured data — we clean it, structure it, and enrich it in seconds, delivered modularly by API. For an AI-native MGA, that means extraction-as-a-service on day one and enrichment on day two: the two capabilities standing between your round and your first bound policy.