PRD: Smart Submission & Renewal Assistant for Commercial P&C Brokers

(Brainforge × Contextual Insurance POC MVP)

Author: Gabe Lam Who This Affects:

Independent commercial P&C brokers (esp. E&S-heavy, complex risk)
Brokerage CSRs / account managers
Wholesale brokers / MGAs / carriers underwriting teams
Sales & partnerships teams at Brainforge and Contextual

1. Function

Smart Submission & Renewal Assistant – A workflow assistant for commercial P&C brokers that:

Standardizes information collection via a single smart intake questionnaire per vertical.
Auto-fills carrier & wholesale supplemental applications using intake data, documents, and meeting transcripts.
Reduces back-and-forth with clients by surfacing remaining questions clearly.
Serves as the foundation for an “AI account executive” that understands policies, renewals, and service tasks.

2. Problem Statement

Commercial P&C brokers—especially those focused on complex E&S business—lose days each month to manual, repetitive tasks required to get from “prospect” to “bindable quote.”

Brokers like Ian:

Spend days to a week gathering information for each submission.
Manually pre-fill 9+ page supplementary forms based on policy docs and conversations.
Rebuild nearly identical carrier-specific forms at new business and renewals.
Rely heavily on CSRs or manual effort as independent agents.

Core pain point:

Time spent collecting, re-entering, validating, and chasing data across multiple forms reduces selling time, constrains growth, and raises service burden.

Impact

On brokers: Lost selling time; over-reliance on CSRs; difficulty evaluating whether a prospect is worth the effort.
On agencies: Revenue per head constrained; independents fail due to service capacity; churn increases when service needs exceed bandwidth.
On underwriters/wholesalers: Messy or incomplete submissions require multiple back-and-forth cycles.
Desired state: A single smart intake → structured data → auto-filled carrier forms → automated renewal workflows.

3. Issues (Pain Points)

Redundant, fragmented form workflows across thousands of supplementals.
Slow information gathering (2–7 days of back-and-forth).
Manual pre-filling & copy/paste for every submission and renewal.
Renewal workflows are manual, tracked via meetings/spreadsheets.
Service burden limits growth; independent agencies can’t scale.
Tool sprawl with no unified “all-seeing eye.”
Language gaps limit addressable market (Spanish-speaking owners, etc.).

4. Discovery

Current State Workflow

Prospect engagement → review existing policies and find a “wedge.”
If moving forward:
- Request documents.
- Select one supplemental as a proxy intake.
- Pre-fill using policy docs + notes.
Send to client → multiple iterations to collect missing information.
Submit to markets (carriers or MGAs/wholesales like CRC, Burns).
Carrier selected → must fill new proprietary supplemental + accords.
Renewals handled manually with monthly meetings and email reminders.

Stakeholders

Primary: Independent brokers (Ian).
Secondary: CSRs, wholesalers, MGAs, underwriters.
Value receivers: Agency owners seeking higher revenue/head + scalability.

AMS ecosystems like Vertafore.
Tools mentioned: WonderWrite, Bold Penguin, Sembly/Assembly-type platforms.
Ian’s custom GPT “AI account executive.”

Research

Deep qualitative discovery call (Brainforge × Contextual × Ian).
Ian’s experience evaluating 40–50 InsurTech demos.

5. Goal

Demonstrate a POC MVP proving that Brainforge × Contextual can:

Cut submission workflow time from 1 week → ≤ 1 days.
Auto-fill carriers’ supplemental PDFs from a single intake + docs/transcripts.
Offer a clear path toward a full “AI account executive.”

Success Criteria

≥50% reduction in manual field entry.
Auto-fill at least 2–3 carrier/wholesale forms + standard accord from one intake.
Show reduced client back-and-forth cycles.
Generate renewal questionnaires from last year’s policy data.

Objectives

Build one end-to-end workflow (intake → normalizer → form fill).
Demonstrate RAG extraction from documents and transcripts.
Implement renewal snapshot + automated questionnaire generation.

6. Architecture

Core Components

Intake & Transcript Ingestion Service
- Supports smart intake forms, PDF uploads, and transcript ingestion.
Form Schema & Mapping Engine (Contextual)
- Normalized field model per vertical.
- YAML/ACL-driven mapping to carrier forms.
Auto-Fill & Review Interface
- View/edit auto-filled forms; surface missing fields.
Renewal Snapshot Module
- Stores policy metadata; re-generates updated questionnaires.

Technical Approach

Frontend

Submission dashboard
Smart intake builder (verticalized)
Form review UI (PDF or structured)

Backend

APIs for submissions, documents, extraction, form generation
Supabase database for clients, submissions, policies, forms

AI/ML (Contextual)

RAG over PDFs + transcripts
Agentic workflows for:
- Field extraction
- Form population
- Missing-field detection

7. Solution

Partnership Model: Brainforge × Contextual AI

The Smart Submission & Renewal Assistant is delivered as a joint solution where Brainforge and Contextual AI contribute distinct, highly complementary components.

This section clarifies who owns what, what each partner delivers, and how the combined system becomes a market-differentiating product for insurance brokers and carriers.

Contextual AI – Intelligence Layer (Core Engine)

Contextual AI provides the structured automation engine that powers the core intelligence of the system:

1. Document & Transcript Understanding

Extracting structured fields from PDFs, policies, contracts, accord forms, and transcripts.
Normalizing data into a unified risk profile.
Reconciling conflicting fields across document sets.

2. Form Schema Mapping Engine (ACL/YAML Driven)

Mapping normalized data to specific carrier supplemental applications.
Applying field transformation rules and validations.
Enabling reusable, version-controlled schema libraries.
Form-fill automation for supplemental apps and standardized accord forms.

3. Agentic Workflow Orchestration

Multi-step workflows: extraction → normalization → validation → mapping → completion → missing-field detection.
Self-checking agents that refine results and output structured JSON.
Insurance-specific logic tuned through SME guidance and sample forms.

4. Model Hosting + Enterprise RAG Infrastructure

Secure model execution (no training on customer data).
Long-context retrieval optimized for underwriting documents.
Auditability and traceability suitable for regulated industries.

Contextual’s role ensures the AI is accurate, reliable, structured, and scalable for insurance use cases.

Brainforge – Product, Implementation, and GTM Layer

Brainforge delivers the application and customer-facing components that turn Contextual’s AI engine into a deployable, sellable product:

1. Productization & UX Layer

Broker dashboard & submission management
Intake builder UI
PDF review & edit interface
Renewal workflow interface
Client-facing intake experiences

2. Integrations & Infrastructure

Supabase-based data model
Backend APIs for submissions, forms, extraction, transcripts
PDF hydration & export layer
Optional AMS/CRM integration
Authentication, permissions, deployment, hosting

3. Verticalization & SME-Collaboration

Creating standardized schemas for specific verticals (e.g., construction, concrete, developers)
Working with brokers & carriers to validate workflows
Setting up reusable templates for future insurance lines

4. Enterprise Implementation

Onboarding & configuration for each brokerage
Tailoring workflows, forms, validation logic
Customer support, training, enablement, and change management

5. Sales & Go-to-Market

Leveraging existing Brainforge brokerage clients
Co-selling with Contextual into larger enterprises
Packaging, pricing, licensing, and customer success

Joint Value Proposition

Together, Brainforge × Contextual deliver something neither partner could deliver alone:

A verticalized, end-to-end insurance automation platform that combines an expert workflow layer (Brainforge) with a world-class AI extraction, reasoning, and form-automation engine (Contextual).

The resulting product:

Can be deployed to small independent brokers and large carriers.
Reduces submission time dramatically.
Creates a defensible technical and GTM moat.
Produces a repeatable, scalable partnership model.

MVP Scope (Week 1)

Included

Contractor vertical smart intake (normalized schema).
Extraction pipeline combining intake + PDFs + transcripts.
Auto-fill for 2–3 supplemental forms + accord.
Renewal snapshot + auto-generated renewal questionnaire.

User Flow (Ian)

Create new submission.
Upload documents + transcript.
Send or fill intake.
System generates normalized profile → auto-fills forms.
Ian reviews forms, edits as needed, exports PDFs.
Renewal module generates updated questionnaires.

Key Features

Smart intake → multi-form mapping
RAG-based field extraction
“Remaining questions” auto-generator
Renewals engine (initial version)

8. Design

Key UI Components

Submission Card

+------------------------------------------------------+
| Submission: ACME Concrete LLC                        |
| Vertical: Construction                               |
| Missing Fields: 7                                    |
| Next: Send Intake / Upload Transcript                |
+------------------------------------------------------+

Intake Form

Annual Revenue: ______
Revenue Split: Commercial __%  Residential __%
Top 5 Contracts: ______
Work Type Mix: Ground-up [ ] Remodel [ ]
...

Form Review

Carrier: XYZ Contractor Supplemental
Filled from Intake: 45 fields
Filled from Docs: 12 fields
Remaining: 8 fields
[View PDF] [Export PDF]

Design Principles

Broker-first
Explainable AI
Low cognitive load

9. Data Model

Supabase Tables (Simplified)

CREATE TABLE clients (...);
CREATE TABLE submissions (
  id UUID PRIMARY KEY,
  client_id UUID REFERENCES clients(id),
  type TEXT,
  status TEXT,
  created_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE risk_profiles (
  id UUID PRIMARY KEY,
  submission_id UUID REFERENCES submissions(id),
  data JSONB,
  created_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE form_templates (
  id UUID PRIMARY KEY,
  name TEXT,
  carrier_name TEXT,
  vertical TEXT,
  schema JSONB,
  created_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE filled_forms (
  id UUID PRIMARY KEY,
  submission_id UUID REFERENCES submissions(id),
  form_template_id UUID REFERENCES form_templates(id),
  filled_data JSONB,
  created_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE policies (
  id UUID PRIMARY KEY,
  client_id UUID REFERENCES clients(id),
  carrier_name TEXT,
  policy_number TEXT,
  renewal_date DATE,
  latest_risk_profile_id UUID REFERENCES risk_profiles(id),
  created_at TIMESTAMP DEFAULT NOW()
);

Relationships

Clients → Submissions → Risk profiles
Form templates → Filled forms
Clients → Policies

10. Success Metrics

Adoption

Number of submissions created
Number of forms auto-filled

Efficiency

% of auto-filled fields
Time from intake → ready-to-submit

Quality

Broker-rated quality
Reduction in client follow-up cycles

Business

Increased submissions handled per broker
Pricing hypotheses validated

11. Timeline & Milestones (Week 1)

Monday

Finalize vertical, collect forms, define schema.

Tuesday

Database + APIs + YAML mapping.

Wednesday

Smart intake UI + first end-to-end test.

Thursday

Add forms, missing-field logic, renewal module.

Friday

Live MVP demo + documentation.

12. Dependencies

Technical:

Contextual AI pipelines
Supabase
PDF parsing/rendering

External:

Real carrier forms from Ian
Sample documents/transcripts

Team:

Brainforge AI
Contextual Customer Success and Engineering
SMEs for validation

13. Future Enhancements

Full AI Account Exec (email monitoring, AMS sync)
Additional verticals
Multi-language intake (Spanish-first option)
Carrier/wholesaler portals integration
Underwriter-facing structured submission interface

14. Open Questions

Which exact forms maximize value in the POC?
How much real sample data can Ian provide?
Should POC emphasize UI polish or backend engine quality?
What minimum automation level defines “AI account executive” for v2?
Pricing model post-POC?

how is form fill / form ingestion going to be done differently via AI?
- There is no active form fill people do it manually
areas of overlap for brokers and underwriters - are there features that we can build out to ultimately support both sides?
- Would focus on brokers for now
ideal submission workflow and review validation

Decision Needed By: End of Next Cycle

Decided By: Brainforge AI, with Contextual AI Support

15. References

Discovery call transcript (Brainforge × Contextual × Ian)
TEMPLATE.md PRD structure

Brainforge Knowledge

Explorer

prd-contextual-ai