DiligenceIQ

DiligenceIQ came to Forcoda with a clear thesis: even sophisticated investment shops still run their pipelines on spreadsheets, their diligence on email chains, and their risk scoring on gut feel. Information is fragmented across CRMs, data rooms, decks, and inboxes, and analysts spend 30–50% of their time on memo prep, document chasing, and formatting rather than on judgment work. The opportunity was to consolidate the entire investor workflow into one intelligent workspace — and to do it with an AI engine purpose-built for how investors actually work, not a generic chatbot bolted onto a CRM.

DiligenceIQ chose Forcoda for the depth of applied AI experience on the team and the discipline of building production-grade AI products in regulated, high-stakes environments. The brief was ambitious: ship a multi-mode AI platform that produces committee-ready output on the first pass, grounded in the firm’s own data, with enterprise security from day one — not as a later retrofit.

DiligenceIQ’s AI engine is built on a different premise. Rather than a single chat surface, the platform exposes five purpose-built reasoning modes — each with its own prompt architecture, retrieval strategy, output schema, and evaluation criteria. Every mode is grounded in the firm’s own structured data (pipeline records, company financials, six-dimension risk scores, document corpus) via a retrieval-augmented generation pipeline, so output is sourced from the firm’s reality rather than the model’s training distribution.

• Deal Memos — generates thesis, market, risk, and next-steps sections in a structured schema that matches PE/VC analyst conventions; pulls source data from the linked deal, company, and risk records
• DD Checklists — stage- and sector-aware reasoning produces a starter checklist with key findings, red flags, and recommended next steps; output is editable to house style and persists as the diligence record
• Risk Summaries — converts the six-dimension quantitative risk scores into board-ready narrative, with each statement traceable to the underlying score and mitigation entry
• Financial Insights — produces analyst-grade observations, concerns, and opportunities from the company’s financial data; structured to surface anomalies without hallucinating numbers
• Conversational Q&A — natural-language queries across the entire portfolio with streaming responses, retrieving from the firm’s data corpus rather than open-web sources

Each mode runs through a custom evaluation harness — the same engineering discipline Forcoda’s CTO Marc Asselin developed across 30+ years building production AI in defense, healthcare, and fintech. Output quality is measured against analyst-validated benchmarks, not vibes. The result is an AI surface that ships memo-quality output on the first pass, requires zero prompt engineering or API key management on the user side, and gets better with structured feedback rather than ad-hoc tuning.

Forcoda built a six-dimension risk scoring framework into the core of the platform: market, operational, financial, regulatory, technical, and competitive. Every deal in the pipeline gets scored on the same axes, and DiligenceIQ classifies each opportunity as low, medium, high, or critical — with mitigation strategies documented inline.

The framework turns gut-feel decisions into a defensible, repeatable process. Standardized scoring means every IC pack reads as if it came from the same shop, and portfolio-wide comparisons become trivial. For investment committees and LP updates, the AI generates an executive risk summary in seconds, drawn directly from the underlying scores.

AI quality is a downstream function of data quality. DiligenceIQ’s reasoning modes are only as good as the structured substrate they retrieve from — so the platform was architected from day one with a unified data model that treats every deal, company, document, risk score, and financial record as a first-class, queryable entity. The moment a deal is created, related companies, contacts, documents, and AI-generated artifacts are linked automatically. There is

no duplicate entry, no stale records, and — critically — no fragmented context for the AI to retrieve over.

This unified model is what lets the AI engine produce sourced, traceable output rather than confabulated summaries. When the Risk Summary mode generates a narrative, it pulls from the actual six-dimension scores and mitigation entries on that specific deal. When the Financial Insights mode flags a concern, it points to the specific revenue, burn, or runway figure that triggered it. The AI doesn’t make claims the data doesn’t support.

The security model meets enterprise standards from the first commit, not as a later retrofit: role-based access control, row-level security on every record (users only ever see their own firm’s data), encrypted credentials with no client-side secrets, server-side admin endpoints, and a real-time sync layer that keeps teams in lockstep without exposing the underlying database. For investment firms handling LP data, deal-flow confidentiality, and regulated portfolio information, this is the floor — not a feature.

Each AI mode was shipped, evaluated against real investment workflows, and refined based on output quality before moving to the next. Rather than building a generic AI wrapper and calling it done, Forcoda treated each AI capability as a standalone product surface — with its own retrieval strategy, output schema, evaluation harness, and editing experience.