GTM Copilot.

The problem

Enterprise AEs spend 4–6 hours researching one account — and the research is stale by the meeting. Existing AI sales tools either hallucinate financial numbers (killing trust with the customer), skip persona context (the brief reads the same for an AE as for a CRO), or lack the audit trail enterprise procurement requires for adoption.

The downstream pain compounds: immediate ROI pressure (every sales-tech investment needs to prove itself in a single quarter), customer data silos and quality issues (CRM, marketing, support, finance never talking to each other), customer workforce adoption resistance (reps avoid tools that don't make their job demonstrably easier), and GDPR concerns that block deployments before they start.

The solution

GTM Copilot replaces account research with instant, structured, grounded, persona-aware briefs. AE provides an account name and optional deal context; the system resolves the company's SEC filing (CIK lookup → 10-K), builds retrieval indexes, and generates the brief structured around the persona (AE gets "what to ask next"; RVP gets "deal risk profile"; CRO gets "portfolio-level signals").

Every number traces back to its source. Every claim is retrievable. The Evaluation Framework is shipped as a first-class capability — not an afterthought — because evals-first positioning is the trust mechanism that makes enterprise adoption possible.

How it works

Flow: AE enters Account Name + optional Persona + optional Deal context (stage, type, incumbent, deal description) → GTM Copilot resolves CIK and identifies 10-K → builds/loads retrieval indexes / vector stores → runs persona-aware retrieval → generates the brief: Exec snapshot, 3–5 priorities, 3 risks, "What to ask next" → financial signals overlay (revenue trends, YoY, segment breakdown, account comparison) → real-time news intelligence merged in.

Primary model: openai/gpt-4.1-mini, selected for cost efficiency, RAG-constrained generation quality, and persona-aware framing reliability. The system prompt is strict: "Use only the provided structured facts and retrieved context. Never invent numbers." Required inputs: Company + Persona. Optional: Company B (for comparison), Seller, Deal Description, Deal Stage, Deal Type, Incumbent flag.

Use cases tested: Account Brief (AE persona), Account Brief with comparison (Company A vs Company B), Deal Context overlay for active opportunities, Financial Signals deep-dive, RVP / CRO persona briefs (different structure, different priorities surfaced).

Who it's for

Buyer / customer: Enterprise Sales Operations / Revenue Operations — they hold the budget, evaluate sales-tech, and own adoption. End-users: Account Executives (AEs), Sales Leaders (AEs, RVPs, VPs), RevOps / Sales Ops. Most revenue-generating users: AEs (volume of usage) and Sales Leaders (strategic decisions on portfolios).

B2B SaaS, enterprise tier. Trigger moment: "I need to prepare fast" — new account assigned, meeting in 2 hours, AE clicks GTM Copilot.

Why it matters

The AI-powered sales enablement category is one of the largest enterprise AI bets, but the trust gap kills adoption: hallucinated financials, generic persona framing, and lack of audit trails make enterprise procurement reject most AI sales tools at the security review stage.

GTM Copilot's structural bet is evals-first + seller-native + auditable multi-agent as the positioning that beats the "AI sales assistant" pack: by making the evaluation framework a shipped feature, the product passes enterprise procurement on day one; by persona-aware framing, it actually fits how GTM teams work; by auditable multi-agent, it gives RevOps the compliance story they need. The bet: in a noisy AI sales market, trust infrastructure is the product feature that wins enterprise deals.

At a glance

Project

GTM Copilot

Built by

Raghu Chanumolu

One-liner

Evals-first, seller-native AI sales enablement — auditable multi-agent Account Briefs, Deal Context Overlays, and 10-K financial intelligence for enterprise GTM teams.