WinShark.

The problem

Enterprise sellers walk into deals under-prepared. Manual account research eats hours per opportunity, intelligence is scattered across LinkedIn, earnings transcripts, news, and CRM, and the battlecards that do exist are generic feature sheets that don't address the specific account, deal stage, or competitive threat in the room.

The downstream pain is predictable: objection surprise (rep hears something they didn't prepare for), generic messaging (talk track misses what *this* customer actually cares about), inconsistent prep quality across the team, poor competitive positioning, and over-reliance on memory in high-stakes meetings.

The solution

WinShark replaces the static battlecard with a real-time AI-generated one. The rep enters company + URL + prospect details + product context; the AI analyses; out comes a structured, executive-ready battlecard plus a how-to-win report with talking points, anticipated objections, executive KPI inferences, and a recommended next move.

The output is intentionally enterprise-grade — operating "at the level of a top-performing AE or Strategic Account Manager" — and tagged for win/loss after the meeting so the system learns from outcomes.

How it works

Flow: Dashboard → Click "+ New Battlecard" → enter company + URL + prospect details + product context → Click Generate → AI analyses inputs → returns structured battlecard + how-to-win report → export/share PDF → use in meeting → tag outcome (win/loss) → AI learns → suggestions improve.

Primary model: GPT-4.1 / GPT-4o (OpenAI) — chosen for reasoning depth, structured output generation, and enterprise-grade language quality. System prompt frames the AI as an enterprise-grade sales intelligence strategist. Test scenarios cover three categories: core use cases, edge cases, and negative cases — ensuring reliable performance in real-world deal conditions.

Who it's for

End users: Sales Representatives, Account Executives, SDRs, and Strategic Account Managers — the people who walk into the room with the prospect. Budget-holder influencers: VPs of Sales, CROs, Sales Directors, and Founders — who feel the team-wide consequences of under-prepared reps.

Segment: enterprise and mid-market B2B SaaS sales teams operating in complex, multi-stakeholder cycles where generic pitches get ignored. Business model: B2B SaaS, sold to sales orgs.

Why it matters

The Sales Enablement Platform Market is projected to expand to $25–35B by the mid-2030s at 16–18% CAGR, but the category is choking on budget scrutiny and sales tech fatigue — reps already have too many tools, and CROs are cutting the ones that don't visibly drive revenue.

WinShark's wedge is AI-native architecture that eliminates the retrofitted-AI feel of legacy platforms, real-time outputs that stay current with every market signal, and a proprietary deal-outcome dataset that compounds with every win/loss tagged. The bet: the future of sales enablement is one structured AI-generated brief at a time, not another static playbook library.

At a glance

Project

WinShark

Built by

Anand Dawson

One-liner

AI-native sales intelligence generating hyper-personalized, real-time battlecards and how-to-win reports for enterprise B2B sales teams.