An AI pipeline that pulls from 19 scrapers and data sources — LinkedIn, SEC EDGAR, Crunchbase, and more — to generate executive dossiers, account plans, and conversation guides automatically. Enterprise teams walk into high-stakes calls prepared.
Quality pre-call research — understanding an executive's background, career arc, leadership style, company strategy, and conversation hooks — takes 3 to 5 days when done by a human analyst. Most teams skip it. Lead Detective eliminates the tradeoff.
| Metric | Human Analyst | Lead Detective |
|---|---|---|
| Time per dossier | 3 to 5 days | ~8 minutes |
| Cost per dossier | ~$1,500 | ~$12.50 to $17.85 |
| Research depth | Surface-level web search | Career trajectories, LinkedIn posts, SEC filings, rapport hooks, tech stack, funding history |
A 10-minute Google search before a call does not surface career trajectories, published writing, board positions, or the topics an executive cares about publicly. It surfaces their LinkedIn headline.
A team of 10 SEs spending 10 hours per week on research equals 400 hours per month — $720K per year in labor. That time either gets cut (bad calls) or gets spent (no selling time).
ZoomInfo tells you who to call. 6sense tells you when to call. Nobody tells you what to say or how to open the conversation with the specific person in front of you. Lead Detective fills that gap.
Every investigation runs the same deterministic pipeline. User pastes a name. Eight minutes later they have a dossier a human analyst would need a week to produce.
User pastes their company website. AI automatically researches the company, identifies ICP, target personas, pain points, differentiators, and value propositions. Configures lead scoring criteria and research prompts. No manual setup required — the wizard does it.
AI-powered prospecting finds companies matching the ICP. Three discovery modes: ICP Match (ideal profile companies), Competitor Customers (accounts using alternatives you should displace), and Buying Signals (recent funding, key hires, expansion signals). Each lead gets a fit score 0-100 with machine-generated reasoning.
Pass/fail gates plus weighted signifiers. Hard requirements filter out disqualified accounts first. Soft signals weight the remaining accounts. Outputs four tiers: Hot, Warm, Nurture, Disqualified. Every lead has a score with the reasoning surfaced in the UI so reps understand why a lead ranked where it did.
Multiple scrapers fire in parallel: LinkedIn company page, tech stack via BuiltWith, SEC EDGAR filings, Crunchbase funding rounds, Glassdoor sentiment, Google News, company website. Raw data is structured into XML blocks with data-tier labels. AI writes a company profile, account plan, and org structure from the structured data.
LinkedIn profile scrape: title, career arc, skills, tenure. Recent LinkedIn posts for personalization hooks. Twitter/X activity. Published articles. AI synthesizes a person profile with conversation topics, rapport hooks, and explicit notes on what to avoid. The output reads like a briefing written by an experienced SDR who spent three days on the account.
Automated contact ranking by buyer likelihood. Title matching carries 60% weight. Seniority bonuses: C-suite +15%, VP +10%. Recency signal: new in role within two years adds +10%. Threshold of 70 or above designates a contact as Hero — the person most likely to be your economic buyer or champion at that account.
The most important architectural decision in Lead Detective: Claude AI is spawned as a child process with web_search and file_write tools — the same workflow as a human analyst. It reads data, searches the web for gaps, and writes documents. It is not prompted and responded to. It works.
19 specialized scrapers and data sources running across the research pipeline. Each scraper is purpose-built for its source: LinkedIn Profile, LinkedIn Company, LinkedIn Posts, BuiltWith tech stack, Google News, Glassdoor, SEC EDGAR, Crunchbase, Twitter/X, and more. Data quality filtering and deduplication run before the AI sees anything.
Three data tiers. Tier 1 is Apify-scraped primary data: LinkedIn, SEC, Crunchbase. Tier 2 is structured company data from websites. Tier 3 is AI web search fill-in. Tier 1 data is never overridden or contradicted by Tier 3. The hierarchy is programmatically enforced in the XML structuring layer before Claude sees the data.
Maximum 5 simultaneous research jobs running at any time. 10-minute timeout per job with graceful failure handling. Effect-TS manages the concurrency model so no job queue can starve or deadlock. SSE (Server-Sent Events) stream real-time progress updates to the frontend as each pipeline step completes.
The setup wizard researches your company and builds your full ICP automatically. No consultants, no onboarding calls. Paste your website and the system configures lead scoring criteria, research prompts, and persona definitions without human input. One project can have multiple revenue channels with separate personas and value propositions.
A second, independent model fact-checks every material claim in the dossier with a fresh web search before it reaches the rep. Each claim is graded High, Medium, or Low confidence. Anything that cannot be confirmed is explicitly labeled unverified rather than presented as fact.
| Tool | Answers | What It Misses |
|---|---|---|
| ZoomInfo / Apollo | WHO to call | Does not tell you what to say |
| 6sense / Demandbase | WHEN to call | Does not prepare the conversation |
| Lead Detective | WHAT to say ← IPS fills this gap | Nothing. This is the missing layer. |
| Gong / Outreach | HOW the call went | Post-game analysis, not pre-game preparation |
Lead Detective started as a question: what if a human analyst's 5-day research workflow could run in 8 minutes? The answer required 19 scrapers and data sources, a new concurrency model, and Claude running as a subprocess. That is the kind of engineering IPS does. If the problem is hard enough, we want to hear it.