Extruct AI

Extruct AI is best understood as infrastructure, not an app. The core is a search-and-enrichment API over a 10M-company index plus live web, and the distinguishing feature is that every search and every Deep Research report comes back with its source cited — which matters a lot if you are feeding results into an AI agent that has to justify what it says. The three discovery modes cover different jobs: Semantic Search for recall-first sourcing from a plain-English prompt, Lookalike Search when you already have one good company domain or UUID and want more like it, and Deep Search when fit matters more than volume. Deep Research generates cited reports on a company, person, or team, and AI Tables turn a list you already have into a repeatable enrichment and scoring workflow. The agent-native layer is where Extruct has pulled ahead of general-purpose enrichment tools: a remote MCP server puts Extruct search and tables inside Claude, Codex, Cursor, and VS Code, and the docs point you at an Extruct API Skill and third-party automation via CRM. The June 2026 change that lifted paid plans to 2,000 results per search removes the previous 250-result ceiling that constrained list-building. The tradeoffs are structural. Pricing is a single shared credit pool, so a Deep Search match costs 2 credits and an email lookup 4 while a phone number costs 20 — heavy phone sourcing will exhaust a Pro plan's 2,000 credits fast, and unused credits do not roll over month to month. Deep Search, AI research tables, and contact lookups sit behind the Pro tier, so Starter buyers get volume and exports but not the deeper workflows. It is not a CRM and does not do email sequencing or sales engagement, and the index skews to companies with a real web presence rather than long-tail private outfits. For a technical team wiring verified company research into its own tooling, that focus is the point; for a non-technical team that wants one login and a sequencer, it is friction.