Sixtyfour

What do they actually do

Sixtyfour provides a developer-facing API and small dashboard that runs “research agents” you call from your code. Today, the API has endpoints to enrich a person, enrich a company, find email or phone, and run a Q&A/agent query. Each call returns a short written note, a machine-friendly structured_data object (e.g., name, email, title, website, location, industry), and an array of findings or signals the agent detected (such as hiring changes, funding, or expertise) docs API reference.

You sign up, generate an API key in the dashboard, and call REST endpoints (synchronous for single records, async for larger jobs). Sixtyfour publishes Colab/Jupyter notebooks so teams can prototype quickly and export results into CRMs, ATSs, or data lakes. Public case studies show usage by go‑to‑market, recruiting, and research teams to enrich people/companies, find contacts, and surface domain experts for model work notebooks Mercor case study Whatnot case study YC profile.

Who are their target customer(s)

• Sales / go‑to‑market (SDR/AE) teams: Prospect research is slow and spread across sources; reps need reliable contact fields and timely signals (hiring, funding) to prioritize outreach and personalize messages docs Mercor case study.
• Recruiting / talent teams: Finding and verifying niche candidates and contact info is manual and error‑prone; teams want structured person data and contact findings pushed into the ATS without hours of web searches YC profile docs.
• Market research / competitive intelligence teams: Mapping markets and linking people ↔ companies ↔ events requires stitching data from many sources; doing this programmatically and exporting clean, structured outputs is hard with current tools docs.
• ML / data teams sourcing domain experts or annotators: Identifying qualified experts or contributors for labeling and evaluation at scale is difficult; teams need signals that surface relevant expertise with enough structure to feed internal pipelines Whatnot case study docs.
• Enterprise research / ops (sales ops, customer success, product ops): They need continuous, pushable signals in CRMs and dashboards, but current workflows rely on manual exports and custom engineering; tighter integrations and real‑time monitoring reduce operational load docs pricing.

How would they acquire their first 10, 50, and 100 customers

• First 10: Run white‑glove pilots via YC network and early inbound: pair each pilot with an engineer to wire the API/notebooks into the customer’s CRM/ATS and deliver a small exportable dataset; use Mercor and Whatnot case studies as proof Mercor Whatnot notebooks.
• First 50: Add targeted outbound to Heads of Sales, Talent, and Research while publishing hands‑on Colab quickstarts and workflow templates so engineers can self‑serve pilots; offer short paid pilots or usage credits and surface docs in outreach to cut integration friction docs pricing/contact.
• First 100: Productize common jobs (prospecting, candidate enrichment, market maps), launch self‑serve discovery and one‑click connectors (CRMs/ATS/BigQuery), and list in partner marketplaces; pursue channel partnerships with recruiting platforms/data vendors and formalize referrals/case‑study discounts docs/roadmap about.

What is the rough total addressable market

Top-down context:

Near‑term TAM aligns with sales intelligence and data‑enrichment spend, roughly $7B combined in 2024 (sales intelligence ≈ $4.4B; data‑enrichment ≈ $2.4–$2.6B) sales intelligence data enrichment. Larger adjacencies (recruitment tech, market research, data labeling) push a directional ceiling into the low‑hundreds of billions but are not immediate targets recruitment market research data labeling.

Bottom-up calculation:

If vendors charge roughly cents per enrichment and typical teams enrich tens of thousands to millions of records annually, account spend ranges from tens of thousands to several hundred thousand dollars. Multiplying that by tens of thousands of GTM/recruiting teams globally yields a multi‑billion market for API‑driven enrichment and signals; Sixtyfour’s current product sits squarely in this slice docs.

Assumptions:

• Sales intelligence and data‑enrichment categories overlap; summing them overstates total but gives a practical ceiling for today’s product.
• Spending ranges assume API pricing per enrichment and typical annual volumes for GTM/recruiting teams; actual mixes vary by company size and data freshness needs.
• Expansion into recruiting, market research, and ML data would capture only slices of those markets and require additional product and integrations.

Who are some of their notable competitors

• ZoomInfo: Enterprise B2B data platform with contact/company records and intent/news signals plus a mature enrichment API and deep CRM integrations; overlaps on enrichment and signals but is built around a curated database rather than agent‑driven web discovery API docs Data API overview.
• Clearbit: Developer‑friendly real‑time enrichment (firmographic/technographic attributes) via API and webhooks; strong for instant, standardized fields in marketing stacks, less focused on bespoke agent‑extracted findings Enrichment Help docs.
• Apollo.io: Large contact/company database with enrichment APIs plus built‑in outreach tools; appeals to GTM teams wanting both data and execution (sequencing, lists) rather than multi‑agent research workflows People Enrichment API docs.
• Cognism: B2B data provider with strong EMEA coverage, compliance posture, and enterprise features; direct alternative for international enrichment and mobile numbers pricing/product.
• Hunter: Simple email‑finder and verification API for teams that need reliable addresses at scale; competes on the contact‑finding piece but not on complex signals or market‑mapping agents Email Finder API Overview.