AI Search Engine+ AI Data Analyston the most complete B2B database of profiles, GitHub and funding data. Tell the AI assistant what you're looking for — get a ready report with contacts.
// no signup · first sample within 1–2h · we onboard a limited number of new clients each month
Outricher is not affiliated with any professional network. Data is collected from open professional sources. GDPR / CCPA · DPA on request.
| # | Name | Role · Company | Signal | Why now | |
|---|---|---|---|---|---|
| 001 | Sarah Chen | Founder · CEONeuralFlow AI | Seed $1.8M | raised 74d ago · no sales team yet | ✓ s@neural.ai |
| 002 | James Rodriguez | Co-FounderSecureStack | No VP Sales | hired 2 SDRs — nobody to run them | ✓ j@securestack.io |
| 003 | Priya Patel | VP GrowthLoopflow | Series A · scaling | day 34 in role — builds the stack now | ✓ p@loopflow.com |
| 004 | Marcus Reyes | Head RevOpsBridgeStack | Hiring 8 AEs | scaling outbound · evaluating tools | ✓ m@bridge.co |
| 005 | Anya Volkov | FounderSynthlab.io | Pre-seed · MLOps | ex-Stripe infra · first GTM hire open | ✓ a@synthlab.io |
| 006 | Dmitri Ivanov | CTORouteForge | Seed · logistics | active on GitHub · owns tooling budget | ✓ d@routeforge.com |
Consulting-grade reports. In a day.
The deck a strategy consultancy bills a quarter for — our tool assembles it from live profile data in a day, and our analysts sharpen the story. These are real public studies, built exactly this way.
$ outricher report --topic "russian-speaking tech diaspora" → resolving cohort ………………… 1.2M professionals · 30 countries → signals ……………………… education × language × migration · 3 independent → calibration ………………… 69,142 known profiles · exact counts → charts rendered …………… 48 · copy drafted · sources pinned ✓ report.html ready [ 07:41:12 ]
The Russian-speaking exodus
1.2M professionals with Russian education mapped across 30 countries — three independent signals, calibrated against 69,142 known profiles. Scale, destinations, and the inner workings of "Russian IT" abroad.
Read the study →What we do — what others architecturally can't
Not "another data platform." Our index over hundreds of millions of profiles + JOINs between sources that aggregators don't have.
Combine signals that don't work alone
"Funded companies that hired an SDR but have no VP Sales yet" — a buying trigger at the intersection of three independent sources: funding rounds, job postings, current org chart. Architecturally impossible for aggregators — they store sources as separate tables without JOIN.
target: companies where: funding_round in [seed, series_a] funding_recency: 6 months has_open_role: SDR | BDR no_employee_with_title: VP Sales → 1,479 leaders · 86% emails
Search by what's not there
"Companies without HubSpot", "teams without VP Engineering", "profiles inactive for 3 months". Apollo and Sales Nav filter by presence. We filter by absence. They simply don't have this SQL operator.
target: people where: title contains: 'Engineer' experience > 5 years current_company: NOT has 'VP Engineering' → 3,847 senior ICs to recruit
Search by career history — who someone used to be
"Ex-SpaceX, now investing in AI", "ex-Stripe at funded startups", "YC 2020-22 grads now CTOs", "worked at FAANG, now at sub-50 startups". Career-history JOIN on billions of positions.
target: people where: past_companies includes [SpaceX, Tesla, Boeing] current_company.industry: 'venture-capital' → 287 ex-rocket engineers turned investors
10,000 contacts in one export. Scored, segmented
Sales Nav — 2,500/mo limit, raw list. Apollo — small batches through the UI. We deliver 10,000+ contacts in a single file, sorted by score (hot / warm / cold for your ICP), pre-segmented ("with email", "actively hiring", "founders only").
delivery/ ├── leads.xlsx (multi-sheet) ├── leads.csv (CRM import) ├── segments/ │ ├── hot.csv (score 25+, with email) │ ├── founders.csv │ └── hiring.csv └── README.md (metrics, distributions)
Full context — not just email and title
Up to 130 fields per person: contacts (email waterfall, phone, GitHub, Twitter), full career (5–13 positions vs 2–3 at competitors), education, skills, recommendations, salary estimate, profile activity, company size and funding, hiring status. 50 fields at sampling, up to 130 after full enrichment.
Name the direction. We cut the exact slice.
Six directions, one stitched database. Each one runs on a different combination of signals — funding, hiring, career history, public code, activity — until the list is exactly the people who matter right now.
Buyers with budget and timing
Not "companies in your industry" — companies at the exact moment they buy.
Investors who already believe
The warm subset of the investor universe — found through what they actually backed.
Personalization written by the data
Every row carries the reason to write — so the first line is never a template.
Candidates by real signals, not résumés
Skills verified by public code, mobility read from career history.
The company, the team, the hiring manager
Skip the black-hole application form — go in the front door with context.
Portraits and maps of whole markets
Audience portraits, talent maps, TAM slices — with reachable contacts attached.
Not cheaper. Deeper.
The main difference isn't price — it's depth and the tool: one plain-language request returns data from several sources, verified.
| Metric | Outricher | Apollo | ZoomInfo | Lusha | Clearbit |
|---|---|---|---|---|---|
| Price per profile | $0.09 | $0.15 | $2.00 | $0.12 | $0.71 |
| Database size | 100M+ | 275M | 260M | 150M | 400M |
| Career history | 5–13 positions | 2–3 | 2–3 | current only | current only |
| GitHub data | 204M | — | — | — | — |
| Personal emails | 237M (free from base) | paid | paid | paid | — |
| Freshness | daily/weekly | monthly | quarterly | unknown | no SLA |
| Plain-language AI search | yes | filters only | filters only | — | — |
| Custom exports | 24–48h for your task | self-serve | self-serve | — | — |
Apollo and ZoomInfo are great self-serve filtering tools. Outricher solves a different problem: you describe the case — we engineer the data for it, with depth (full career, GitHub, funding, hiring) that ready-made catalogs don't have.
Real cases. No varnish.
What we actually shipped — scopes and coverage straight from delivery logs. Where a client is named, the name is disclosed with permission.
Adobe — data engineers by open-source activity
Immigration services — their future clients, found by signal
Brighty — audience portraits for a fintech app
B2B SaaS — founders of funded startups
Series A/B — search by absence of a role
AI/ML recruiting — visa-friendly talent
Three steps. No onboarding.
Describe the case in plain language. We engineer the data. You get a scored, ready-to-use file.
Describe the case
In one sentence or a short brief — who you need, which signals matter, what to exclude. No filters to learn, no UI to master. We reply with a pre-computed estimate at 0 cost.
We engineer the data
We run the cross-source query, score every row for your ICP, segment it, and enrich contacts through the waterfall. You get a first sample to validate the direction.
Take the scored file
Full delivery: scored Excel (multi-sheet) + CSV for CRM import + segment files + a README with metrics and distributions. Ready for outreach, not raw fields.
One POST. 130+ fields. 1–2 seconds.
Full context of one person
Send an email, profile URL, phone, GitHub or social handle — get 130+ fields in 1–2 seconds. Plug into your script, outreach, CRM or automation — every contact becomes a full dossier.
- Career — all positions (5–13 vs 2–3 at competitors), dates, role descriptions
- Contacts — personal + work email, phone, social profiles, GitHub
- GitHub — repos, languages, activity, hireable flag (204M — unique)
- Company — size, industry, org structure, funding (seed→IPO rounds)
- Education — universities, degrees, years, field
- Freshness — last-activity date + our refresh date on every profile
- Extras — skills, certifications, salary estimate, recommendations, interests