[ OUTRICHER · AI DATA ANALYST & AI SEARCH ENGINE ]
The research layer over the most complete B2B database
AI search across one of the world's most complete bases of people and companies — we find anyone, on queries of any complexity. Not a raw list: a curated, ranked slice of exactly the people you asked for — through the AI agent, or through the API inside your systems.
Describe any group of people or companies in one sentence — the agent turns intent into a selection by meaning, not by keywords.
Every contact fully enriched: personal emails, phones, social profiles, the entire career — 130+ fields per profile.
Analytical slices and research reports: one sentence in — a ready, scored file with contacts out, usually the same day.
[ IN SHORT ]
A research layer on top of one of the freshest datasets in the world
Our database is continuously integrated and refreshed against the freshest professional datasets in the industry — the same data grade leading GTM platforms (the Clay category and similar) run on. On top of it — enriched professional profiles, GitHub and Crunchbase, an AI tool for exports, and an API for integration.
Active specialists weekly, the whole base monthly. Average freshness ~3 days, max 90 days — plus instant refresh on request.
Professional profiles + GitHub + Crunchbase + enrichment databases + hiring and funding data — aggregated and cross-checked, never collapsed.
AI exports built for your task (via an operator or self-service) — and an enrichment API to embed into your CRM, outreach and automations.
We unpack your task, show which data and signals will actually work, and stay with you from the first brief to a recurring pipeline. Operators 24/7.
[ 01 · THE TOOL ]
AI Search Engine + AI Data Analyst
The market has shifted: many have the raw data — what's scarce is the tool on top of it. But a tool is useless without depth. We have both: coverage is the floor; what we sell is the research layer above it.
Request any group of people or companies by the parameters you need. The agent clarifies the details and proposes a brief based on what's actually in the database.
Right in the chat: scores results against your ICP, surfaces the hottest leads and decision-makers, builds segments, runs the analysis and assembles the report.
All filters combine. The output is either a focused set of the hottest decision-makers, or a full export of everyone matching the request — every contact with evidence why it made the list. Via an operator (ready scored file in 24–48h) or via the API (plain-language request → JSON with ranked contacts).
What you can collect
[ 02 · DEPTH ]
Five things a catalogue architecturally can't hand you
Not «yet another data aggregator»: our own index over one of the largest profile datasets in the world, with cross-source JOINs competitors don't have. Five techniques unavailable in Apollo / ZoomInfo / Sales Navigator.
Combine signals that don't work in isolation
«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. Aggregators store these in separate tables with no cross-JOIN.
→ real case: 1,479 leaders · 86% emails
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 is NOT there
«Companies with NO HubSpot», «teams with NO VP Sales», «profiles that did NOT post for 3 months». Others filter by presence of a trait; we filter by absence — a different SQL operation competitors haven't implemented.
→ 3–4× less noise on outreach vs positive-only filters
where:
title contains 'Engineer'
experience > 5 years
company.has_title: NOT 'VP Engineering'
→ 3,847 senior ICs to recruit
Search by career history — who a person used to be
«Ex-SpaceX, now investing in AI», «ex-Stripe at funded startups», «YC 2020–2022 grads, now CTO». A career-history JOIN over 5.6 billion positions — the filter runs on a specific past+present pair.
→ up to 100% match precision
where:
past_companies includes [SpaceX, Tesla]
current_company.industry: 'venture-capital'
→ 287 ex-rocket engineers turned investors
10,000 contacts in one export — already scored, already segmented
Sales Nav — 2,500/mo and it's a raw list. Apollo — small batches through the UI. We deliver 10,000+ contacts in one export, sorted by score (hot / warm / cold for your ICP), split into ready segments.
→ outreach-ready format, not raw fields
├─ leads.xlsx (multi-sheet)
├─ leads.csv (CRM import)
├─ segments/
│ ├─ hot.csv (score 25+, email)
│ ├─ founders.csv · hiring.csv
└─ README.md (metrics)
Full context — not just email and title
Contacts (email waterfall, phone, GitHub, Twitter), current role and full history, education, skills, recommendations, salary estimate, profile activity, company size, funding and hiring status — up to 130 fields per person.
→ 50 fields in free sampling · up to 130 after full enrichment
contacts: email · phone · socials
career: 5–13 positions
company: size · funding · hiring
code: GitHub repos & activity
→ one stitched object, 130+ fields
[ 03 · AUDIENCES ]
Who it works for
Trigger signals: funding, hiring, role change. 130+ fields per lead — personalization that proves the email isn't a template. Outricher + an LLM + a sender = effectively an autonomous SDR.
8–15% replies vs 1–2% generic204M developers with GitHub — real code, not words on a résumé. Passive candidates ordinary search won't show.
25–40% reply rate vs 10–15%The personal email of an investor who already backed similar projects. Decision-makers for first sales. A co-founder currently between projects.
Is the company really hiring, or is it a ghost job? The hiring manager and their direct contact. Deep team research before the interview.
15–25% conversion vs 2–5%Three engineers leaving a company at once — a signal. Talent tracking, portfolio monitoring, competitive mapping.
[ 04 · RESEARCH DESK ]
Consulting-grade reports in a day
Beyond exports, the same engine produces finished research: market maps, talent-flow studies, ecosystem and audience analyses. The kind of work consulting firms bill in weeks, we assemble from live profile data usually within a day — charts, segments, conclusions and the contact files behind them. Below are public studies; most client research ships privately under NDA. Any of them started as one sentence in a chat.
Brighty App — a full company breakdown
6 reports from open data only: company and team, hiring dynamics, 8 audience portraits with ready contact segments, and the full anatomy of a 130-field profile object.
Open the study →
The SpaceX alumni ecosystem
One company's alumni traced through career history: who left, when, what they founded and who backs them. A targeting map for investors and recruiters.
Open the study →
The Russian-speaking tech exodus
A population-scale migration study built purely from profile data: which hubs absorbed the engineers, seniority and stack mix, and where density is high enough to hire or sell into.
Open the study →
Hustlemaxxing — the multi-job economy
How the workforce spreads across ten pay bands and where people quietly stack several roles at once — compensation analytics no salary survey can see.
Open the study →
US home services — an outreach-ready market
Thousands of US home-services businesses scored by digital gaps — who has no booking, weak web presence, room to sell into. A report that doubles as a ready lead pool.
Open the study →[ 05 · PROOF ]
Real cases
Names disclosed with client permission; numbers are from real delivery logs.
Adobe — data engineers by open-source activity
Java / Scala / C++ with live Apache activity, including 11 confirmed Apache committers (Flink, Spark, Trino, Iceberg). Scoring 0–27 → tiers A+/A/B/C; the team focused on 54 A+ candidates in the first two weeks.
B2B SaaS — founders of funded startups
8,597 contacts at 2,390 companies · 79% email on the top 3,000 · 77% actively hiring · 8 segments. Outreach via founders' personal email. The contract became recurring.
Series A/B — search by the absence of a role
A request Apollo and ZoomInfo can't run: «companies where the role doesn't exist yet». 1,479 leaders at 747 companies · 86% email. Pilot: 8% reply rate vs 2% baseline; 14 deals closed.
AI/ML recruiting — visa-friendly talent
8,000 validated candidates (4 batches) · ML engineers with 5–15 years of experience · education cross-checked against 7,650 US universities. Foreign-educated candidates converted to interviews ~30% better.
This is only a part. Tell us your task — we love unpacking new cases. Book a call or tg @outricher
[ 06 · WITH YOU ]
Consulting and integration
Most clients arrive with one task and leave with three. We don't just hand over a file — we help you build data, automation and AI into your processes.
20–30 minutes — together with our AI agent we unpack your situation and show which data and signal combinations will actually help.
Connecting to your CRM, outreach, n8n / Pipedream; trigger signals and scoring set up for your ICP.
Regular exports with refresh, and feedback on data quality and outreach conversion.
[ 07 · START ]
How to start
Describe the task
Who you're looking for, in free form — a single message on tg @outricher — or book a call.
Get a free trial set
The agent clarifies details and builds the brief. You get 20 trial contacts for your ICP, free — usually within 1–2 hours.
Scale or refine
Like it — order the full export or connect the API. Doesn't fit — we rebuild it for the refined request.
[ 08 · MARKET ]
How we compare to the market
The main difference isn't price — it's depth and the tool: one plain-language request returns data gathered from several sources and verified. For a sense of cost and coverage:
| Metric | Outricher | Apollo | ZoomInfo | Lusha | Clearbit |
|---|---|---|---|---|---|
| Price per contact | ~$0.09 | $0.15 | $2.00 | $0.12 | $0.71 |
| Database size | largest on the market | 275M | 260M | 150M | 400M |
| Career history | 5–13 positions | 2–3 | 2–3 | current | current |
| 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.
[ 09 · UNDER THE HOOD ]
The data, in full
Everything above rests on one database. This is the «basement» for those who want the details — you don't need it to start; just describe your task.
Company profiles, hundreds of millions of ready contacts (free from base), full career-position history, developer profiles, job postings and education records — one stitched dataset.
Selected profiles and companies go on watch: role change, new funding round, open vacancy, activity spike — a notification when something changes. Triggers combine for your ICP.
An email, a profile link, a phone or a social handle in — a full dossier back in 1–2 seconds. Works in both directions: contact → profile, and description → people.
Per contact for full data via API with a live refresh at request time — well below competitors. Exact cost depends on enrichment depth and volume.
[ 10 · THE OBJECT ]
What you get in a profile
The unit of data is a stitched profile: one person = identity + contacts + full career + current company + education + skills + public code, linked into a single object of 130+ fields. Sources are cross-checked but never collapsed — the same object answers sales, recruiting and research questions at once.
Person
+Identity & contacts4 groups
+Career & experience3 groups
+Education & developer profile5 groups
+Location, activity & extended profile7 groups
Company
+Basics, structure & relationships6 groups
+Funding & hiring7 groups · unique
Which fields matter to whom
| Task | What carries it |
|---|---|
| Sales | Role + company + headcount + geo + email. Timing signal: the start date in the role — the first 90 days is when new tools get evaluated. |
| Recruiting | Career history + education + skills + contacts. «Ex-fintech backend engineer near Tbilisi» is one query — past employer, geo and role live in a single object. |
| Investing & research | Company + headcount + founding year + hiring. A two-year-old company opening 10+ roles is a growth signal no business directory shows. |
| Marketing & ABM | Headline + industry + geo + activity. The headline is the person's own one-line pitch — personalization written by the recipient. |
The full object — a live example
One real, complete object from a recent API pull — a fractional CTO in London, 114 fields. Base fields, db_* and api_* layers are independent and never collapsed — that's why the union goes past 130.
+profile.json — open the full object114 fields · real API pull
{
"profile_id": 10153971,
"full_name": "Alfonso Ferrandez",
"headline": "The Restless CTO · AI Evangelist · Co-founder Equiyd · Educator · Advisor · Fractional CTO",
"title": "Fractional CTO",
"function": "consulting_strategy",
"role_bucket": "c_level",
"seniority_tier": "A",
"years_experience": 25,
"company_name": "Little Journey",
"company_domain": "littlejourney.health",
"company_employee_count": 38,
"current_positions_count": 8,
"location_name": "Greater London, England, United Kingdom",
"email": "alfonso.ferrandez@doctorlink.com",
"all_emails": [
"alfonso.ferrandez@gmail.com",
"alfonso.ferrandez@doctorlink.com",
"alfonso@fruitfulinsights.co.uk"
],
"personal_phone_numbers": ["+4478•••••928"],
"github_url": "github.com/drfonz",
"twitter_url": "x.com/drfonz",
"skills": ["Leadership", "Software Design", "C#", ".NET", "JavaScript", "SQL", …],
"db_experiences": [
{ "title": "Software Architect", "company": "Pace Micro Technology plc", "start_year": 2006, "end_year": 2008 },
{ "title": "Software Developer", "company": "Sony Computer Entertainment Europe", "start_year": 2001 },
{ "title": "Technology Adviser", "company": "Friend MTS", "start_year": 2023, "is_current": true },
{ "title": "Professor — AI Executive Master", "company": "Instituto de Inteligencia Artificial", "start_year": 2021 }
],
"db_education": [
{ "school": "University of Leeds", "degree": "PhD", "field_of_study": "Computational BioFluid Dynamics" }
],
"total_positions_count": 165,
"between_jobs": false,
"signals": ["signal_fractional", "signal_advisor", "signal_portfolio_career"],
"score_details": ["signal_fractional:+30", "signal_advisor:+18", "seniority_a_founder_clevel:+20", "has_email:+3"],
"lead_score": 99,
"lead_label": "A+",
"outreach_ready": true,
"api_connectionsCount": 4496,
"api_followersCount": 6362,
"api_seniority": { "totalExperienceYears": 24, "currentTenureYears": 12 },
"salary_min": 150000,
"salary_max": 250000,
"gender": "Male",
"db_profile_activity_at": "2026-03-05T13:26:40Z",
"…": "— 114 fields in this object; full schema in the docs"
}
[ 11 · COMPLIANCE ]
Sources and compliance
Ready to start, or just want to talk?
Full documentation, JSON response examples, a free trial export, an AI-tool demo, or a live consultation — all through one chat.