Aidenix

Take a contact off this account's opt-out list — for a record added by mistake, or a person who asked to be contacted again. Only a record this account created can be lifted: the list is shared, so one account must not undo an exclusion another was asked for. Reports whether the record was lifted, was never there, or belongs to another account.

Record a person in this account's opt-out list when they ask not to be contacted. Takes an email, phone or LinkedIn profile and detects which it is. Repeating a contact is safe — the second call reports it was already there instead of adding it twice. The list is enforced: contact analysis refuses an excluded contact, and people lists leave them out. Costs no quota — recording a request not to be written to must work on an exhausted plan — but new records are capped at 10 per account per day, so this is for people who asked, one at a time, not for bulk-marking a list. Use remove_opt_out to lift a record this account made.

Return a complete user-facing single-contact business-fit analysis and outreach drafts. When presenting a completed result, hide technical metadata and use human-readable headings while preserving every user-facing content field.

Submit up to 500 contacts for a named batch business-fit analysis.

Build the seller's ICP profile by reading their own website: what they sell, the value proposition, which roles and industries they sell to, the pain points, proof points and tone of voice. The new profile becomes the active one, so every later score and every generated email is weighted by it — confirm with the user before running it on a website they have not named. Use it to onboard a new account or after a repositioning; use list_icp_profiles to see what is already there. The URL is the seller's own site, never the prospect's. A recent profile for the same site is not rebuilt — the upstream reports it already exists.

Admin-only: the upstream refuses this tool for customer accounts, so do not offer it as an option — company_signals covers the account and analyze_contact covers the person. Get the people of a company and the addresses to reach them. Given a domain, LinkedIn slug or name, returns the team by name — role, grade, seniority, country, LinkedIn — and the work addresses the sources actually hold, ranked by who decides and who is reachable. Every address carries what it is worth: on the company domain, a shared company inbox (info@ / support@), a short address two namesakes share, one a source flagged as a previous job, or a private mailbox — often the only way to reach the founder of a small company. Addresses are never invented from a name@domain template and are not SMTP-probed — these domains are mostly catch-all, so a probe would prove nothing; use email_intel when one specific address has to be trusted. Phone numbers are not returned, and people who opted out are excluded entirely. This is the roster layer: company_signals profiles the ACCOUNT, person_signals explains ONE human, analyze_contact scores the deal and drafts the outreach. Use roles=founder,ceo to keep only the people who answer cold email. Present who to write to and what their address is worth; keep the raw rows for your workflow.

Profile a company and its current moment before working its people. Given a domain or name, returns firmographics + revenue, team makeup, hiring velocity from careers (which catches layoffs a static card misses), activity signals (funding, hiring focus, product, partnerships, attrition), geo footprint, a growth / reorg / stealth / stable verdict with outreach timing, and a teaser of which organizations the team reads. Set relationships=true to expand that teaser into the full attention map — the orgs they follow (competitors / vendors / media), top public accounts influencing the buyer (warm-intro paths), and internal amplifiers by role. team.tools lists what the team uses inside (Snowflake, Kafka, Salesforce…) from current employees' skills, with how many people use each; add enrich=true for an LLM read of the opinion-leaders, what the pattern means, and org categorization (~20s, hot accounts only). Employee names are never exposed. Costs one request of the user's quota per call — priced per account, not per person, so one call carries every lead from that company; do not repeat it per contact. Call once per account for research and timing; then analyze_contact for the individuals. Present the verdict and timing; keep the raw blocks for your workflow.

Decide whether an email address is worth contacting, before drafting anything. Answers two independent questions: does the mailbox accept mail, and is the person still there. Catches what email verifiers miss — someone who left the company, a renamed domain, a historic address — because a live mailbox at a stale address still bounces the deal, not the message. Costs one request of the user's quota, the same as an analysis: run it before analyze_contact when the address might be dead — personalizing for someone who left wastes the analysis — and skip it on a contact you already trust, where it only adds a second request. Present the recommended action and the reason; keep raw verdicts and scores for your own workflow.

Assess a whole list of addresses in one call, before spending analysis on any of them. Same two questions as email_intel — does the mailbox accept mail, is the person still there — answered per address, and returned as a split by recommended action so you can drop the dead ones and keep the rest. Up to 50 addresses per call, or 10 when deliverability or enrich is on, because those layers are slow; split a longer list yourself. Costs one request of the user's quota per address, so it pays for itself on the addresses it lets you drop rather than analyse — worth it on a cold list, not on a handful of contacts you already trust. If the remaining quota covers only part of the list, the rest is reported as skipped, not assessed; say so instead of presenting a short answer as a complete one. Present the counts and name the addresses not to send to; keep the per-address blocks for your workflow.

Create a short-lived CSV or JSON export link for a completed batch.

Who a person is and what is going on with them. Given an email, LinkedIn URL, or slug, returns their role, grade and current company; the career trajectory from dates (months in the seat, whether they grew inside, their usual tenure); what they publish themselves and the subjects they raise; whose content they read; what they said about themselves (looking for work / hiring / milestone); and a verdict with the career moment and how to approach. Works even when the address is dead — the person is resolved to their current employer, which is often the point. Costs one request of the user's quota per contact, the same as an analysis — reach for it when you need the human behind the address, not as a warm-up before analyze_contact. This is the person layer: email_intel answers whether the ADDRESS is usable, analyze_contact scores the DEAL and drafts outreach, this one says who the human is. Asked by email, the answer carries no name and no profile slug — turning a mailbox into an identity is exactly what email_intel refuses to do, and this layer follows the same rule; ask by profile URL or slug and the name comes back, since the caller already holds it. Present the career moment, their own subjects, and the timing; keep raw blocks for your workflow. When match_confidence is low, say why: either the address did not match the profile name, or linked_profiles shows several people behind one address and the most corroborated was taken.

Find companies that fit — by industry, size, country, revenue, website tech, the tools their team uses inside, hiring, funding and what they are researching now — without people. Returns one row per company: name, domain, LinkedIn page, industry, size, country, revenue range, funding, website tech stack, intent topics, the company's own public emails, and why it matched. team_uses sees internal tools a website scan never does (Snowflake, dbt, Kafka, Gong), counted from current employees' skills, with how many people use each; the tool's own maker is left out. researching is buyer intent: topics the company's people read about lately. Each company returned costs one request of the user's quota; start with a small limit, then page with next_cursor. This is the first step of account-based work: pick the accounts here, then person_search with the same filters or with domains finds the people, and company_signals profiles one account in depth. Present the total and the companies with the reason they matched.

Return the authenticated Aidenix account plan, quota, and selected ICP, using the profile website as its human-readable label.

Poll one business-fit analysis job and return the complete user-facing result when finished; hide technical metadata and use human-readable headings while preserving every user-facing content field.

Get readable batch progress and optionally one page of results.

List batch analyses by name with readable status and progress.

List ICP profiles by website and product description, including which website is selected.

Find people who fit — by role, company and timing — with their contacts. Returns one row per person: name, title, seniority, department, company and its website, LinkedIn, location, the work email at the CURRENT employer, all known work emails, personal emails and phones from professional sources, months in role against their usual tenure, and why they matched. Beyond title and firmographics it filters on timing: started the role in the last N days, alumni of a company who left, in the seat longer than usual, engaged with a company's LinkedIn posts, talks about a topic, writes code in a language, company website runs a technology, fresh funding. Opted-out people are excluded. Each person returned costs one request of the user's quota; an empty page costs nothing — start with a small limit to see the total, then page with next_cursor. This is the way IN from an account or a segment: company_people lists one company's team (admin only), person_signals explains one human, email_intel checks an address you already hold. Emails here are not SMTP-checked — run them through email_intel_bulk before a send. Present the total, then the people with the reason they matched; keep cursors and raw fields for your workflow.