Kitchen Sink

The Kitchen Sink resolvers are named after the saying "everything but the kitchen sink." The idea: pass in whatever you know about a person or company (any mix of identifiers), and Fiber does its best to find the match across its databases and additional sources. You don't have to know which field is "the right one," and you don't have to worry about the fields you're missing.

There are two resolvers:

The problem it solves

Sales teams and CRMs almost never have clean, uniform data. One record has a LinkedIn URL; the next has only an email; another has just a name and the company they work at; a company record might have only a domain, or only a brand name. Forcing the caller to pick the correct typed endpoint for each shape of data is brittle and slow.

Kitchen Sink collapses that into a single endpoint per entity type where you throw in whatever you have and get back a resolved, enriched record. This is why it's a favorite for:

• CRM enrichment / cleanup: normalize and complete records that each carry different fields.
• Lead intake: resolve inbound leads regardless of what the form captured.
• Data unification: merge partial records from multiple systems by resolving them all to the same canonical entity.

How it works

• Accepts any combination of identifiers. Provide one or several of the supported fields; Kitchen Sink combines whatever is present and ignores what's absent. More signals generally improve match accuracy.
• Auto-detect, don't pre-classify. You don't tell it "this is a slug" vs "this is a URL" — it figures out and normalizes the inputs.
• Finds matches wherever the signal lives. Kitchen Sink draws on Fiber's full identity graph (including resolving an email to the person behind it), so even sparse records land on the right entity.
• Fast and cost-effective by design. Lookups run against Fiber's continuously refreshed index and return quickly at low credit cost. When you need the freshest possible snapshot of an already-clean identifier, use Live Enrich instead.

Kitchen Sink Profile (person resolver)

Resolve a person from any mix of:

• a LinkedIn identifier: URL, slug, or numeric user ID (profileIdentifier),
• an email address (emailAddress),
• a name (personName), plus their company as a name, domain, or LinkedIn identifier (companyName, companyDomain, companyIdentifier),
• a job title or LinkedIn headline (jobTitle, linkedinHeadline),
• a school (schoolName, schoolDomain, schoolIdentifier), and
• a location (profileLocation: country code, state, or free text).

Text fields are objects with a value (personName: { value: "Jane Doe" }); the email is a plain string.

It returns the same comprehensive profile shape as live profile enrichment — 44+ top-level fields by default, including:

• Experiences (full work history) and tenures (time in role / at company)
• Education (schools, degrees, fields, years)
• Skills
• Tags (system-assigned, e.g., founder, investor)
• Inferred location with coordinates and timezone
• Career-began date
• Follower count and connection count
• is-hiring and open-to-work status flags
• Identity basics (name, headline, LinkedIn URL/slug, profile image)

// Pass whatever you have — any subset of these fields.
const person = await KitchenSinkProfile({
  body: {
    apiKey: process.env.FIBER_API_KEY!,
    profileIdentifier: {
      identifier: "linkedinUrl", // or "linkedinSlug" / "userID"
      value: "https://www.linkedin.com/in/example",
    },
    emailAddress: "jane@acme.com",
    personName: { value: "Jane Doe" },
    companyDomain: { value: "acme.com" },
  },
});

console.log(person.data?.output.data); // matched profiles, best first

Tuning a person lookup

Kitchen Sink Company (company resolver)

Resolve a company from any mix of:

• a domain (companyDomain, e.g. acme.com),
• a LinkedIn identifier (companyIdentifier): slug (e.g. stripe), URL, or organization ID (e.g. 1441),
• a company name (companyName, the brand name), and
• a headquarters location (companyLocation) to tell apart companies with similar names.

Ask for up to 10 candidates with numCompanies (default 1).

It returns rich company data in one call, including:

• Tech stack: technologies the company uses
• Funding: rounds, stages, amounts, investors
• Headcount history: employee count over time, not just the current number
• Social presence: follower count and LinkedIn activity
• Plus identity/firmographics: name, domain, slug, org ID, industry, HQ/location, founding date, operating status, logo.

const company = await kitchenSinkCompany({
  body: {
    apiKey: process.env.FIBER_API_KEY!,
    // any one (or several) of these:
    companyDomain: { value: "stripe.com" },
    companyIdentifier: { identifier: "linkedinSlug", value: "stripe" }, // or "linkedinUrl" / "linkedinOrgID"
    companyName: { value: "Stripe" },
  },
});

console.log(company.data?.output.data); // matched companies, best first

Bulk identifier-based lookup

When the messy records number in the dozens, the Kitchen Sink module also supports bulk identifier-based company and profile lookup: resolve up to 50 people or companies per call instead of looping the single resolvers. Every row in the batch can carry different identifier types, just like the singles: a CRM export where one row has a LinkedIn URL, the next only an email, the next a name plus company, all resolve in one pass.

const resolved = await KitchenSinkBulkProfile({
  body: {
    apiKey: process.env.FIBER_API_KEY!,
    profiles: [
      { profileIdentifier: { identifier: "linkedinUrl", value: "https://www.linkedin.com/in/example" } },
      { emailAddress: "jane@acme.com" },
      { personName: { value: "Jane Doe" }, companyDomain: { value: "acme.com" } },
      // ...up to 50 per call
    ],
    // liveFetch: true,  // optional: also refresh each person from LinkedIn
  },
});

// One array of matches per row, in the order you sent them.
console.log(resolved.data?.output.data);

Each bulk row takes the identity fields (profileIdentifier, emailAddress, personName, jobTitle, companyName, companyDomain, companyIdentifier, and numProfiles); liveFetch, forceCompanyMatch, fuzzySearch, thoroughness, and the detail flags apply to the whole batch. Bulk defaults to thoroughness: "low" for speed; set "high" when coverage matters more.

Bulk behavior to plan around:

• Charging is per entity, only when found. 2 credits per resolved person or company; unresolvable rows are not charged. A row that returns several candidates (numProfiles above 1) is charged for each one. The company bulk lookup works the same way at 2 credits per company.
• liveFetch opts each profile into a fresh LinkedIn pull for 2 additional credits per person. Leave it off for index-speed lookups; turn it on when the batch needs "as it stands right now" data.
• Give it room to run. Bulk calls carry a 3-minute recommended timeout; batches of 50 mixed-identifier rows are not sub-second calls. At 60 requests per minute, a 10,000-row backlog clears in about 200 calls and under 4 minutes of rate-limit window.
• Pair bulk resolution with batch contact reveal for full pipeline enrichment: resolve the messy identifiers first, then feed the resolved people into batch contact reveal for emails and phones.

Kitchen Sink vs. Search vs. Live Enrich

Mental model:

• Search discovers many.
• Kitchen Sink resolves one (or bulk) from whatever you have.
• Live Enrich refreshes one to "right now."
• Contact Enrichment reveals how to reach them.

Operations

Credit costs

• Kitchen Sink charges 2 credits per resolved entity, single or bulk, and only for entities found. Asking for several candidates bills each one that comes back. liveFetch adds 2 credits per person actually fetched.
• Listed amounts are defaults — custom pricing is available for bulk use cases (Billing & credits).
• Rate limits: 120 requests/minute for single lookups, 60/minute for bulk. Recommended client timeouts: 30s (company), 60s (person), 180s (bulk).
• It does not include contact reveal — emails/phones are billed separately via Contact Reveal (Normal stack: work email 2, personal email 2, phone 3; other stacks differ).
• output.chargeInfo is authoritative. Surface it verbatim, and check GET /v1/get-org-credits?apiKey=xxx (free) before large bulk resolutions.