Overview
A founder-led B2B SaaS operator (under 10 employees, founded 2024) runs a boutique outbound program — high-quality, manually reviewed touches, ~150 per week — against a custom ICP framework. Instead of buying a sales-tech stack across firmographics, list-building, enrichment, and delivery, the team built a single pipeline ondb/linkedin/search/companies plus four supporting endpoints.
The database-backed search replaces a multi-vendor stack with one structured payload and a mini-DSL for keyword matching. Output is raw rows the team scores on their side — no inherited vendor schema, no per-record fees on data discovery.
14,000 companies
Discovered in a single sprint from one query (~2s wall-clock)
180 ready-to-send leads
Hook + verified email + no role drift, per week
~$15–20 / sprint
Out-of-pocket LLM cost; discovery API calls free under MCP Unlimited
The Challenge
Boutique B2B outbound demands a tight ICP: not “B2B tech” but a specific cross-section of company type, size, geography, funding stage, and product positioning. Off-the-shelf sales-tech stacks force a coarse ICP — industry codes, employee ranges, hard categories. To narrow further, teams stitch four to six vendors with overlapping pricing and partial coverage, then export and re-classify on their own side anyway. The team needed:- Bulk discovery in one call — pull 1,000–10,000 candidates per sprint, not 50-result pagination loops
- Soft-signal search — match phrases across
description,short_description,specialities,hashtags, andlocationssimultaneously - Strict structured filters — country (ISO2), employee range, founded year, active-status flags — applied at the database layer, not client-side
- Sub-second iteration — try ten different keyword sets in an hour, not three over a week
- Schema ownership — every row returned in full LinkedIn shape so the team writes its own scoring rubric
The Solution
The/db/linkedin/search/companies endpoint exposes the database directly via a structured payload. Keywords get a mini-DSL (whitespace = AND, | = OR, "phrase" = exact phrase, -token = NOT). Filters are typed: employee_count_min/max, founded_on_min/max, country_hq, country_any, is_active, has_description, min_description_length, and more.
1. Cross-field keyword search via mini-DSL — 70% of calls
A singlekeywords field searches across name, description, short_description, specialities, hashtags, and locations simultaneously. For 27 ICP categories ranging from autonomous outbound agents to compliance research tools, the team builds one DSL string per sprint:
2. Token-aware structured geo + size filters — 20% of calls
A common pitfall in coarse country filters is substring matching: theUS token used to false-match against Austria, Australia, Austin, Houston. The country_hq and country_any params parse the ISO2 code directly out of headquarter_location and the locations[] array, eliminating that class of false positive.
3. Per-field DSL for surgical narrowing — 10% of calls
When the cross-fieldkeywords casts too wide a net, the team narrows by specific fields:
specialities: "data engineering|ML"— match only in declared specialities, ignoring marketing-copy descriptionname: "ai|agent"— match in company name (catches AI-native startup branding)description: '"sales intelligence" -consultancy'— boolean exclusion of consulting hits
The Data Pipeline
Daily ICP refresh runs as a chained pipeline:
End-to-end run time: ~1.5 hours for 14,000 companies. Cost out-of-pocket: ~$15–20 in LLM calls. Discovery API calls: free under MCP Unlimited.
Results & Scale
The team operates the entire outbound funnel — discovery, enrichment, scoring, intent detection, personalization, delivery prep — without subscribing to a separate firmographics, list-building, enrichment, or delivery vendor.
Key Anysite Endpoints Used
Why Anysite
The combination of LinkedIn-depth firmographic queries + flexible mini-DSL + structured filters is unique to the platform. Recreating it elsewhere requires stitching four to six vendors — firmographics, sales-nav search, list-building, enrichment, funding intelligence — into a pipeline with per-record pricing at every layer. For sales teams that want to own their ICP definition instead of inheriting one from a vendor, the SQL-style discovery endpoint is the unlock: write your own scoring rubric, run it against the underlying database in seconds, and pay only for the rows that survive your filter.Key Takeaways
- Own your ICP, don’t inherit it. A structured, SQL-style discovery endpoint lets the team write its own scoring rubric instead of accepting a vendor’s coarse categories.
- One endpoint replaces a four-to-six-vendor stack. Firmographics, sales-nav search, list-building, enrichment, and funding intelligence collapse into a single pipeline.
- Discovery is cheap; precision is the value. 14,000 companies narrow to 180 hand-picked touches — a 2.3% HOT rate, independently confirmed at 99% against an external target list.
- Boutique scale without sales-ops headcount. A sub-10-person team ships 150 hand-picked outbound touches per week — work that would otherwise need a full sales-ops function and a five-figure annual tooling budget.