Unwrapped

Teardown · seekout

SEEKOUT

SEEKOUT

CategoryAI RecruitingValuation · $1.2B · 2022Site ↗
  • Tiger Global

LinkedIn public profiles + customer ATS files + LLM APIs + screening workflow.

01

Public data / API layer

LinkedIn public profiles
LinkedIn public profilesScraped
GitHub public events
GitHub public eventsPublic
Apollo.io API
Apollo.io APIAPI
Customer ATS data
Customer ATS dataYours
Inbound applicant records
Inbound applicant recordsYours

Internal replication score

Easy
0.71

Feasibility of a useful internal substitute built with Claude (or similar), the same data access, and light agent logic — not rebuilding the whole product.

IRS = 0.30·D + 0.25·L + 0.20·O + 0.15·R + 0.10·Sthis record · 71%
  • D

    Data accessibility

    weight 0.300.75
    • 1.0mostly customer-owned / public / standard third-party sources
    • 0.5mixed accessibility
    • 0.0hard-to-access or proprietary source layer
  • L

    LLM substitutability

    weight 0.250.80
    • 1.0mostly retrieve / prompt / cite / summarize / classify / compare
    • 0.5mixed standard + custom behavior
    • 0.0strongly custom model behavior (fine-tunes on proprietary data, etc.)
  • O

    Output simplicity

    weight 0.200.70
    • 1.0straightforward internal work product (memo, list, reply, SQL query)
    • 0.5moderately specialized
    • 0.0highly specialized (e.g. FDA-graded clinical text)
  • R

    Review / risk tolerance

    weight 0.150.65
    • 1.0internal use with human review is acceptable
    • 0.5moderate risk
    • 0.0very low tolerance for error (e.g. external legal filings)
  • S

    Surface complexity

    weight 0.10inverse — higher means less surface dependence0.50
    • 1.0a simple internal shell is enough
    • 0.5polished workflow matters somewhat
    • 0.0product surface / rollout / trust posture is central to value
LabelsEasy ≥ 0.67Medium ≥ 0.34Hard < 0.34

Missing factor rows use heuristics from wrapper scores. Editorial heuristic, not investment advice.

Build it yourself

Recreate the workflow inside your org.

Internal build

Build it yourself

Same LinkedIn/GitHub/Apollo access + Claude with custom rubric + ATS connector — loses pre-built recruiter workflows and compliance guardrails.

Internal use only. Replacing them in-market is a different bar than replaying the useful workflow inside your org.

01 · Connectors & flow

LinkedIn public profiles
LinkedIn public profiles
GitHub public events
GitHub public events
Apollo.io API
Apollo.io API
Customer ATS data
Customer ATS data
Inbound applicant records
Inbound applicant records

Internal build map

Data in

Connectors
Connectors

Agent layer

Planner
Tools + retrieval
Reasoning model

Logic

LLM API
retrieve
score
rank
screen
generate outreach
not custom weights

Outputs

Internal search
Answer
Citations

02 · Claude / agent prompt

Paste as the system or developer message in Claude (or your agent runtime). Scroll to read; Copy grabs the full text.

Claude / agent prompt

// Internal recruiting agent You are a candidate screening agent inside [YOUR_COMPANY] recruiting operations. You help talent acquisition teams evaluate candidates using ONLY materials the candidate has provided or that are publicly accessible: resumes, LinkedIn profiles, GitHub activity, application responses, and internal ATS records. ## What you must do 1. Retrieve first: Pull candidate records from ATS, enrich with LinkedIn/GitHub public data if available 2. Score against rubric: Evaluate every candidate against the specific role requirements provided by the recruiter 3. Cite rigorously: Every evaluation claim must reference specific resume bullets, work history, or public profile evidence 4. Surface conflicts: Flag when a candidate's stated experience conflicts with public records or when key requirements are unverifiable 5. Generate evidence packets: For shortlisted candidates, produce a transcript-style summary with direct quotes and sourced claims ## What you are not Not a replacement for recruiter judgment or hiring manager interviews. All AI-generated scores and summaries require human review before a candidate is contacted or rejected. Internal use only — never share raw scores or AI-generated summaries with candidates. ## Refusal Refuse to score candidates when the rubric includes protected characteristics (age, race, gender, etc.). Refuse to generate outreach that makes claims about company culture or specific team dynamics you cannot verify. When a role requirement is ambiguous, ask the recruiter to clarify before scoring. ## Safety All candidate evaluation happens inside [YOUR_COMPANY] systems. AI-generated outreach must be reviewed by a recruiter before sending. Screening transcripts are for internal decision-making only and must not be shared externally. EEOC and OFCCP compliance requires human oversight of all AI-assisted hiring decisions.

03 · Result

Which candidates in our ATS from the last 6 months match this senior backend engineer role requiring Rust experience and distributed systems work?
customer-ats

3 candidates match: two with Rust + Kafka at prior employers, one with Rust open-source contributions. Evidence packets attached.