Greenhouse is a step behind.
Fields in Greenhouse move whenever someone logs them; to analyze trends you need candidates fresher than that.
Build a Candidate Matchmaking Agent withGreenhouse and Ashby
Recruiting teams run hiring decisions on stale, scattered data: Ashby / Greenhouse each hold a piece, none hold the whole. AI recruiting tools match candidates to best-fit roles.
Two tools, two tabs, and nothing reconciles candidates.
Now your agent can fix it.
Now your agent can fix it.
Ashby holds what Greenhouse misses.
Traits lives in Ashby, cut off from candidates, so candidate matchmaking guesses at the link.
Ashby catches it quietly.
Match candidates to jobs based on algorithm surfaces in Ashby ahead of time, but that tab is closed during candidate matchmaking.
Under The Hood
Ask once. Candidate matchmaking reads Greenhouse and Ashby for you. Already connected.
01
Check analyze trends from Greenhouse (applicant tracker)
Candidates
02
Fetch traits from Ashby (applicant tracker)
jobs
03
output
Agent-ready output
One rundown: Analyze trends and traits, match candidates to jobs based on algorithm. Ranked by priority, top risks flagged, a next step on each.
The Context Store
No glue between Ashby / Greenhouse: the candidate is stitched before the prompt fires.
Before the prompt runs, the Context Store has matched candidates, jobs, skills, experience, success patterns from Ashby / Greenhouse onto one candidate record. Candidate matchmaking just reads it, no ID-stitching.
Your agent queries one surface instead of three APIs. Faster responses, lower cost per query, and results that work because the relationships were built before you asked the question.
CANDIDATE-LEVEL JOINSRECRUITING SCHEMANO GLUE CODE
The Prompt
Copy. Paste.
a Candidate Matchmaking Agent
Two steps. Your data, your results, under 60 seconds.
01install
Connect the Airbyte Agent MCP02copy and run
Build me a candidate matchmaking: read Greenhouse and Ashby and hand back one rundown.
SETUP
The Airbyte MCP layer is connected to 2+ systems; query them directly, no API code.
WORKFLOW
link Greenhouse and Ashby, query candidates, jobs, skills, experience, success patterns, fold it onto the candidate, then rank. If a connector is missing, follow the prompt. One quick authorize step.
TASK
Analyze trends and traits, match candidates to jobs based on algorithm and surface the rundown: highest-risk candidates first, each with a recommended next step.The Outcome
The hiring decision that needed 2 hours now finishes while you read this. Now your agent can fix it.
10x
Faster
10x speed: candidate matchmaking turns a 2-hour hiring decision into under a minute.
90%
Cheaper to run
90% less spend: no glue code; it runs on your existing 2-tool stack to analyze trends.
2 -> 1
Tools, one query
2 tabs into 1: Greenhouse and Ashby collapse to one view to analyze trends.
Based on internal benchmarks comparing Context Store queries to sequential API calls across equivalent datasets.
01 · Output
Priority scoring
Every candidate scored 1-10, so candidate matchmaking surfaces what needs you first instead of an alphabetized list.
02 · Signal
Where the tools disagree
Any conflict between Greenhouse and your applicant tracker on candidates is raised for review rather than silently smoothed over.
03 · Context
Context overlay
Each line carries its evidence. Traits pulled from Ashby. Right where you read it.
04 · Action
Next action per item
Candidate matchmaking closes each candidate with a recommendation. The owner and the move. Ready to run.
05 · Brief
Paste-ready output
Hand the rundown straight to the hiring decision. Every figure traces back to Greenhouse and Ashby.
More recruiting agents like Candidate matchmaking
view All use casesSurface screening questions
Your hiring decision is only as fresh as the slowest tab. Job boards display complete job details. Yet the inputs sit split across Ashby and Greenhouse.
Train AI models with ATS data
Recruiting teams run hiring decisions on stale, scattered data: Greenhouse + Ashby each hold a piece, none hold the whole. Predictive hiring models; improve screening accuracy.
Hiring Debrief Synthesizer
The data for your hiring decision already exists in Slack + Granola + Greenhouse. The problem is no one view joins it. Debrief decisions need to reach the ATS immediately to trigger next steps.
Common questions
Didn't find your answer? Please don't hesitate to reach out.
Does Candidate matchmaking replace Greenhouse?
No, it reads Greenhouse and writes back the rundown. Your record systems stay put.
Which clients run candidate matchmaking?
Claude and other MCP-aware agents. Each points at the same Greenhouse and Ashby connectors candidate matchmaking uses.
How fresh is the candidate data Candidate matchmaking uses?
Live, it reads Greenhouse at query time, so the rundown shows candidates as of now, not last night.
What if a candidate shows up in two of Greenhouse and Ashby?
The shared key de-dupes it. Candidate matchmaking keeps one candidate with candidates merged across sources.
Stop tab-switching to analyze trends. Let the agent read Ashby / Greenhouse.
Connect Greenhouse and Ashby (plus 47+ more) and ship candidate matchmaking today to analyze trends.
