Candidates sits alone in Greenhouse.
Judging train ai models with ats data also takes application, and that never shares a screen with Greenhouse.
Build a Train AI Models With ATS Data Agent withGreenhouse and Ashby
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.
Train AI models with ATS data runs on candidate data split across 2 tools.
The hiring decision pays for it.
The hiring decision pays for it.
Ashby tells a different story.
What Ashby knows about application rarely flows back to Greenhouse. Two tools, one unreconciled gap.
Ashby catches it quietly.
Interview data surfaces in Ashby ahead of time, but that tab is closed during train ai models with ats data.
Under The Hood
No exports. Train ai models with ats data reads Greenhouse and Ashby in a single pass. Already connected.
01
Fetch efficiently train data models using candidate from Greenhouse (applicant tracker)
Candidates
02
Fetch application from Ashby (applicant tracker)
applications
03
output
Agent-ready output
The readout for the hiring decision: Efficiently train data models using candidate, application, and interview data, riskiest items surfaced and owned.
The Context Store
Candidates and the rest of Greenhouse + Ashby, already one record.
Before the prompt runs, the Context Store has matched candidates, applications, interviews, scorecards, job postings from Greenhouse + Ashby onto one candidate record. Train ai models with ats data 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.
SHARED KEY2 SOURCESONE VIEWLIVE READS
The Prompt
Copy. Paste.
a Train AI Models
Two steps. Your data, your results, under 60 seconds.
01install
Connect the Airbyte Agent MCP02copy and run
Build me a train ai models with ats data: read Greenhouse and Ashby and hand back one readout.
SETUP
You have the Agent MCP, wiring up 2+ tools you can query in plain language.
WORKFLOW
link Greenhouse and Ashby, query candidates, applications, interviews, scorecards, job postings, fold it onto the candidate, then rank. If a connector is missing, follow the prompt. A one-time browser auth.
TASK
Efficiently train data models using candidate, application, and interview data. Deliver a readout I can paste into the hiring decision. Ranked, sourced, one action per item.The Outcome
The hiring decision that needed 2 hours now finishes while you read this. Now your agent can fix it.
10x
Faster
10x. 2 hours to efficiently train data models using candidate becomes one run of train ai models with ats data.
90%
Cheaper to run
~90% cheaper: Train AI models with ATS data reuses the 2 connectors you already pay for.
2 -> 1
Tools, one query
2 sources, 1 prompt: Greenhouse and Ashby reconciled before train ai models with ats data runs.
Based on internal benchmarks comparing Context Store queries to sequential API calls across equivalent datasets.
01 · Output
Risk-scored output
Train AI models with ATS data ranks each candidate by risk, not by name. The top of the list is where to start.
02 · Signal
Reality-check flags
When your applicant tracker and Greenhouse disagree on efficiently train data models using candidate, the gap is flagged. Not averaged into a guess.
03 · Context
The why, attached
Each line carries its evidence. Application pulled from Ashby. Right where you read it.
04 · Action
One move per line
For each candidate, train ai models with ats data names the next step. What to change and who owns it. Not just a number.
05 · Brief
Paste-ready output
A readout you can drop into the hiring decision: ranked, sourced from Greenhouse and Ashby, scoped to candidates.
Keep going. More recruiting agents from here
view All use casesHiring 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.
Candidate journey for recruiters
Your hiring decision is only as fresh as the slowest tab. Recruiting analytics; identify pipeline bottlenecks. Yet the inputs sit split across Salesforce / Greenhouse / HubSpot.
Candidate journey for candidates
Right now the hiring decision means stitching Greenhouse / Gmail / Ashby by hand. Candidate experience portals show application status, so the work lands late and half-blind.
Common questions
Didn't find your answer? Please don't hesitate to reach out.
Can I tweak what Train AI models with ATS data returns?
Edit the TASK line. Change the ranking, the readout format, or which of Greenhouse and Ashby it leans on.
How fresh is the candidate data Train AI models with ATS data uses?
Live, it reads Greenhouse at query time, so the readout shows candidates as of now, not last night.
Is candidates stored anywhere by Train AI models with ATS data?
No, train ai models with ats data reads candidates, applications, interviews, scorecards, job postings live through the connectors and returns the readout; nothing persists outside Greenhouse and Ashby.
What does Train AI models with ATS data cost to run?
It rides the 2 connectors you already license. No seats, no glue code, no infra to efficiently train data models using candidate.
Train AI models with ATS data is one prompt away from Greenhouse + Ashby.
Wire Greenhouse and Ashby and 47+ sources into the Airbyte Agent MCP and build train ai models with ats data on data you already own.
