01 — Problem
The friction
Recruiters waste cycles on mismatched resumes and thin interview briefs.
TalentScout
TalentScout screens at fleet scale without losing judgment. It ranks profiles against role criteria, surfaces fit signals, and hands interviewers a brief that actually prepares them.
- Screening time
- −70%
- Interview prep
- 5× faster
- Fit precision
- +24%
Demo
See it in motion
A short walkthrough of the agent loop — from signal in to decision out.
Demo reel
Role shortlist & briefing
Overview
Case at a glance
Target users: Talent acquisition, hiring managers, and interview panels.
02 — Solution
The approach
A recruiting agent that scores candidates against explicit role criteria, drafts interviewer briefs, and keeps humans in the loop for every decision.
03 — Value
Why it matters
Faster shortlists with clearer fit rationale and far less resume fatigue.
How it works
The agent loop
From raw signal to a decision TalentScout can defend.
- 01
Parse
Normalizes resumes and profiles into structured skills, seniority, and project evidence.
- 02
Score
Evaluates each candidate against must-haves and nice-to-haves taken straight from the role brief.
- 03
Compare
Ranks the pool, explains why one profile sits above another, and marks near-misses worth a screen.
- 04
Brief
Writes the interview brief: strengths, risks, and the questions that would resolve the open doubts.
Capabilities
What TalentScout actually does
Built for Talent acquisition, hiring managers, and interview panels.
- 01
Criteria-aware ranking
Scores profiles against must-haves, nice-to-haves, and role-specific signals.
- 02
Interview briefs
Prepares panels with strengths, risks, and suggested probe questions.
- 03
Evidence-linked review
Points to the exact resume passage behind each score instead of asserting a verdict.
- 04
Pipeline digest
Weekly summaries of funnel health and emerging talent patterns.
Architecture
How the system thinks
Data flows left to right: sources feed the orchestrator, specialists reason in parallel, tools execute, and one artifact comes out.
Sources
- DataATS & resumes
- DataRole criteria
- DataInterview history
Orchestration
- OrchestratorRecruit orchestrator
- LLMReasoning LLM
Specialists
- AgentScreening agent
- AgentBriefing agent
Tools & output
- ToolEmbeddings
- ToolRank engine
- OutputShortlist pack
- Data
- Orchestrator
- LLM
- Agent
- Tool
- Output
In action
A real exchange
An abbreviated conversation, close to what the agent actually returns.
TalentScout Session active
- You
Shortlist for the senior data engineer role.
- TalentScout
Eight of 143 clear every must-have. The top three all have production streaming experience. Two more are close but light on orchestration — worth a screen if you widen scope.
- You
Why is candidate 2 ranked above candidate 1?
- TalentScout
Same core skills, but candidate 2 led a two-year platform migration, which maps to the "own the roadmap" requirement. The evidence is in their project section, not their summary.
Design decisions
Trade-offs worth naming
The constraints that shaped the system — and what was deliberately left out.
- 01
Criteria are explicit
Ranking weights come from the role brief rather than model intuition, so hiring managers can argue with them and change them.
- 02
Evidence-linked scores
Each score cites the passage it came from. Reviewers can disagree with the reading instead of the number.
- 03
No auto-rejection
The agent ranks and explains; rejecting a candidate remains a human decision by design.
Honest limits
Where it stops, where it goes
What the agent does not handle today, and what comes next.
Current limitations
- Resume text only — portfolios and code samples are not assessed yet.
- Scores reflect what candidates wrote, not what they can do.
- Needs a well-written role brief to perform well.
On the roadmap
- Structured scorecards synced back to the ATS
- Portfolio and repository review
- Calibration against past hire outcomes
Stack
Technology
Nothing added without a concrete requirement behind it.
PythonFastAPIOpenAIPineconeNuxt