LiyaEngine

Talent AI is LiyaEngine's solution for recruiting teams. It replaces manual resume review with structured AI intelligence that understands careers, competencies, and role fit — not just keyword matching.

Domain pack: hiring
API base path: POST /v1/hiring/{intent} or POST /v1/run with pack: "talent-ai"

What Talent AI does

Capability	What it replaces
Parse and score every resume against a JD	Manual resume review
Surface hidden competencies from career history	Keyword filter screens
Generate ranked shortlists with explainable rationale	Recruiter gut-feel ranking
Generate role-specific knockout and interview questions	Generic question banks
Coach candidates on gaps, career direction, and interview prep	Ad-hoc feedback

Recruiter workflow

The typical recruiter pipeline runs five intents in sequence:

Step 1 — Analyse the job description

POST /v1/hiring/jd-analysis

{
  "input": {
    "user_id": "recruiter_001",
    "job_description": {
      "title": "Staff Engineer",
      "description": "We are looking for a staff-level engineer to lead our platform team...",
      "company_name": "Acme Corp",
      "required_experience_years": 7
    }
  }
}

Returns a structured role spec — required skills, inferred competencies, seniority signals — used as the scoring baseline for every candidate.

Step 2 — Generate knockout screening questions

POST /v1/hiring/knockout-question-generation

{
  "input": {
    "user_id": "recruiter_001",
    "job_description": {
      "title": "Staff Engineer",
      "description": "..."
    }
  }
}

Returns a set of role-specific knockout questions tuned to the seniority level and technical requirements of the role. Use these in your application form or ATS to filter before bulk screening.

Step 3 — Prescreen candidates

POST /v1/hiring/candidate-prescreen

{
  "input": {
    "user_id": "recruiter_001",
    "profile": {
      "name": "Jane Smith",
      "title": "Senior Engineer",
      "years_of_experience": 6,
      "skills": ["TypeScript", "Kubernetes", "Go"],
      "work_experiences": [
        {
          "title": "Senior Engineer",
          "company": "Stripe",
          "start_date": "2020-01-01",
          "end_date": null,
          "achievements": ["Led platform migration reducing deploy time by 60%"]
        }
      ]
    },
    "job_description": {
      "title": "Staff Engineer",
      "description": "...",
      "required_experience_years": 7
    }
  }
}

Returns a multi-dimensional prescreen result:

{
  "output": {
    "overall_score": 84,
    "fit_score": 88,
    "skill_score": 79,
    "experience_score": 81,
    "recommendation": "strong_yes",
    "strengths": ["Deep Kubernetes experience", "Track record of platform-scale impact"],
    "concerns": ["1 year short of required experience"],
    "risk_flags": [],
    "rationale": "Strong technical depth and measurable impact at a comparable scale..."
  }
}

recommendation is one of: strong_yes · yes · maybe · no · strong_no

Step 4 — Score and rank the shortlist

POST /v1/hiring/candidate-scoring

Runs a deeper comparative scoring pass on shortlisted candidates. Returns a ranked list with per-dimension breakdowns. Call this after prescreening to produce the final ranked shortlist your hiring team acts on.

Step 5 — Generate interview questions

POST /v1/hiring/interview-question-generation

{
  "input": {
    "user_id": "recruiter_001",
    "profile": { "name": "Jane Smith", "skills": ["TypeScript", "Kubernetes"], ... },
    "job_description": { "title": "Staff Engineer", ... }
  }
}

Returns a personalised interview question set tuned to the candidate's specific profile — not generic role templates. Questions target skill gaps, probe notable strengths, and cover the exact role requirements.

Candidate-facing workflow

Talent AI also powers candidate-facing features in career and job platforms:

Intent	Use case
`resume-analysis`	"Here's how strong your resume is and what to fix"
`resume-improvement`	"Here are specific edits to make your resume more compelling"
`cover-letter-generation`	"Generate a tailored cover letter for this role"
`intro-script-generation`	"Generate a 60-second intro for this interview"
`job-fit-analysis`	"How well does this role match your background?"
`career-assessment`	"Where are you in your career trajectory?"
`skill-gap-analysis`	"What skills do you need for your target role?"
`career-path-planning`	"What are your realistic next moves from here?"
`career-transition-planning`	"How do you move from X industry to Y?"
`mock-interview`	"Practice the exact questions you'll be asked"
`coaching-session`	"Ongoing career coaching conversation"
`general-chat`	"Free-form career Q&A"

Full intent reference

Intent	Endpoint	Required inputs
`resume-analysis`	`/v1/hiring/resume-analysis`	`resume`
`resume-improvement`	`/v1/hiring/resume-improvement`	`profile`
`cover-letter-generation`	`/v1/hiring/cover-letter-generation`	`profile`, `job_description`
`intro-script-generation`	`/v1/hiring/intro-script-generation`	`profile`, `job_description`
`jd-analysis`	`/v1/hiring/jd-analysis`	`job_description`
`knockout-question-generation`	`/v1/hiring/knockout-question-generation`	`job_description`
`interview-question-generation`	`/v1/hiring/interview-question-generation`	`profile`, `job_description`
`job-fit-analysis`	`/v1/hiring/job-fit-analysis`	`resume`, `job_description`
`candidate-prescreen`	`/v1/hiring/candidate-prescreen`	`profile`, `job_description`
`candidate-scoring`	`/v1/hiring/candidate-scoring`	`profile`, `job_description`
`career-assessment`	`/v1/hiring/career-assessment`	`profile`
`skill-gap-analysis`	`/v1/hiring/skill-gap-analysis`	`profile`
`career-path-planning`	`/v1/hiring/career-path-planning`	`profile`
`career-transition-planning`	`/v1/hiring/career-transition-planning`	`profile`, `career_goals`
`mock-interview`	`/v1/hiring/mock-interview`	`profile`
`coaching-session`	`/v1/hiring/coaching-session`	—
`general-chat`	`/v1/hiring/general-chat`	—

See Hiring Domain for full input/output schemas.

Knowledge sources

Upload your own data to ground Talent AI responses in your company's context:

Source type	Namespace	What to upload
Resume	`hiring:resume`	Candidate CVs for retrieval-augmented scoring
Job Description	`hiring:job_description`	JD library — past and active roles
Policy	`hiring:policy`	Hiring policies, EEOC rules, compensation bands
Profile	`hiring:profile`	Structured candidate profiles

# Upload a hiring policy document
curl -X POST https://api.liyaengine.ai/dashboard/knowledge/upload \
  -H "Authorization: Bearer $JWT_TOKEN" \
  -F "file=@hiring_policy.pdf" \
  -F "domain=hiring" \
  -F "source_type=hiring:policy"

Using the unified run endpoint

You can also call Talent AI via the unified /v1/run endpoint using pack:

POST /v1/run
{
  "pack": "talent-ai",
  "intent": "candidate-prescreen",
  "input": {
    "user_id": "recruiter_001",
    "profile": { ... },
    "job_description": { ... }
  }
}

This is equivalent to POST /v1/hiring/candidate-prescreen and returns the same response shape.

Next steps

All Hiring Intents — full input/output schema reference
Building a RAG Pipeline — upload your resume and JD library
Sessions and Memory — multi-turn coaching and interview flows
Configuration — persona, models, and guardrails

Talent AI

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