AI Recruiting Assistant
Screen, rank, and schedule candidates consistently — without the resume pile.
- Project range
- $5,000–11,000
- AWS running cost
- $40–220/mo
- Time to deploy
- 3–5 weeks
- Best-fit industries
- Staffing, Healthcare
Executive summary
An assistant that ingests every application, extracts structured data from resumes, and scores each candidate against the role's must-haves using transparent, configurable criteria. It answers applicant questions, schedules interviews, and hands recruiters a ranked shortlist — turning days of manual screening into minutes.
Business problem
High-volume roles draw far more applicants than a recruiter can review well. Screening is slow and inconsistent, strong candidates go cold waiting for a reply, and ad-hoc judgment invites bias. Coordinating interviews across calendars adds still more delay.
Architecture
AWS services
Amazon API Gateway
Networking- — Application intake and applicant chat
- — Rate limiting
AWS Lambda
Compute- — Parse, score, and rank candidates
- — Drive interview scheduling
Amazon S3
Storage- — Encrypted resume and document storage
- — Lifecycle retention policies
Amazon Textract
AI / ML- — Extract text and fields from resumes and forms
Amazon Bedrock
AI / ML- — Match candidates to role requirements
- — Applicant Q&A and summary write-ups
Amazon DynamoDB
Database- — Structured candidate records
- — Screening status and audit trail
Amazon EventBridge
Messaging- — Trigger scheduling and status notifications
ATS / calendar adapters
Integration- — Sync to the applicant tracking system
- — Auto-book interviews and send updates
Amazon CloudWatch
Observability- — Logs, metrics, and cost alarms
Data flow
- 1
An applicant submits a resume; it is stored encrypted in S3 and parsed by Textract.
- 2
Bedrock matches the candidate to the role while transparent scoring rules apply the must-haves and weights.
- 3
Candidate records and scores are written to DynamoDB with a full audit trail.
- 4
Applicants can ask questions and self-schedule; strong matches are auto-advanced via EventBridge to the ATS and calendar.
- 5
Recruiters receive a ranked shortlist with concise, evidence-based summaries.
Security considerations
- Applicant PII encrypted at rest and in transit; strict retention and deletion policies.
- Scoring criteria are explicit, weighted, and auditable to support fair, defensible decisions.
- Least-privilege IAM; ATS and calendar credentials in Secrets Manager.
- Configurable exclusion of sensitive attributes from scoring inputs.
Cost considerations
- Textract and Bedrock are the primary variable costs — per page and per candidate.
- S3, DynamoDB, and Lambda are pay-per-use and inexpensive at rest.
- Retention lifecycle rules keep storage costs bounded over time.
Scalability
- Serverless throughout; absorbs hiring surges without provisioning.
- New roles and scorecards are configuration, not code.
- Additional ATS or calendar systems attach as adapters.
Deployment roadmap
Phase 1 — Define the scorecard
Week 1- — Agree on must-haves, weights, and fairness guardrails
- — Provision AWS foundation and connect the ATS
Phase 2 — Build & integrate
Weeks 2–4- — Build parsing, scoring, and scheduling
- — Wire ATS, calendar, and email adapters
Phase 3 — Pilot & calibrate
Week 5- — Run on a live requisition
- — Calibrate scoring against recruiter judgment
Future enhancements
- Structured interview kit generation per role.
- Skills assessments triggered automatically for shortlisted candidates.
- Candidate re-engagement from a silver-medalist talent pool.
- Diversity and funnel analytics into the executive dashboard.