Product Management — Ummidvar Job Agent
Discover → score → tailor → apply → track → sponsor — product definition for an autonomous job agent.
0
Packages Scoped & Shipped
0
Acceptance Tests Defined
0+
Job Boards in Scope
Tools & Methods
The Challenge
Job searching at scale means applying to dozens of roles with individually tailored materials — hours per application done properly. The PM challenge was defining a product scope that served multiple distinct personas (recent graduates, senior professionals, international job seekers needing visa sponsorship, India-first candidates on Naukri) without over-engineering for edge cases in V1. The anti-hallucination requirement — ensuring generated cover letters never fabricated experience — had to be treated as a non-negotiable product constraint, not an engineering nicety. This meant the Facts Graph wasn't optional scope: it was an acceptance criterion. Feature sequencing across 11 modular packages had to avoid cross-package dependencies that would block parallel development tracks.
Product Requirements Document
PRODUCT REQUIREMENTS DOCUMENT
Ummidvar — Autonomous Job Application Agent
Varun Cumbanungam · AI Product Manager · 2024
Doc ID
UMD-PRD-V1
Status
Approved
Owner
Varun C.
Date
2024
Version
1.0
Problem Statement
Job searching at scale requires hours per application. Ummidvar must automate the full loop — discover, score, tailor, apply, track — without fabricating claims, across 7+ job boards.
Primary Users
Recent Graduate
Volume apps, Naukri + LinkedIn, India-first
Senior Professional
Quality-filtered, explainable scoring
International Seeker
Visa sponsorship filter, 5-country registry
Multi-Profile User
Parallel search across résumé profiles
Core Packages
- discovery — 7+ board aggregation, deduplication
- scoring — 100pt composite: skills, title, location, salary
- tailoring — Facts Graph anti-hallucination constraint
- adapters — Playwright ATS: Greenhouse, Lever, Workday
- replies — Gmail classifier: offer/interview/rejection/ghost
- sponsorship — 5 govt registries (UK, AU, NZ, CA, EU)
Key Acceptance Criteria
- AC-1Facts Graph: zero fabricated claims in cover letters
- AC-2Every score explainable with component breakdown
- AC-3Deduplication removes >95% cross-board duplicates
- AC-4CAPTCHA → HITL hand-off, state snapshotted
- AC-5Reply classifier >90% accuracy on labelled set
- AC-6Sponsorship registries refreshed weekly via CI
Risk Assessment
- HIGH
LLM fabricates experience — reputation damage
Facts Graph → structural prevention, HITL review
- HIGH
ATS bot detection — account ban
HITL queue, human-like delays, no bulk mode
- MED
Job board scraping block
Multi-provider fallback + rate limiting
- LOW
Multi-profile context collision
Profile ID namespacing at all boundaries
Product Artefacts Delivered
- PRD V1 — personas, package scope, ACs, risk
- 11-package architecture spec — domain model
- 286 acceptance tests — full coverage spec
- Anti-hallucination spec — Facts Graph overview
CONFIDENTIAL · Ummidvar PRD
PRD · Architecture Spec · 286 Acceptance Tests · Anti-Hallucination Spec
Full PRD and supporting artefacts available upon request
Results
11-package modular architecture shipped with 286 acceptance tests covering every major component. Seven job boards integrated in the discovery layer. Five government visa sponsorship registries (UK, AU, NZ, CA, EU) integrated and refreshed weekly via CI. The Facts Graph anti-hallucination constraint was met: every generated claim is grounded in an extracted fact before LLM generation, structurally preventing fabrication. Apache-2.0 licensed and Docker-deployable in a single command — designed for future hosted-tier expansion without re-architecture.
Gallery & Demos
Click any image or video to expand · ← → keys navigate
Interested in this work?
Full architecture walkthrough and code review available during interviews.