Personal project · pre-launch

ApplyWise: CV Tailoring Where Every Line Cites a Fact

A web app, not launched yet, that breaks a CV into small editable facts, matches them to a job description, and writes a tailored CV and cover letter where each line cites the facts behind it. A verifier (rules first, then an LLM judge) strips anything unsupported. There is also a free ATS checker, an application tracker and a Chrome extension.

ApplyWise showcase on GitHub Contact Janindu Gayanga

The problem

Tailoring a CV by hand takes me 30 to 60 minutes per job, and generic AI tools happily add skills the candidate doesn't have.
Applicant tracking systems quietly drop CVs that use columns, tables or images.
A CV is personal data, so it has to be encrypted, exportable and deletable.

Architecture and key decisions

Next.js 15 web app, a NestJS 11 API with Prisma 6, and a Go service that parses PDFs, produces the ATS report and renders an ATS-safe single-column PDF.
Clean architecture by convention: the domain classes (fact extractor, match scorer, hybrid retriever, generator, verifier) import nothing from Nest or Prisma, and controllers and repositories are thin adapters.
One LLM interface with Anthropic, Gemini and Ollama adapters plus a deterministic fake. The real adapters are unit-tested against mocks and haven't been run against a live model yet. Structured outputs, a versioned prompt registry with fingerprints, and a per-user cost ledger with a monthly budget.
Hybrid retrieval with pgvector (HNSW) and Postgres full-text search, fused with Reciprocal Rank Fusion. The vectors come from a deterministic 512-dimension hashing embedder, so there is no embedding vendor to pay.
Personal data is encrypted with AES-256-GCM before it reaches Postgres, emails are looked up through an HMAC blind index, and there are export and delete endpoints. Google sign-in uses OIDC with PKCE, state and nonce.
A BullMQ worker classifies application emails you upload or paste (.eml) and moves the card on the kanban tracker. There is no inbox integration. Plans and usage metering sit behind a payment port (only a fake provider so far), and quota is reserved under a Postgres advisory lock.
Terraform for AWS in ap-south-1: eight modules (KMS, S3, SQS, Secrets Manager, IAM, ECR, network, ECS Fargate + RDS), with least-privilege IAM checked by terraform tests.

Results

An eval gate of 30 golden CV and job-description pairs runs in CI and fails the build if 1% or more of shipped lines are fabricated. It runs on a deterministic fake model that plants three fabrications in every pair.
Latest report: the verifier removed all 90 planted claims, and 0 of 570 shipped claims were fabricated. A run on a real model comes next, once I have a paid API key.
Six parallel tailorings on a free plan give exactly 3 successes and 3 HTTP 402s, on real Postgres.
S3, SQS, Secrets Manager, KMS and IAM were applied, checked and destroyed on LocalStack. ECS, ECR, RDS and the VPC are planned only.
Go tests write golden JSON that a shared zod schema checks on the TypeScript side, and the API validates every live parser response.

Tech stack

TypeScript
Next.js 15
NestJS 11
Prisma
PostgreSQL
pgvector
Redis
BullMQ
Go
LLM evals
Anthropic / Gemini / Ollama adapters
OIDC
Chrome Extension
Terraform
AWS (LocalStack)
Playwright
Jest

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