Project Gallery

AI Product Case Studies With Interview-Ready Structure.

Each project follows the same pattern: Problem, Architecture, Evaluation, and Deployment proof. This keeps technical review fast and comparable across roles.

6 Projects RAG + Agents + SaaS FastAPI · n8n · SQL · Evaluation

Featured Project · Product + Platform

SaaS Featured

Mini SaaS: RAG for Teams

Full multi-tenant AI product with auth, team boundaries, billing flow, usage limits, and audit trails.

Demonstrates ownership of the product shell around LLM features: authentication, tenancy, billing, and operations.

Auth + Org Billing Flow Rate Limits Audit Logs

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def process_team_query(env: RequestEnvelope) -> dict[str, Any]:
    ctx = platform.authorize(env.token, env.request_id)
    policy = plans.get(ctx.org.tier)

    platform.assert_org_state(ctx.org)
    platform.assert_feature_gate(ctx, env.endpoint, policy)
    platform.assert_safety(env.query, ctx.org)

    answer = rag.answer(
        org_id=ctx.org.org_id,
        query=env.query,
        top_k=top_k,
    )

    return build_response(answer)

RAG Production

DocChat RAG (Production)

Upload PDF/docs, chunk and embed content, then answer with citations and conversation memory.

Evidence tracked in deployment: citation checks, fallback-rate monitoring, and p95 latency trends.

Citations Session History Grounded Answers

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Evaluation Regression

RAG Evaluation Lab

Automated scoring for faithfulness, accuracy, and regression safety after every update.

Release gates enforce measurable baselines: faithfulness >= 0.88 and p95 latency <= 1900ms.

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Automation n8n + API

Customer Support AI Triage

Routes incoming email/DM, drafts replies, creates tickets, and keeps human approval in the loop.

Measured for operational impact: faster triage with approval checkpoints for quality control.

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Hybrid Agent SQL + RAG

SQL + Docs Hybrid Agent

Chooses SQL for numeric facts and docs retrieval for policies, then merges both safely.

Evaluated on routing precision and correctness across mixed numeric and policy queries.

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Pipeline Ingestion

Knowledge Base Builder

Ingests PDF/URL/markdown, normalizes content, versions changes, and re-indexes only what is needed.

Measured for maintenance efficiency: incremental indexing reduces full reprocessing overhead.

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Review Flow

Consistent Format Across Every Case Study

Problem Framing

Use-case, constraints, assumptions, and the KPI that defines success.

Architecture Plan

Data flow, model-tool orchestration, contracts, and failure boundaries.

Validation

Accuracy and reliability checks, regression guards, and measurable evidence.

Delivery

Operational strategy, ownership tradeoffs, and production-readiness notes.

Designed so an interviewer can scan methodology quickly before diving into the full case page.