Enterprise Document Intelligence Platform

Mohammad Ahmadian — AI / Machine Learning Engineer

Enterprise-Grade Intelligent Document Processing System powered by OCR, NLP and Large Language Models.

Hiring managers: Demo Website · Dashboard · Architecture · Presentation pack

Recruiter snapshot

| Item | Detail | |------|--------| | Domain | Intelligent Document Processing (IDP) | | Capabilities | Ingest · extract · chunk · embed · semantic search · RAG with citations · field extraction | | Target roles | Applied AI Engineer · LLM Engineer | | Serving | FastAPI /v1/ingest · /v1/search · /v1/ask · /v1/extract | | Integrity | Citations required · sample corpus · extractive default (optional LLM) |

Evaluation (sample corpus)

| Metric | Value | |--------|------:| | Retrieval hit-rate @5 | 1.000 | | Citation coverage | 1.000 | | Faithfulness checklist | 1.000 |

Full report: reports/EVALUATION_REPORT.md

Architecture

System architecture (Released)

Figure 1. System architecture (Released).

Quick start

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

set PYTHONPATH=src
python scripts/build_index_and_eval.py
uvicorn docintel.api.main:app --app-dir src --host 0.0.0.0 --port 8000
# http://localhost:8000/docs

Example ask

curl -s -X POST http://localhost:8000/v1/ask ^
  -H "Content-Type: application/json" ^
  -d "{\"question\":\"What is the warranty period?\"}"

Documentation

docs/00_MASTER_INDEX.md · Status: docs/status.md

Doc 13 is Document Processing Pipeline (IDP-specific; same numbering DNA as Project 1).

Limitations (honest)

Public/sample corpus — not a customer production archive
Default answers are extractive unless LLM_API_KEY is set
Local vector store for demo/CI; Postgres/pgvector path documented for enterprise Compose

Contact

mohammad.ahmadian.dev@gmail.com · github.com/ahmadian-dev · Turkey (GMT+3)

License

MIT — see LICENSE.

Source documentation