It took about four months. Correctness was reviewed continuously rather than only after implementation. We built the system incrementally in small slices, with humans handling the architecture, and review while AI performed much of the implementation and front end is fully doe by humans.

The ingestion side includes durable asynchronous processing, recovery and replay, idempotency, strict ordering, structure aware segmentation, parent child relationships, and batched local embedding. The retrieval side includes hybrid (rrf), text and vector search, document versioning, collection and category scoping, document level ACL enforcement, parent expansion, provenance, and citation tracking.

Your estimate is fair. But, reliable WAL and retrieval layer are each substantial projects. We did not build the underlying database query engine from scratch, but the correctness work around ingestion, recovery, hierarchy, security, ranking, and provenance was still significant.

The project now has more than 3,000 automated tests, including coverage for recovery, concurrency, ordering, ACLs, versioning, and retrieval behaviour. Tests are not proof of correctness, but incremental implementation, explicit invariants, failure testing, and continuous human review are how we built confidence in it. We also had script to automate live endpoint tests with real data samples, this help largely to mitigate issues after a refactoring or enhancement. total production code is 80k LOC and rest is test coverages. One thing to say after seeing the results are, many of the RAG framework does it wrong. We got exceptional results and we also have agentic retrieval for answering complex queries. We designed it in our own way because government systems cannot afford false results, especially when the product is intended for crime investigation or compliance management in banks.

Edit: I forgot to mention that we also support PostgreSQL.

After building this system, we concluded that vector dimensionality is only a small part of retrieval accuracy. We use 384 dim embeddings and still achieve excellent results.