Cyan graph threads connecting points across tall server shelves like a dependency map of a codebase

Code Intelligence Search

Software & SaaS
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Software & SaaS

Code Intelligence Search

Ordinary vector search cannot reliably answer questions about exact symbols, call hierarchies or architectural relationships in large repositories. RemSoft.Dev engineered a code-intelligence platform for C#, JavaScript and TypeScript codebases around three complementary signals: semantic retrieval over LLM-generated descriptions (Qdrant), exact code search (PostgreSQL full-text) and dependency-graph traversal (Neo4j), fused into one ranked result. Rust components and tuned NVIDIA/AMD drivers accelerate local indexing and model execution; branch-aware incremental indexing keeps the index current on every merge or pull request. Developers and AI agents can search by business meaning, identifiers, call hierarchies and architecture, with cloud or local models. A companion self-learning knowledge base - re-vectorising code changes on every merge - is deployed in several production projects in CPU-only and GPU-accelerated variants.

GraphRAGDeveloper toolingCode searchLLM

Key Challenges

Client: Engineering teams with large codebases

Signals: Vector + full-text + dependency graph

Stack: Qdrant, PostgreSQL, Neo4j, Rust, local & cloud LLMs

Deployment: Several production projects; CPU and GPU editions

The project faced several key challenges:

  • Exactness and meaning together: A symbol search and a 'how does billing work' question need different engines - the platform fuses them instead of choosing.
  • Staying current: Indexes go stale fast; branch-aware incremental indexing on every merge keeps answers trustworthy.
  • Local GPU performance: Rust services and tuned drivers make indexing practical on developers' own NVIDIA or AMD hardware.

Results

  • Hybrid semantic + keyword + graph retrieval
  • Branch-aware incremental indexing
  • Cloud or local model support
  • Deployed in production projects

3 signals

vector, full-text and graph fused into one ranking.

Every merge

triggers incremental re-indexing.

CPU + GPU

editions for lightweight and high-load environments.

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