01 Where friction lives
What breaks without this.
- ✕RAG systems miss relevant context
- ✕HNSW recall degrades at scale
- ✕Search quality is not measured rigorously
- ✕Retrieval latency budgets are unclear
- ✕Multi-hop agent workflows amplify retrieval errors
- ✕Evaluation pipelines are weak or absent
02 What we implement
The workflow layer we install.
- →RAG architecture review
- →Retrieval quality evaluation
- →Vector search benchmarking
- →Recall & latency profiling
- →Search pipeline implementation
- →High-recall retrieval kernel experimentation
- →Microservice extraction planning
Stack & integrations
pgvectorFAISSHNSWQdrantCohereOpenAI
03 Fixed-scope packages
Choose your starting point.
Retrieval Eval Sprint
Measure search quality rigorously and find the gaps.
- →Eval harness
- →recall@k + latency profile
- →Failure analysis
- →Improvement roadmap
High-Recall Search Sprint
Implement a high-recall retrieval pipeline.
- →Index tuning
- →Re-ranking layer
- →Latency budget
- →Benchmark report
04 Outcomes