Existing retrieval
Start with the customer's current vector, BM25, hybrid, or reranked top-k context.
Technology
From retrieved context to compact evidence packet.
Trajbl sits after retrieval and before generation. It is CPU-first, model-free, and designed to reduce LLM context without hiding the source evidence.
Optional fusion
Use hybrid RRF or BGE reranking when the stack already has it. Trajbl does not need to replace retrieval.
Evidence packing
Score and keep complete source sentences that answer the question inside a compact budget.
LLM-ready packet
Send a smaller, readable context packet to the QA model, agent, or knowledge assistant.
System flow
Each layer has one clear job.
InputUser query
RetrieveVector / hybrid / BGE
CompressTrajbl
GenerateLLM answer
Deployment model
Built as a local preprocessing layer.
Trajbl can run before the LLM call in a private RAG or agent service. The next engineering milestone is a production API, infra benchmark, and integration package for real customer workflows.
Discuss deploymentCPU-first compression
No GPU dependency and no hosted compression model required for the benchmarked path.
Private integration
Run compression inside the customer's app, service, or controlled infrastructure boundary.
Auditable context
Keep complete source sentences visible for inspection, debugging, and failure analysis.