Enterprise knowledge chat
Compress retrieved policies, project notes, tickets, and internal documentation before they reach the LLM.
Existing retrievalTrajbl compresses
Use cases
Compress the context your AI stack already retrieves.
Trajbl is designed as a practical plugin layer: existing RAG or agent retrieval finds candidates, then Trajbl packs a smaller evidence packet before generation.
Agent memory
Keep long-running agent context smaller while preserving the evidence sentences needed for the next action.
Existing retrievalTrajbl compresses
Cost-efficient RAG
Add Trajbl after vector, BM25, hybrid, or reranked top-k retrieval to reduce context load without replacing the current stack.
Existing retrievalTrajbl compresses
Legal and compliance
Build smaller answer packets that keep complete source sentences instead of fragmenting key clauses.
Existing retrievalTrajbl compresses
Research workflows
Reduce dense technical context while retaining numbers, dates, entities, and traceable evidence.
Existing retrievalTrajbl compresses
Customer support
Send fewer help-center, account, and policy snippets to the model while keeping answerable context.
Existing retrievalTrajbl compresses
Local and edge AI
Run CPU-first compression where a GPU-backed compression model is not practical.
Existing retrievalTrajbl compresses
Private AI systems
Keep retrieval and compression inside a controlled customer or VPS environment.
Existing retrievalTrajbl compresses
Pilot path
Measure Trajbl on a real knowledge workload.
A practical pilot should compare raw top-k context, LLMLingua2 compression, and Trajbl compression on the same retrieved inputs, with accuracy, token savings, latency, and failure cases logged.