Turn conversational English into terse, token‑efficient context.

How the algorithm works

Our pipeline combines grammar manipulation, deterministic state machines, and machine learning to reshape text: we normalize structure, strip redundancy, and distill dialogue into crisp cues the model can rely on—without throwing away the reasoning trace your application needs.

External LLM calls are used sparingly: they verify intermediate shapes and optimize final wording so compressed prompts stay faithful to the original intent. The heavy lifting stays inside our own transformation stack, which keeps latency low and costs predictable.

What you get

• Fewer input tokens on every request, with clearer anchors for the model.
• Better alignment: terse context elements highlight goals, constraints, and entities explicitly.
• A single HTTP API—integrate once and route traffic through POST /detokenate for the latest revision.