Agent frameworks
Shorten task instructions, planning notes, memory snippets, and tool context before an agent sends work to a premium model.
Put prompt compression where token spend quietly compounds.
PlanFerret fits best when your app sends many repetitive, verbose, or machine-generated prompts to downstream LLMs. Compress first, then pass the leaner query into your model call.
RAG and chatbots
Reduce user questions and repeated application boilerplate before retrieval, reranking, or final answer generation.
Support automation
Compress ticket text, routing prompts, and conversation summaries in high-volume helpdesk and customer success workflows.
Batch enrichment
Lower the input cost of classification, tagging, extraction, summarization, and moderation jobs that run across many records.
Internal copilots
Route prompts from Slack bots, CRM assistants, browser extensions, and operations tools through a single low-latency compression step.
Prompt caching and memory
Store compact versions of reusable instructions, user preferences, and conversation summaries so long-running assistants can replay context without dragging every token forward.
How customers integrate
Add PlanFerret as middleware before your LLM provider: send the original prompt to POST /detokenate, then use the
returned q
as the prompt for OpenAI, Anthropic, or any model API.
curl https://planferret.com/detokenate \
-H "Authorization: Bearer pf_live_..." \
-H "Content-Type: application/json" \
-d '{"q":"Please summarize this long support conversation..."}'Use PlanFerret when cost and throughput matter.