Language, reduced to its semantic core.
CLM extracts structured meaning from system prompts, conversation threads, and data catalogs — in sub-millisecond time, without a model in the loop. Run it standalone as a compression layer, or place it in front of any LLM to cut what you send it by up to 85%.
Best-case reduction on a single well-structured transcript reaches 85%+ — that's the ceiling. 27% is the number that survives contact with everything else: short threads, mixed languages, malformed input, edge cases nobody optimizes for. It's the figure we'd defend in a review.
One compression pass, seven downstream reads.
Each entry reads off the same compressed output. Pair any of them with an LLM if you want one — none of them need it to run.
Every thread reduces to a QA-ready field set
Resolution state, sentiment, agent actions, and commitments come out as structured fields the instant a thread closes — plug your own rubric or model on top.
Catch commitments before they slip
CLM surfaces explicit commitments ("demo by Friday", "proposal in 48h", "refund in 3–5 days") as structured fields you can track and alert on — support, sales, or ops.
Intent, stage, and language — extracted in microseconds
Structured fields for intent, stage, language, and sentiment are ready before any downstream system touches the thread — combine them into your own routing logic.
Aggregate trends across millions of threads
Because every output is a structured dict, grouping by intent, domain, or sentiment is a single SQL query — no embeddings, no clustering.
Auto-populate fields from every interaction
Structured output maps directly to your own schemas — domain, service, trigger, context entities — no brittle regex or form-fills required.
Structured fields for compliance review
CLM extracts PII artefacts, commitments, and conversation state as structured fields — giving compliance teams a clean, reviewable feed instead of raw transcripts, whether the source is a support call, sales negotiation, or advisory session.
Surface recurring issues across any process
Group by trigger + domain to find what comes up most — then generate runbooks, FAQs, or self-serve docs from real interaction data.
One framework, three encoders.
CLM compresses the three content types that drive most LLM cost: system prompts, conversation threads, and structured catalogs.
Every compression is checked, not assumed.
The CLM quality gate runs three weighted entropy analyses on every compressed output to certify semantic losslessness before it reaches your LLM.
gate = CompressionQualityGate()
report = gate.analyze(
original=encoded.original,
compressed=encoded.compressed,
structured=encoded.to_dict(),
)
# → Verdict: ACCEPTABLE
# → Confidence: 91.5%
From natural language to compressed tokens, one call.
import clm client = clm.Client(api_key="YOUR_API_KEY") result = client.encode( text="You are a customer service AI. Analyze " "the transcript and extract the main issue, " "sentiment, and agent actions. Return JSON.", data_type="SYSTEM_PROMPT" ) print(result.compressed) # [REQ:ANALYZE>EXTRACT] [TARGET:TRANSCRIPT] # [EXTRACT:ISSUE+SENTIMENT+AGENT_ACTIONS] # [OUT:JSON] print(f"Reduced {result.n_tokens} -> {result.c_tokens} tokens") # Reduced 148 -> 19 tokens (87% reduction)