Decision & Action Models (JEV, CLM)
When the Job Is a Decision or an Action, Don't Use a Text Generator
LLMs write answers one word at a time, which is the wrong tool when your software only needs a label, a route or the next action. JEV (TypeSafe AI) returns structured, scored decisions in one parallel pass. CLM (Stanford, NVIDIA) picks the best action without generating any text. We decide which tasks need an LLM, then benchmark and integrate the rest on your data. Speed and cost figures are as reported by the developers.
Structured + confidence
JEV output
Up to 200x faster
JEV speed (reported)
Up to 400x cheaper
JEV cost (reported)
Up to 9x faster than JEV
CLM speed (reported)
Use Cases by Industry
Instant fraud flags and transaction routing returned as structured data with a confidence score
Support ticket classification and routing across thousands of requests in parallel
Document and claim triage that feeds directly into the next system, with no text to parse
Content and application screening where every decision must carry a score and be auditable
Agents that select the next UI or tool action quickly, without generating text first
How It Works
Task Audit
Sort your AI tasks into generate, decide and act, and find where an LLM is being used only to produce a label or a route.
Benchmark on Your Data
Compare LLM, JEV-style and CLM-style approaches for accuracy, latency and cost on your real cases.
Integrate
Wire decision outputs into your systems as clean structured data, with thresholds that send low-confidence cases to a person.
Monitor
Track accuracy and confidence calibration over time and re-tune when data drifts.
BringUsOneWorkflowThatNeedsFixing
Send us a process, a dataset or a customer journey. In a 30-minute call we will tell you whether AI is the right tool, what it would take, and what we would build first.