ML Infrastructure
Make deep agentic workflows cheaper without hosting inference
LE Control Plane is an API-only protocol that enables stateful continuation across multi-step workflows in your own vLLM / sglang / HF stack. The Vertical Training Compiler applies the same spine/leaf structure to vertical corpora to reduce training cost.
- →Deeper workflows without exponential cost
- →Lower latency on shared-context pipelines
- →Predictable spend for verifier and retry loops
Designed for teams already running multi-step AI workflows in production.
What CLC Does
CLC Labs emerged from building real multi-step AI systems where repeated recomputation of shared context became a dominant cost driver. The pain of watching the same context processed multiple times across workflow steps led us to focus on execution-layer optimization.
LE Control Plane reduces redundant computation in deep, multi-step agentic workflows by replacing full prompt replay with stateful continuation. It operates as a control plane (policy + receipts) while inference remains in your infrastructure.
CLC delivers its strongest economic and latency gains when repeated prefill dominates execution cost; in highly optimized, high-concurrency clusters, its value shifts to predictable session behavior rather than additional speed.
CLC runs node-local alongside standard inference runtimes. It does not replace engine-level optimizations and does not move computation across nodes.
Who It's For
CLC is designed for Phase-1 buyers: teams transitioning from API-only inference to self-hosted deployments.
- —You're transitioning off API-only inference to self-hosted deployments
- —You run single-node or small-cluster deployments (not fully distributed)
- —You operate long-context, multi-step workflows with shared context
- —Cost predictability matters more than peak throughput
- —Hardware resources (VRAM) are constrained
- —You only use hosted API providers (OpenAI, Anthropic)
- —You operate fully optimized, high-concurrency inference clusters where prefix reuse is already amortized
- —You need cross-node computation portability or distributed optimization
- —You're focused on single-turn interactions without workflow depth
Why CLC
Avoided recompute with proof
Receipts quantify avoided prompt replay and provide auditable lineage for benchmarking and governance.
Customer-hosted inference
You keep GPUs and model endpoints. LE stays a control plane, not an inference vendor.
Verticalization
The Training Compiler makes vertical structure explicit (spines + leaves) to reduce training cost and improve convergence.
Who We're Building With
We are working with a small number of teams building deep, multi-step AI systems where repeated context processing is a dominant cost driver.
Our design partners are teams building:
- Multi-step agent systems with sequential reasoning
- Long-context reasoning pipelines with shared context across steps
- Inference-constrained deployments where execution overhead limits workflow depth
These teams are evaluating LE Runtime (Coming Soon) on production workloads to measure cost and latency impact before committing to production deployment.
Work with us as a design partner
Early collaboration and evaluation access for teams building deep agentic workflows.
Start with a bounded eval
Run the EVAL policy profile and collect receipts that quantify avoided recompute. Promote to production without changing tooling.