CLC is early.
That's intentional.
We're not launching a platform or an SDK. We're validating a structural change in how AI workflows execute.
This post is part of a series on the economics of multi-step AI workflows. We examine why inference costs scale with depth, why verification is disabled in production, and why existing optimizations fail to eliminate redundant execution across workflow steps.
Who We're Looking to Work With
We're partnering with a small number of teams who:
- Run deep, sequential **multi-step AI workflows**
- Use large shared context
- Have verification or retry logic they'd like to keep enabled
- Feel constrained by current inference cost structure
- Can evaluate changes at the infrastructure or runtime boundary
This is not a fit for:
- Prompt-only systems
- Single-turn chat products
- Teams without control over execution
What a Design Partnership Looks Like
A typical engagement is short and focused:
- Identify one representative workflow
- Measure baseline execution cost and latency
- Introduce an execution-layer change
- Compare structure-level outcomes
No rewrites. No platform migration. No long-term commitment required.
What We're Optimizing For
At this stage, success is not:
- Public benchmarks
- Broad adoption
- Feature completeness
Success is:
- Clear cost-structure improvement
- Verification staying enabled
- Workflow depth increasing without cost explosion
Why This Is Limited
Execution-layer changes are foundational.
We're keeping the surface area small to:
- Avoid premature abstraction
- Ensure correctness
- Protect long-term differentiation
If this resonates, the next step is a direct conversation—not a signup flow.
Understanding why LLM workflow cost scales with depth helps identify if this is a fit. Teams dealing with AI verification loops that get disabled due to cost are ideal candidates.
CLC Labs is selectively engaging with teams who already feel this problem and want to help shape the solution.