Semantic Cache Engine intercepts repeated questions, serves deterministic answers from a shared reasoning layer, and routes new questions with provenance-rich context — cutting inference cost and keeping agent behavior consistent across your codebase and document workflows.
Semantic Cache Engine is a local-first server written in Rust. It exposes a REST and MCP interface and runs on your own hardware — on-prem, in a VPC, or on a developer workstation. There is no hosted service, no metered API, and no inference endpoint owned by us. Every query is resolved against a local store.
It is not a framework and not an agent. It is a substrate layer that any agent, any model, and any orchestration stack can call. The same server that answers a single developer's questions during a session can serve a fleet of 50 concurrent agents across an enterprise workflow without modification.
VS Code integration is available as an MCP client — but the server is the product.
In a modern agent workflow, multiple agents — sometimes dozens — are querying an LLM independently. Each one re-establishes the same foundational understanding of your documents, your rules, and your domain. None of them share what they learned. The token meter runs the same for agent number fifty as it did for agent number one.
At scale, this is the primary driver of inference cost in document-heavy workflows. Insurance underwriting. Legal document review. Regulatory compliance. Claims processing. Every run of the same logical question burns the same tokens, produces the same answer, and then discards it.
Point the server at your document sources. It chunks and indexes them into a local store. Every section becomes a retrievable reasoning anchor with provenance tracking.
When an agent sends a question, the resolver checks the local store through a multi-tier matching cascade. Exact matches, paraphrases, and semantically equivalent questions are all candidates for a warm recall — without calling the LLM.
Questions that are close but not confident enough for a direct recall are returned to the agent with a graph-hydrated context packet. The agent reasons with a structured starting point instead of a blank slate — fewer tokens in, same quality out.
When the agent produces an answer on a cache miss, it commits the result back to the store. Every future agent asking an equivalent question gets a zero-token recall. The store gets smarter on every handoff.
The core reasoning engine itself — deterministic recall, provenance, dependency-graph cascade invalidation — proven out against a full acceptance test suite, then handed to real, independent developers for hands-on testing via the packaged VS Code extension.
Full exercise of the Rust server itself — concurrent multi-agent fleets, multi-tenant isolation, explicit dependency-edge workflows between agents, and cascade invalidation under real load, not just single-session testing. Confirms the server holds up before any pilot commitment.
TLS, authentication, request-level audit logging, configurable retention, structured error handling, observability, and backup procedures. Deployed into one or two high-value enterprise workflows to produce real-world ROI evidence at scale.
Versioned releases, schema migration tooling, SLAs, multi-tenant and VPC reference architectures, client SDKs, and enterprise onboarding. The substrate becomes a shared reasoning layer across departments — a single store serving every agent in an organization, with compliance-ready audit trails built in.
SCE is designed for document‑heavy workflows where the same reasoning patterns fire repeatedly — insurance underwriting, legal review, regulatory compliance, and policy Q&A. Code is fully supported, but structured documents deliver the largest and most defensible token savings.
Our pilot program is for teams ready to run SCE against a real production workflow before GA. We select pilot partners based on workflow fit — where the engine can deliver the strongest results for you. Alpha (September) and Beta (October) validation ensure the engine is hardened before deployment. After reviewing results from both rounds, we reach out ahead of a targeted November pilot start. You will receive full evidence reports before committing to anything.
