Beta complete · Rust-native · Zero egress by default

Governed memory. Verified recall. Fully auditable.

The reasoning layer your agents can actually trust — a shared memory of what's already been reasoned through, where every recall is gate-checked and cryptographically signed before it's served, all inside your own walls. Nothing leaves your infrastructure unless you export it yourself.

Talk to us See how it works

egress by default

73–80%

token reduction, warm queries

tokens on cache hits

orchestrators, minimal changes

Every agent call that re-reasons from scratch is wasted inference. SCE turns every answer into verified, reusable infrastructure the next agent recalls for zero tokens — not a cache you hope is right, a cache you can prove is right.

// Capabilities

A substrate layer, not a plugin.

LangChain, LangGraph, CrewAI, AutoGen, or a raw OpenAI/Anthropic SDK client — any orchestrator that supports a custom endpoint gets cache-hit recall with a minimal config change, not a rewrite. MCP clients (VS Code, Cursor, Claude Desktop) are supported the same way.

Integrationbase_url swap

ProtocolsREST · MCP · OpenAI-wire

Code changesMinimal

Works with any model; swap providers freely. Cards are fingerprinted against the model that produced them, so a model change is detected automatically — affected cards demote gracefully on next hit and re-commit under the new signature. No manual purge required.

FingerprintingPer-model signature

On model swapAuto re-commit

Manual purgeNot required

All reasoning cards, embeddings, and graph edges live in a local store. SCE is a reasoning cache and router, not a reasoning generator — on a cache miss, the gateway forwards only to the LLM endpoint you configure. Point it at an air-gapped model and the entire stack stays air-gapped.

StorageLocal SQLite

Phone-homeNone

Air-gap compatibleYes

Every committed card carries a dependency graph and an ed25519 signature. Multi-tenant namespace isolation keeps each team or department's cards strictly separated, and cascade invalidation evicts affected cards automatically when a source document updates.

Signinged25519

Tamper checkBLAKE3 hash

IsolationPer-namespace

Every agent handoff that produces an answer commits it back to the store. The next agent asking an equivalent question pays zero. The ratio improves continuously as the card store matures — it never resets.

Per-card trackingTokens invested / saved

Live endpoint/stats

TrendImproves with use

// How It Works

Ingest. Resolve. Commit. Repeat.

Ingest your documents

Point the server at your sources. It chunks and indexes them into a local store with provenance tracking on every section.

Every question hits cache first

The resolver checks the local store through a multi-tier matching cascade before ever calling the LLM.

Partial matches get context

Near matches return a graph-hydrated context packet — fewer tokens in, same quality out.

New answers commit back

Cache misses are committed on return. Every future equivalent question gets a zero-token recall.

Four routing outcomes

Recall

High-confidence match. Zero tokens. No model call.

Graph-assisted

Near-match plus related graph context. Reduced token cost.

Agent handoff

No match, but a structural document map is available.

Generic

Cold miss. Full agent call. Answer committed on return.

// Evidence

What the test suite proves.

40 / 40

Milestone tests passing

Multi-domain retrieval, confusable-pair disambiguation, semantic delta detection, cross-document rule reuse, concurrent fleet simulation. All green.

Wrong-card recalls

Across ~250,000 targeted adversarial queries and a 5-day, 2-VM production soak (3,089 audited recall events), zero wrong-card recalls. Still tuning additional gating for rarer edge-case value swaps.

73–80%

Token reduction, warm queries

Per-run savings of 73–77% on first warm pass; all-time ratio reaches ~80% as the card store matures. Verified with real LLM API calls, engine-on vs engine-off.

Concurrent workers, real hardware

Tested on real 2-vCPU production hardware: clean throughput up to 32 workers, latency-based ceiling found at 57. Zero wrong recalls and zero audit mismatches at every stage, even pushed to 100 workers past that ceiling.

17 / 17

Acceptance patterns verified

Cascade invalidation, cross-document reasoning, multi-tenant isolation, tamper detection, concurrent writes, dependency edges, full audit trail completeness.

25k

Cards, correctness held at scale

10,000 cards committed with zero errors and sub-20ms resolve latency (P99 18.9ms). Conflict-detection gates re-verified clean at 25,000 background cards under paraphrase, negation, and numeric-collision stress testing.

// Where This Is Going

Every stage is earned, not scheduled.

Stage 1Done

Alpha — Local reasoning substrate

Core reasoning engine (deterministic recall, dependency-graph cascade invalidation, bundle importing), proven against a full acceptance suite, then handed to independent developers for hands-on testing.

Stage 2Done

Beta — Full server + agent workflow verification

Concurrent multi-agent fleets, multi-tenant isolation, dependency-edge workflows, cascade invalidation under real load — including a real 2-VM hosted soak test, 5-day continuous run. Still tuning gating for rarer edge cases found along the way.

Stage 3In progress

Enterprise Conversations & Development Partnerships

Starting October 2026, open-ended. Direct conversations with prospective enterprise teams to map real workflows, compliance requirements, and integration needs before any pilot commitment — not a countdown to a pre-set pilot date. Duration depends on the partnerships, not a fixed timeline.

Stage 4

Pilot Testing

Begins once a development partnership reaches pilot readiness — no fixed start date. Live deployment inside a real production workflow, collecting real ROI evidence.

Stage 5

Enterprise GA

Gated by pilot success and full enterprise-readiness closure. Versioned releases, SLAs, multi-tenant/VPC reference architectures, client SDKs.

// Why SCE Exists

I built SCE out of frustration as a Quality and Automation Engineer. Watching dev teams burn huge token budgets re-feeding the exact same codebase and policy files every morning made no sense.

My background is in test automation, internal tooling, and systems verification. I built SCE with a zero-tolerance approach to reliability: strict single-writer SQLite channels, BLAKE3 tamper verification, and deterministic recall.

Reasoning layers should be treated like core infrastructure: reliable, verifiable, and cost a fraction of what they do today.

— John, Founder & SCE Architect

// Enterprise Conversations

Let's talk about your workflow.

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.

Alpha and Beta (including a 5-day, 2-VM hosted soak test) are both complete and hardened. We're now in direct conversations with prospective enterprise teams — not counting down to a pre-set pilot date. You will receive full evidence reports before committing to anything.

The Substrate & Governance Thesis

The Evidence Behind the Token Firewall, Memory Hub, and Compliance Standard

Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% today.¹ But as agent adoption accelerates, teams are hitting a wall.

Bain's Automation and AI Pathfinder Survey found nearly 40% of companies measuring AI cost savings landed below 10%, despite targeting 11–20%; data access and integration, not model quality, is the number-one barrier to progress.²

NIST's AI Risk Management Framework formally names computational cost as a distinct AI-specific risk category, separate from traditional software risk.³ Reasoning models and multi-step agent workflows compound this further: industry commentary from AI tooling vendors puts reasoning-token overhead at roughly 5–20x a standard exchange, higher still across multi-agent pipelines.⁴ Re-feeding the same codebases, policy documents, and compliance rules to every agent in a fleet, every time, is no longer just inefficient. It's a compounding cost problem.

SCE was built to serve as the shared reasoning substrate across your entire agent stack.

The Token Firewall: intercepts repeated questions locally, reducing measured inference costs 73–80% on warm queries, with minimal orchestration changes required.
The Cross-Agent Memory Hub: when Agent A reasons through a complex document, it commits its card back to the local store. Agents B and C recall that verified output at P50 8.3ms – P99 18.9ms, at zero token cost.
The Compliance Standard: every card carries a BLAKE3 tamper-evident fingerprint and local audit provenance inside your own VPC, supporting the transparency, accountability, and audit-trail expectations described in NIST's AI RMF.⁵

"Reasoning shouldn't be recomputed on every agent handoff. It belongs down in the core infrastructure stack: predictable, verifiable, and a fraction of what it costs today."
— John, Founder & SCE Architect

Sources

Gartner, "Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026", Aug 26, 2025.
Bain & Company, "Your AI Budget Is Growing. Your Returns Aren't. Here's Why.", Jun 1, 2026.
NIST AI RMF 1.0 (NIST AI 100-1), Appendix B, "How AI Risks Differ from Traditional Software Risks," p.39.
MindStudio, "What Is the AI Token Cost Crisis?" (industry/vendor commentary, not primary research).
NIST AI RMF 1.0, Sections 3.3 "Secure and Resilient," 3.4 "Accountable and Transparent," 3.5 "Explainable and Interpretable," 5.2 "Map," 5.4 "Manage."