Enterprise Consistency Layer

Make your AI
behave consistently
in production

Mnemostroma enforces decisions, constraints, and domain rules across sessions — so your AI stops contradicting itself and breaking workflows.

Book enterprise demo See how it works

No retraining. No model changes. Works with your existing stack.

On-Premise Air-Gapped Zero Data Leakage

Technical Deep Dive

mnemostroma SYSTEM ARCHITECTURE · INFRASTRUCTURE TOPOLOGY REV 2026.04 · STRICT CONDUCTOR PROXY TCP / HTTP Intercept Dynamic Context Injection stdio · Daemon INJECT <memorycontext> L1 · I/O BOUNDARY CLIENT (IDE/CLI) User Interface Standard I/O AI AGENT LLM Engine FORK STREAM L2 · ASYNC SIDECAR PIPELINE OBSERVER (NON-BLOCKING CAPTURE) Total: ~20ms latency Regex Pass 0.1ms · PII strip HybridNER 10ms · Entity Extr. e5-small 15ms · Vector Embed TinyBERT Ranker 6ms · Core logic L4 · DATA ROUTING & DISPATCH "THALAMUS" ORCHESTRATOR (PERSISTENCE HUB) Queue Batching Parsed Kernels L3 · MEMORY CORE (HETEROGENEOUS) RAM INDEX In-Memory Vector Engine fp16·512d Relevance Fn R·T·I Score Retrieval Latency ~20ms p95 Eviction Policy LRU + Decay Session Bound 200-500 cap SQLITE WAL Disk / IO Core Ledger Fact Vault Anchor Constraints Decision Trees Write Mode Async Append Storage Engine WAL2 + fsync Hydration Cold Boot Hot Vectors Fsync Facts L5 · DECAY ENGINE (BACKGROUND CRON) DREAMER BACKGROUND WORKER Trigger: Idle 5m Dissolution Pipeline: FULL TEXT → GIST → SKELETON EMBEDDING Compression Ratio: ~85% reduction. Evicts low LRU-score records from RAM Index. Scan low-score L6 · MCP API SURFACE MCP EXPOSED TOOLS (READ-ONLY) Transport: stdio / JSON-RPC ctx_semantic() ctx_anchors() ctx_bridge() ctx_search() Agent sends query args Return Vectors Return Facts Data Payload (JSON to Agent) MAX FOOTPRINT 600MB RAM INFRA DEPENDENCY ZERO (Local SQLite & ONNX) RUNTIME ENGINE ONNX Runtime INT8 / CPU

Your AI is unreliable
by default

This is not a model quality issue. This is a missing behavioral enforcement layer. Without it, your production AI creates significant operational risk.

  • ✕ Contradicts previous decisions
  • ✕ Ignores defined constraints
  • ✕ Breaks consistency across sessions
  • ✕ Produces inconsistent outputs for same logic

Operational Risk

Inconsistency in production means broken workflows, manual overrides, and loss of trust in your AI infrastructure.

CRITICAL: AI ignored project constraint "Use Kafka for events" after session reset. Manual hotfix required.

What changes in Production

Standard LLM Pipeline

"How should we handle the architecture? Should we reconsider event-driven approach?"

→ Previous architectural decision ignored.
With Mnemostroma Enforcement

"You selected event-driven architecture with Kafka. Continuing with that constraint."

→ Decision enforced automatically.

We enforce how your AI behaves

Beyond memory: Behavioral consistency for AI products.

Persistent Decisions

Decisions made in session 1 are automatically enforced in session 100.

Constraint Guard

Domain rules and constraints are injected into every prompt systematically.

Context Injection

Dynamic context is retrieved in 20ms and injected before the agent acts.

Contradiction Prevention

Detected conflicts between current logic and past decisions are flagged.

Works from the first session.

Deploying Mnemostroma requires zero model retraining and zero agent code changes. We integrate with your existing LLM stack in days, not months.

3–5 Day Setup
Rapid on-premise or VPC integration.
No Model Reset
Existing model weights remain untouched.
Local / Private
Full air-gapped deployment available.
ROI Focused
Immediate token cost reduction (80%+).

Gets stronger over time

  • → Adapts to your team's unique workflows
  • → Reinforces best practices automatically
  • → Reduces repeated errors in outputs

Architectural Review

30min technical call. Quantify your token waste and constraints fit.

Schedule Review