MDIL Labs
Helsinki, Finland

Deterministic reasoning infrastructure
for agentic AI

Separating reasoning from generation. Hard logic from fuzzy logic.

Patent pending 226181688-FI | MCP compatible

92%
of enterprise AI agent deployments are exploitable

30 CVEs in 60 days. Asana customer data breach.
MCP ecosystem: 8,282 security findings across 1,808 servers.

Agentic AI has no control plane

  • Every framework treats the LLM as the reasoning engine — a black box
  • PII exposed to every model in every agent chain
  • No audit trail. No determinism. No enterprise control.
  • EU AI Act enforcement: August 2, 2026 — penalties up to 35M EUR
  • 40% of agentic AI projects will be cancelled (Gartner)

The industry is building agents without the infrastructure to control them.

The MDIL Paradigm

Every other framework guards the output. MDIL controls the input.

Today: hope-based AI
Input (PII exposed)
↓
LLM reasons + generates
Black box. Non-deterministic. Unauditable.
↓
Post-hoc guardrails
Try to catch problems after they happen
MDIL: engineered reasoning
MDIL Reasoning Engine (L1-L5)
Context, dimensions, knowledge, synthesis, assembly — sub-3ms, deterministic
↓
Symbol Security (patent pending)
PII becomes meaningful descriptions. Model never sees sensitive data.
↓
Any model generates (focused task only)
4B local SLM, cloud LLM, or no model at all (edge/IoT).
↓
Role-based reconstruction
Admin sees all. Employee sees names. Public sees nothing. Full audit.

Symbol Security — The Moat

Patent pending. A new category of privacy technology.

  • Concept-level abstraction — not token masking. Semantic meaning preserved.
  • RBAC reconstruction — same output, different views per role and permission
  • K-anonymity — symbols mathematically indistinguishable
  • Semantic graph — entity relationships maintained for intelligent reconstruction
  • Full audit trail — every access logged, hash-chained, tamper-evident

The AI model operates on meaningful descriptions
while never accessing the underlying data.

vs. NemoClaw (NVIDIA)

Token-level PII stripping at boundary only. Lossy. No reconstruction. No RBAC. No audit trail. No semantic preservation.

Symbol Security operates between every layer. Concept-level. Reconstructable. Role-aware. Patented.

Compliance by architecture

GDPR Art. 15/17/20. HIPAA Security Rule. EU AI Act Art. 10/13. Not bolted on — built in.

Three Product Surfaces, One Core

All share MDIL Core + Symbol Security + Memory. Model-agnostic. YAML-driven.

LLM Reasoning Wrapper

Wrap any LLM with deterministic reasoning + privacy. MDIL pre-reasons everything — the model gets a focused task, not an open-ended prompt.

↓ Substantially fewer tokens per interaction. Lower costs, lower power.

→ Next slide

MMT — Edge Reasoning

Deterministic micro-operations at sub-3ms. Pure CPU — no GPU, no model, no inference. Drone, IoT, real-time. Potentially patentable.

↓ Zero tokens. Zero cloud cost. Zero power for inference. Future potential: kernel-level reasoning service on any Android/edge device.

→ Slide 7

Secure Agentic Reasoning OS

Multi-agent orchestration with Symbol Security. Reduced token usage across entire agent chains — MDIL pre-processes so each agent receives less, reasons less, costs less.

↓ EU AI Act compliance by architecture. European AI sovereignty — no US cloud dependency. Enterprise-grade audit + RBAC built in.

→ Slides 8-9

Across all surfaces: MDIL reduces or eliminates token costs and power requirements. Business logic lives external to the model — switch models without changing logic.

LLM Reasoning Wrapper

Production today. Wrap any LLM with deterministic reasoning and privacy in minutes.

  • 7-layer reasoning pipeline — context analysis, dimensional classification, knowledge routing, meta-synthesis, response assembly, generation, reconstruction
  • YAML configuration — all intelligence in config files, not model prompts. Domain experts configure, engineers deploy
  • Model-agnostic — Claude, GPT, Gemini, local SLMs. Same reasoning, same privacy, different generation backend
  • Prompt engineering immune — MDIL's reasoning doesn't break when models update

In production

Wine experience design (tastingmoments.fi), leadership coaching platform. Different domains, same MDIL Core — only YAML configs differ.

Design AI

Natural language to YAML configuration. Describe what you want in plain language — MDIL generates validated, production-ready configs. 60+ tests, 3-stage validation.

MMT — Mini Manual Transformers

Deterministic external reasoning as micro-operations. Potentially patentable innovation.

  • Same pattern as DeepSeek MoE — but fully deterministic, auditable, and runs on any hardware
  • Sub-3ms per operation — L1-L5 as standalone micro-transformers. 300+ operations per second on consumer hardware.
  • No model, no GPU — pure CPU execution. Zero inference cost. No GPU time, no VRAM, no CUDA. Runs on a $35 Raspberry Pi.
  • Composable — parallel, sequential, or mixed. Multiple MDILs watching different concerns simultaneously.

Transformer-analogous micro-operations — but deterministic and deployable on a $35 Raspberry Pi.

Drone demo (built)

Autonomous drone sensor fusion. One YAML for rotors, one for gyro, one for LIDAR, vision YAMLs each watching specific things. No LLM. Sub-3ms decisions.

Use cases

Industrial IoT monitoring. Real-time log analysis (L1 as sentinel — 300+ events/sec). Autonomous vehicle decision layers. Medical device safety checks. All deterministic, all auditable.

Secure Agentic Reasoning OS

The control plane for enterprise agentic AI. Architecture overview.

Foundation
MDIL Core (L1-L5)
7-layer deterministic reasoning. 11 YAML registries. Sub-3ms.
Symbol Security
Patent pending
Concept-level privacy
MDIL Memory
Persistent context
Cross-session
Design AI
NL to YAML
Config generation
↑↓ ↑↓ ↑↓ ↑↓ ↑↓
Universal Control Block
Agent Bridge — 5 Universal Operations
Preprocess
L1-L5 + SS
Symbolize
Mid-stream PII
Reconstruct
RBAC per role
Synthesize
Multi-agent
Gate
Human-in-loop
↑↓ ↑↓ ↑↓ ↑↓ ↑↓
Integration Adapters
MCP Server
13K+ ecosystem
REST API
Any HTTP client
Message Queue
Kafka / Redis
CLI Pipeline
stdin/stdout
Embedded
Python import
↑↓ ↑↓ ↑↓
Cloud LLMs
Claude, GPT, Gemini
Local SLMs
4B on consumer hardware
No Model (MMT)
Edge/IoT deterministic
● Built ● Planned

Universal Integration

5 operations plug into any system. MDIL wraps agents — it doesn't replace them.

MCP Server

Model Context Protocol (Linux Foundation / AAIF standard). 13,000+ servers in ecosystem. 88% need credentials, 53% use insecure secrets, zero offer concept-level privacy. MDIL fills this gap.

Multi-Agent Orchestration

Dynamic agent selection from pool. Weighted synthesis with conflict detection. Each agent gets MDIL-preprocessed, PII-free context. Works with any agent framework.

Enterprise Hooks

3-level config hierarchy (enterprise > business unit > user). Webhook, Slack, email connectors. SLA escalation. Gate-based approval workflows. All YAML-controlled.

Agent Lifecycle Manager

Spawn, monitor, pause, resume, terminate agent instances. MDIL manages the reasoning session — container orchestrators manage the runtime.

Domain Packs

Self-contained YAML directories for healthcare, finance, legal. Plug into any deployment. 3-level certification: syntax valid, runtime valid, domain certified.

EU AI Act — Compliant by Architecture

Every article maps to an existing MDIL capability. Not bolted on. Not a compliance layer. The architecture IS the compliance. 4 months to enforcement.

→ Full mapping slide ahead

The SLM Opportunity

Gartner: SLMs used 3x more than LLMs by 2027. The problem is controllability.

Small language models are fast, cheap, and private — but unpredictable. They can't reason about 10 rules simultaneously. They hallucinate. They drift.

MDIL doesn't make SLMs "smarter." It moves all reasoning outside the model. The SLM never needs to reason — it receives a laser-focused generation task from MDIL's deterministic pipeline. The result: predictable, controllable, auditable output from a model running on consumer hardware.

Instead of one prompt with 10 rules that overwhelms a small model: 5 sequential MDIL passes, each with 2 rules the SLM executes perfectly.

4B
Parameters at
near-frontier quality
$599
Mac Mini runs full
enterprise AI
90%+
Cost reduction vs
cloud tokens

Beyond-frontier project control

MDIL enables capabilities no frontier model offers natively: thousands of documents and calendar events in active reasoning context, processed in sub-3ms real-time. Deterministic multi-instance orchestration across parallel agent pipelines — all on local hardware.

Multi-model escalation

Local SLM (speed + privacy) → validator (quality check) → cloud LLM (if needed). Full audit trail of why escalation happened. YAML-controlled.

Why Now

  • EU AI Act — 4 months to enforcement. Every enterprise needs auditable AI.
  • MCP + AAIF — Linux Foundation standardizing agentic protocols. MDIL fills the reasoning + privacy layer nobody else builds.
  • 40% project failures — uncontrollable, unauditable agents. MDIL is the rescue architecture.
  • SLM adoption accelerating — 3x by 2027. MDIL is the only enabler for enterprise SLM deployments.
  • 12-18 month lead — no competitor does external deterministic reasoning. Patent protects Symbol Security. MMT potentially patentable.

Category of one

Analyzed: OpenClaw, NemoClaw (NVIDIA), LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, Salesforce Agentforce.

No competitor does external deterministic reasoning before the model. Every framework guards the output. MDIL controls the input.

European AI sovereignty

Finnish company. EU-origin technology. Not a US wrapper. Designed for GDPR and EU AI Act from day one. ISO 27001+27701 certification path in progress.

EU AI Act — Article-by-Article Mapping

Enforcement begins August 2, 2026. MDIL maps to every high-risk AI requirement.

Requirement
Article
MDIL Capability
Risk Management
Art. 9
L4 safety checks, decision trees, escalation logic — deterministic risk evaluation
Data Governance
Art. 10
Symbol Security — PII never reaches any model. Concept-level abstraction.
Record Keeping
Art. 12
Full event streams, MDIL Memory, hash-chained tamper-evident audit logs
Transparency
Art. 13
L1-L5 audit trail — every decision logged, reproducible, explainable
Human Oversight
Art. 14
External gates with response schemas — human-in-the-loop at any lifecycle point
Accuracy
Art. 15
L4 cross-validation, multi-agent conflict detection, alignment assurance

Every competitor will need to bolt compliance onto their architecture.
MDIL was designed for it from day one.

MDIL Labs

The reasoning layer the
agentic AI industry is missing.

Patent
Symbol Security
MCP
Compatible
EU AI Act
By architecture

European AI sovereignty. Built in Helsinki. Compliant by design.

mdil.ai

antti@mdil.ai

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