MDIL Labs
MDIL provides a deterministic reasoning layer that separates decision-making from text generation, allowing AI systems to operate on transparent, human-readable rules. Every answer produces a verifiable receipt documenting each reasoning step, enabling full auditability. The SDK can be embedded into chat agents, tool calls, or entire processes, and supports zero-trust environments where no personal data crosses between the model and user data.
- Artificial Intelligence
- Developer Tools
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Most AI systems operate as black boxes, making decisions that users cannot verify or audit. This lack of transparency forces businesses to trust AI outputs blindly, creating compliance, safety, and accountability risks across industries.
Solution
MDIL provides a deterministic reasoning layer that separates decision-making from text generation. The system applies transparent, human-readable rules to determine outcomes, while any language model writes the words without making decisions. Every answer produces a verifiable receipt documenting each step of the reasoning process, enabling full auditability. The layer can be integrated into chat agents, tool calls, or wrapped around an entire process to orchestrate every step. It also operates as a zero-trust environment, ensuring model outputs contain no personal data. This architecture shifts AI from an opaque black box to a checkable glass box.
Target Audience
Developers building AI into their own products need transparent decision-making, along with businesses seeking an AI agent that provides auditable, rule-based answers and asks for input when it lacks knowledge.
Features
- Deterministic rule engine that makes all decisions based on readable, auditable rules
- Model-agnostic text generation—any language model can write responses without decision authority
- Verifiable receipt for every output, logging each reasoning step (e.g., rule citations, review triggers)
- SDK for embedding the reasoning layer into custom products and existing stacks
- Zero-trust environment capability where no personal data crosses between the model and user data
- Tool-call validation: checks every tool call before execution, blocking out-of-policy actions (e.g., transfers over limits)
- Configurable policy packs, guardrails, and audit logging for compliance-sensitive deployments
- Human review escalation when rules require manual approval