Deep Neural Logic Assurance
Know exactly what you're trusting your AI system with.
DNLA is an independent diagnosis, control, and decision layer for enterprise AI systems. We answer the one question the market struggles to ask out loud: does the AI system you built, bought, or are planning actually justify the money, the risk, and the trust your organization is putting into it.
The problem
Demos are not production. The gap between them is where money burns.
Enterprise AI moved fast from experimentation to expensive, complex, real-world projects. Organizations built impressive proofs of concept, signed with a vendor, shipped a chatbot, a RAG system, or an autonomous agent, and then discovered the system doesn't behave like the demo. The model is rarely the real problem. Usually it's deeper: no measurable business problem, no operational owner, unreliable data, a fragile architecture, no quality monitoring, no cost control, no kill switch, and no independent party willing to tell leadership the system doesn't justify continued investment.
QAi, DNLA's flagship product, exists precisely in that gap: between an impressive model answer and management's responsibility for a live system.
The reference standard
Five layers of a healthy AI system
Every audit is measured against the same engineering standard, regardless of vendor, model, or stack.
Agent Runtime & Orchestration
How does the system run and coordinate actions?
State management, queues, retries, timeouts, handoff, idempotency, workflow traceability, compute efficiency under load.
Tool Layer
What is the system actually able to do?
Permissions, API contracts, validation, rollback, read/write boundaries, auditability.
Data & RAG
What does it know, and how reliable is it?
Source quality, freshness, retrieval, metadata filtering, permissions, provenance, reranking, semantic caching, context management.
Guardrails & Safety
What is blocked, and who approves it?
Action policy, human-in-the-loop, blocking dangerous actions, PII handling, prompt injection, output validation.
Evaluation & Telemetry
How do you know it's actually working?
Evals, canary questions, regression tests, cost per task, cost per customer, latency, drift, quality dashboards.
The outcome
Every audit ends in a verdict, not a report for the drawer.
Not every system is assumed guilty. A system can be healthy, need a light tune, require a focused fix, justify a rebuild, or be better stopped before more money burns.
The system is healthy and properly managed.
Fundamentally sound, needs targeted tuning.
Clear, fixable failures on an otherwise sound foundation.
Right problem, wrong foundation to keep building on.
Continued investment isn't justified as-is.
The DNLA suite
QAi is the flagship. It isn't the only product.
Red-teaming, incident investigation, benchmarking, and governance readiness, built to stand on their own or plug into a QAi engagement.
Red Team Lab
An adversarial testing lab that deliberately tries to break your AI system before someone else does.
Incident Review
An independent investigation when an AI system causes harm, a wrong decision, or an operational incident.
Impact Assessment
In devA structured AI Impact Assessment before deployment, or after a significant change.
Governance Readiness
Preparation for an AI management system aligned to ISO/IEC 42001, the NIST AI RMF, and the EU AI Act.
Benchmark Lab
A lab that builds a benchmark custom to your system, instead of testing on random questions or gut feel.
Registry
In devA central registry of every AI system running inside your organization.
First principles
What DNLA won't compromise on
Radical independence
Whoever builds the system cannot be its sole judge. The audit isn't tied to a vendor, a tool, or a model.
Surgical truth
The goal isn't to reassure the client; it's to reveal the system's actual state, even when the conclusion is to stop.
Operator before tool
A system's value depends on judgment, accountability, and business context, not model power alone.
Economics before technology
Every technical finding is translated into money: current cost, future cost, money at risk, cost to fix.
Before you invest more, know what you're standing on.
A Health Check is priced far below the cost of finding out the hard way.