DNLA's flagship product
QAi: the AI System Health Check
QAi is a structured, repeatable diagnostic product that performs a comprehensive health check on an existing or planned AI system. The result isn't just a technical report; it's a business-and-engineering verdict: Healthy, Tune, Fix, Rebuild, or Kill. QAi isn't one engagement; it's a family of engagements built around the same standard, sized to where you are: before you sign, while you're live, when it's urgent, or when capital is on the line.
Why it exists
Turning uncertainty into a decision
DNLA isn't a development shop, an integrator, or a tool vendor. QAi doesn't sell another system, another model, or more development hours. It sells independent professional truth, translated into a management, engineering, and financial decision. The audit examines five system layers through eight dimensions of judgment, and translates every finding into business meaning: cost, risk, and a clear course of action.
The QAi family
One standard, six engagements
Every tier applies the same independent methodology, scoped to the decision in front of you.
Pre-Flight AI Check
Before you sign an AI project, a vendor contract, or a Statement of Work.
$5,000 – $8,000
Architecture Due Diligence
A startup preparing to raise, an investor before committing capital, or a beta product before it scales.
$8,000 – $15,000
Health Check
A system already in production, or close to it, showing signs of failure, rising cost, or eroding trust.
$10,000 – $16,000
Executive Rescue Sprint
An urgent situation: the system is failing, customers are complaining, costs are spiking, and leadership needs a fast, defensible call.
$13,000 – $23,000
Verification Badge
Companies preparing to raise, get acquired, or clear enterprise procurement, who want independent verification in hand before the conversation starts.
Scoped per engagement. Get in touch.
Managed Governance
After an initial audit, when you want ongoing monitoring of quality, cost, and risk instead of a one-time snapshot.
$2,500 – $8,000 / month
Eight diagnostic dimensions
From technical system to management decision
The five layers describe where in the system the components live. The eight dimensions describe what needs to be judged about them: the layer between the engineering anatomy and a management, financial, and legal decision.
| Dimension | Key question | Money at risk |
|---|---|---|
| 01Problem Fit | Are the problem, solution family, model, tools, and automation level matched to the business outcome? | Investment in the wrong solution shape (wrong model, tool, workflow, or spec) even where AI is conceptually the right call. |
| 02Architecture | Is the engineering skeleton stable, operable, and extensible? | Growth ceilings, performance failures, runaway operating costs, and scale that never improves unit economics. |
| 03Data & Corpus | Are the information sources reliable, complete, current, and authorized? | Wrong decisions made on corrupted, stale, or unauthorized data. |
| 04Logic & Code | Does the actual implementation match business and engineering intent? | Silent bugs, unhandled edge cases, unpredictable behavior in production. |
| 05Eval & Hallucination | How do you know the system answers correctly instead of making things up? | Reputational, legal, and operational damage from confidently wrong answers. |
| 06Operational Maturity | Is the system actively managed over time under production conditions? | Silent decay, drift, rising cloud spend, cost-per-task and ROI eroding without warning. |
| 07Security | Is the system protected against attack, leakage, and misuse? | Data leaks, unauthorized actions, and damage to customer trust. |
| 08Compliance & Regulation | Does the system meet its legal, privacy, documentation, and fairness obligations? | Fines, legal exposure, blocked enterprise sales, or forced shutdown after deployment. |
The verdict model
Healthy · Tune · Fix · Rebuild · Kill
Every audit ends with the most responsible decision the evidence, cost, risk, and business value support, not a default assumption that something is broken.
| Verdict | When it's used |
|---|---|
| Healthy | Problem, architecture, data, measurement, security, and economics all meet the bar. |
| Tune | The foundations are sound but prompt, retrieval, caching, metrics, process, or cost need improving. |
| Fix | Clear failures exist, but they're addressable without changing the foundations. |
| Rebuild | The problem is right, but continuing on the existing foundation is expensive, risky, or unscalable. |
| Kill | The problem doesn't justify the solution, the risk isn't defensible, or continued investment will only increase the damage. |
Scope
What the audit is not
To protect trust, transparency, and independence, it's just as important to define what QAi does not provide.
- A full legal opinion: the audit flags privacy, regulatory, and compliance exposure, but doesn't replace binding legal counsel.
- A full penetration test: we assess AI-specific security and permission risk, not a substitute for a dedicated cyber pen test.
- A guarantee of commercial success: we assess fit, risk, and readiness, not market adoption or revenue.
- A replacement for your dev team: QAi doesn't step in for the team; it adds a layer of diagnosis, control, and direction.
- A tool or model sale: recommendations follow the problem, not any incentive to promote a particular stack.
- A default stop to your project: the audit runs alongside your existing progress; its job is better decisions, not artificial delay.
Beyond QAi
The rest of the DNLA suite
QAi is the flagship diagnostic, but several of its components (red-teaming, incident investigation, benchmarking) stand on their own as focused engagements, alongside a set of governance and compliance products built for specific risk needs.
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 developmentA 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 developmentA central registry of every AI system running inside your organization.
See which package fits where you are.
From a pre-signature sanity check to ongoing managed governance.