Every vendor claims their model is the smartest. aiiq.ms turns that claim into something measurable — a clear, repeatable way to evaluate how AI systems reason, retrieve, and respond across the tasks that actually matter to you.
aiiq.ms replaces hype and guesswork with measurement. Four evaluation dimensions, one explainable score, continuous monitoring.
Measure reasoning, coding, retrieval, summarization, and tool use against realistic prompts drawn from your actual workloads — not synthetic benchmarks designed to make every model look good.
See what each point of accuracy actually costs, so you spend deliberately. The most expensive model is almost never the right model for every task in your stack.
Flag confident-but-wrong answers and unsafe outputs before users see them. Safety scoring is built into every evaluation run, not bolted on as an afterthought.
Watch how a model's performance shifts release to release and catch silent regressions before they become production incidents. Get warned the moment quality drops.
From defining tasks to ranked, explainable recommendations and continuous monitoring — in minutes, not months.
Specify the tasks and quality criteria that matter for your use case. Use built-in suites or build from your own data and prompts.
aiiq.ms runs every candidate model against a consistent, repeatable test set. Same inputs, same conditions, no cherry-picking.
Results scored across accuracy, cost, latency, and safety. Every metric is explainable and reproducible to hold up to scrutiny.
Ranked comparison delivered instantly. Ongoing monitoring continues as models change, with regression alerts the moment quality drops.
aiiq.ms doesn't just produce scores. It turns measurements into decisions your entire organization can act on and defend.
Match each task to the model that wins on your criteria — not the one with the best marketing. Route intelligently and stop overpaying for general capability when specialist models outperform.
Justify model choices to finance and leadership with hard numbers, not vendor slides. Every procurement decision becomes evidence-based and auditable from day one.
Get warned the moment a model's quality drops in production. Automated re-scoring on every provider update means silent regressions become visible alerts, not user-reported bugs.
Give everyone — engineering, product, and leadership — a shared, grounded understanding of what your AI can and cannot do. Shared language, shared numbers, shared accountability.
Every score is explainable and reproducible. Run the same evaluation again and get the same result. Results hold up to internal audit, procurement review, and board scrutiny.
Build a defensible AI governance program backed by measurement. Demonstrate to regulators and auditors that model selection and quality assurance follow a documented, repeatable process.
Before signing a contract or committing to a provider, run every candidate model against your actual workloads. Arrive at vendor negotiations with evidence — not assumptions. Replace the gut-feel selection process with ranked, explainable scores that hold up to scrutiny at every level of your organization.
AI features already in production need continuous monitoring, not a one-time evaluation. aiiq.ms re-scores automatically when providers update their models, so you catch quality regressions before users do. Build quality gates that automatically alert the team when scores drop below acceptable thresholds.
Procurement and vendor reviews that need objective, auditable evidence. Replace vendor-supplied benchmarks with independently generated scores on your own task sets. Provide finance and legal with the defensible documentation they need to approve AI spend at scale.
As AI spreads through an organization, leaders need a defensible answer to "why this model, and how do we know it's good enough?" aiiq.ms produces that answer in numbers, building team-wide AI literacy and supporting formal AI governance programs that satisfy board and regulatory expectations.
aiiq.ms evaluates models from every major provider — commercial frontier models, open-source, and custom fine-tuned endpoints. One consistent benchmark, all your options, side by side.
Plus any OpenAI-compatible endpoint. New providers added continuously.
aiiq.ms handles evaluation data with the same rigor you expect from mission-critical infrastructure. Evaluation prompts and outputs stay in your control — always.
| Feature | Starter | Team | Enterprise |
|---|---|---|---|
| Task-based scoring | ✓ | ✓ | ✓ |
| Domain-specific test sets | — | ✓ | ✓ |
| Regression monitoring | — | ✓ | ✓ |
| Safety & hallucination checks | — | ✓ | ✓ |
| SOC 2 / GDPR reports | — | — | ✓ |
| Self-hosted deployment | — | — | ✓ |
| Custom model endpoints | — | ✓ | ✓ |