SPARKITTest the real job.
A public ranking cannot tell you whether a system will work in your company. A useful evaluation uses the tasks, data, risks, and rules that matter to your decision.
About SPARKIT
AI should prove it works.
We independently evaluate AI systems, publish public benchmarks, and help teams put the right models and agents into real work.
Why this exists
AI systems are easy to demo and hard to judge. A model can look impressive in a short example and still fail on the work that matters to your team. The company selling it may not tell you which mistakes it will make, how often it will make them, or what those mistakes will cost. SPARKIT gives teams clear evidence before they choose or use an AI system.
What SPARKIT does
Independent evaluations. You tell us what an AI system needs to do. We build tests around that work, run the tests independently, and explain what works, what fails, and why.
AI and agent integration. Once you choose a system, we help connect it to your software, data, tools, and workflows. We also help define permissions, human review, and the places where AI should not be used.
Research agent. SPARKIT also offers an agent that searches the web and scientific literature, reads sources, runs calculations or code when needed, and returns a cited report your team can inspect.
Why we publish benchmarks
A public benchmark lets anyone inspect the same comparison. We publish the systems tested, the questions, the scoring method, and the limits of the result. A benchmark cannot choose a system for every team, but it can replace vague claims with evidence. See the public benchmarks →
What we believe
Show the evidence.
We explain what was tested, how it was scored, what failed, and what the result cannot prove. You should be able to see the evidence behind a recommendation.
Choose tools by the job.
We are not tied to one AI company. We choose and test models and tools based on what they can do, how safe and consistent they are, how fast they run, and what they cost. The right system can change over time.
Your queries belong to you.
We do not sell customer data or use customer questions and reports to train models. Our Privacy Policy names the outside companies that process data to provide the service.
Say when we do not know.
AI systems can be wrong, and evaluations are never universal. We state uncertainty and known limits instead of turning partial evidence into a bigger claim.
Use powerful AI carefully
AI can produce confident mistakes and can make harmful work easier. We check questions and answers for safety, show sources and limits, and require people to review important or risky work. No safeguard is perfect, so people remain responsible for important decisions.
The SPARKIT team
Our evaluation reports, benchmark releases, and technical notes are prepared by the team doing the work. Public benchmarks name the systems tested and explain the method, scoring, and known limits. Questions or corrections can be sent to info@sparkit.science.
Get in touch
If you need an independent evaluation or help integrating AI into your organization, write us at info@sparkit.science.