AI Research
Measurement against your tasks, not public benchmarks.
Public leaderboards tell you very little about how a model performs on your work. We build a measurement harness over your own tasks and data, then use it to choose models, tune prompts and decide whether fine-tuning is worth it.
What is AI research and evaluation?
AI evaluation means testing AI models on your own real examples, your customer questions, your documents, your calls, and measuring how accurate, fast and expensive each one is.
Public leaderboards tell you which model is best in general. They do not tell you which one is best for your work, or warn you when a model update quietly makes your product worse.
Before launch, 300 real customer questions are run through four models. The second-cheapest scores highest on your data, saving cost while improving answers.
How it helps your business.
Pick the right model
Choose based on how models perform on your tasks, not marketing claims.
Lower running costs
Often a smaller, cheaper model does the job just as well once tested properly.
Catch problems early
Automatic tests run on every change, so quality drops are found before customers notice.
Proof for stakeholders
Clear numbers on accuracy and cost to share with leadership or compliance.
Real ways teams use it.
Not sure if AI Research fits your business? A 15-minute call will tell you.
You are probably here because one of these is true.
Deliverables, not a summary of our thinking.
Evaluation harness
A repeatable test suite over your real tasks, with scoring you agree with.
Model comparison
Candidates measured on accuracy, latency and cost against your own workload.
Prompt engineering
Systematic iteration with every change measured, not vibes.
Fine-tuning assessment
Whether it helps, what data it needs, and what it would cost to maintain.
Capability map
What the system reliably does, what it does sometimes, and what it should not be asked.
The shape of the engagement.
The harness is the deliverable that outlasts the engagement. Once it exists, every future change to your AI system can be measured instead of argued about.
Questions about AI Research.
Why not just use the most popular AI model?+
The most popular model is not always the most accurate or affordable for your specific work. Testing on your own examples shows which one actually is.
What data do you need?+
A set of real examples from your business, questions, documents or calls, and what a good answer looks like.
How often should AI be re-tested?+
Every time the prompt, data or model changes. We set up automatic tests so this happens without extra effort.
Can you test a system that is already live?+
Yes. We can measure your current system and suggest specific improvements.
How long does an evaluation take?+
A first evaluation usually takes two to four weeks.
Tell us what you are trying to do.
We will tell you honestly whether AI Research is the right answer, and if it is not, which of the other seven is.