01
Benchmark
Measure useful performance on real tasks, not only demonstrations selected because the model already does them well.
eFind Research
AI Research
Models are moving quickly. Product judgment still has to keep up.
RESEARCH
The question
Models are moving quickly. Product judgment still has to keep up.
Research is where eFind is allowed to be uncertain on purpose.
The job is to turn a broad question into things we can test, measure, reject, improve and eventually—if the evidence is good enough—build into a product or infrastructure decision.
Curiosity needs a method.
AI Research
eIntelligence
FindUnderstandCreateAct
What we are exploring
Can intelligence be useful and honest at the same time?
The topics below describe areas of investigation, not guarantees about a future product.
CAPABILITY
Reasoning + synthesis
Study where models can reliably help people understand, compare, create and plan.
GROUNDING
Retrieval + sources
Connect generated answers to current information and the evidence behind them.
EVALUATION
Measure the failure modes
Accuracy, calibration, safety, latency, cost and user trust all need testing.
SYSTEMS
Efficient inference
Explore how models, hardware and infrastructure can deliver useful intelligence efficiently.
How research earns its keep
Research should end with fewer mysteries, not more slides.
02
Break it
Actively search for hallucinations, brittle reasoning, unsafe behavior and cases where the model sounds more certain than the evidence allows.
03
Trace it
Connect answers to retrieval, sources and system behavior so failures can be investigated rather than described as “AI being AI.”
04
Ship—or don’t
Turn evidence into a product decision. Sometimes the correct research result is that a feature is not ready, not useful or not worth its cost.
The standard
A model demo is not a product. Reliability is the bridge.
The models will keep changing. The durable capability is knowing how to evaluate them, connect them to evidence, use them efficiently and decide where intelligence genuinely improves a person’s experience.
Research questions
Better models are useful. Better systems are the real product problem.
Our AI research interests sit around retrieval, reasoning, tool use, multimodal interaction, evaluation, efficiency and the controls needed when intelligence touches personal context or the physical world.
Evaluation before applause
A demo can be persuasive and still fail on ordinary use. We care about whether a system retrieves the right source, follows instructions, handles uncertainty and improves the task it was supposed to help with.
Freshness is a systems problem
Models have training cutoffs. Search, news, weather and product data change. Reliable intelligence needs retrieval and tool use rather than confidence about information it does not actually have.
Efficiency changes what can be local
Smaller and more efficient models can move useful intelligence onto devices, reduce latency and lower infrastructure cost. Bigger is one dimension, not the strategy.