AI Research & Feasibility
You have a possible use for AI, but need to know whether the data, approach and business case justify a prototype.
Explore feasibility studies ↗Engagements
Resolve the question before the investment.
Find out whether an AI idea is practical, test its essential behaviour or examine why an existing system gives unreliable answers.
AI Proof of Concept
You have a defined question. Build a limited experiment and test it against a simpler alternative before committing to a full product.
Explore proofs of concept ↗
Model Evaluation & Benchmarking
You have a model or system to assess. Examine accuracy, source support, failure cases and practical trade-offs on an agreed task.
Explore evaluation ↗Choose the next decision.
“We do not know whether this is worth building.”
Start with feasibility. The useful output is a recommendation with reasons, including what would need to be true before an experiment is worthwhile.
“We need to see whether the essential idea works.”
Start with a proof of concept. Agree the assumption and test criteria first, then build only what is needed to investigate it.
“We need evidence about the system we already have.”
Start with evaluation. Separate finding the right information from using it correctly, and examine consequential errors rather than relying on one average score.
Evidence you can inspect.
Each engagement has a defined question, permitted material and agreed deliverables. A useful result may support a build, suggest a narrower experiment or recommend stopping.
Our worked answer-review example shows the level of traceability we mean. It is an illustrative teaching example with invented source material, not a customer case or model benchmark.
Read how an engagement runs and who we work with. For a production software or automation implementation, Softomate Solutions is the implementation business; Manas Labs focuses on the research and evaluation question.