Find the objective
Interpret the outcome you want and identify the constraints that could change it.
A consumer intent layer for AI · In development
You know what you mean.
AI should, too.
You shouldn’t need to engineer a prompt to get a useful answer. rikai is building the intelligence layer that turns what you say into what you actually want accomplished.
See the idea in actionYour wordsUnderstandingYour outcome
01 / The experience
Less back-and-forth.
More “that’s it.”
A simple request can hide the details that matter. rikai’s planned experience identifies consequential ambiguity and asks only the questions worth your time.
Interactive concept illustration. This example uses preset content; it does not call an AI model or recommend real products.
rikaiConcept preview
A clearer task, behind the scenes
Choose a budget and primary use to see how two details change the task.
Your goal stays yours. The task becomes clearer.
02 / Our approach
Understand first.
Then execute.
We’re designing a system that closes the gap between human intent and AI execution.
Clarify selectively
Use relevant known context when available. Ask zero to two high-value questions when the expected improvement justifies another interaction.
Make the task precise
Turn the enriched intent into a structured task specification for the model, tools, and context it needs.
Check the outcome
Evaluate the answer against the original objective and repair it before delivery where possible.
The measure that matters
First-Shot
Success.
Did you get what you actually wanted?
Our north-star metric is the share of requests resolved without a correction, substantial rewrite, repeated regeneration, or long clarification loop.
We plan to compare ordinary AI interactions with intent-enriched ones, measuring successful outcomes alongside turns, time, and tokens per resolved task.
A validation goal. No performance results claimed.03 / The vision
Users shouldn’t have to learn
how to talk to AI.
AI should learn how to
understand users.
Our long-term goal is a model-neutral personal intent layer: understanding what you mean, knowing which context matters, and choosing how your goal should be executed across models and tools.
Intent Engine What do you want?Context Engine What information matters?Execution Engine How should it get done?
What we’re building next
Starting with intent.
Learning from outcomes.
rikai is at the concept stage. The immediate goal is to validate selective clarification and intent resolution on everyday consumer requests.
We plan to use Claude for intent interpretation, structured task extraction, clarification decisions, enriched task execution, and outcome evaluation. Over time, acceptance and correction signals could teach rikai which questions, context, and execution strategies actually help.