Project Story: From correctness to intent understanding
A major AI client was scaling a conversational support system across multiple global markets, but a recurring issue emerged: users were receiving technically correct answers that still failed to resolve their problems.
As deployment expanded across languages and regions, the inconsistency became more pronounced. The same user intent was being interpreted differently depending on phrasing, tone, and cultural context.
This created a widening gap between benchmark performance and real-world success. While models appeared strong in evaluation, user satisfaction metrics told a different story.
The client needed a way to systematically identify when models were failing to understand intent— not just language accuracy.
Sigma’s approach: Structured intent intelligence
Sigma addressed this by treating intent as a structured evaluation problem rather than a classification task.
We deployed trained multilingual evaluators who interpreted user inputs across context, tone, and implied meaning. Their work was guided by structured rubrics that defined intent categories consistently across regions and use cases.
Through calibration loops and multi-layer QA, evaluator alignment was continuously refined to ensure consistency even in ambiguous or edge-case scenarios
Each judgment was supported by written reasoning, allowing teams to understand not just what failed, but why it failed — turning subjective interpretation into actionable intelligence.
Execution: Turning intent into structured data
The evaluation pipeline was designed to convert real user interactions into structured intent
signals at scale.
- Intent classification: Evaluators identify the underlying user goal, such as troubleshooting, escalation, or information seeking, while accounting for tone and context.
- Response alignment scoring: Each model response is evaluated based on how well it satisfies the inferred intent, not just its factual correctness
- Side-by-side validation: Responses are compared directly to surface differences in intent handling and resolution quality.
- Rationale capture: Evaluators document why a response succeeded or failed, creating a structured layer of explainability that feeds directly into model improvement.
Why intent alignment defines trustworthy AI
As AI systems move into high-stakes and regulated environments, intent alignment becomes essential.
Automated metrics cannot reliably determine whether a system understood a user’s goal, emotional state, or context. Only structured human evaluation can consistently measure and close that gap.
At Sigma, we build structured human evaluation systems that turn intent alignment into
measurable intelligence — helping teams understand not just what AI says, but whether it truly
understands
