Evaluation suites
Create test sets and scorecards for accuracy, groundedness, refusal behavior, and task completion.
MLOps & LLMOps
LLMOps and MLOps for governed production AI.
Ydhya builds the operating layer around AI systems: evaluation, monitoring, release control, incident review, cost visibility, and governance. This is what keeps a useful AI workflow from quietly degrading after launch.
Talk through this use caseThe buyer problem
AI systems fail differently from traditional software. Outputs drift, prompts regress, retrieval quality changes, model costs move, and nobody notices until users lose trust.
Operating systems we build
The work we take on
Prompt and model releases
Track changes, compare behavior, approve updates, and roll back when quality drops.
Production monitoring
Watch latency, cost, failures, user feedback, drift, and review queues.
Delivery model
How Ydhya delivers it
Define quality
We translate business risk into concrete evaluation criteria and examples.
Instrument the system
We add logging, feedback, tracing, scoring, cost reporting, and alerting.
Run the cadence
We establish reviews for regressions, drift, incidents, and continuous improvement.
The production bar
What makes it production-ready
No blind launches
Every material behavior change needs a comparison before rollout.
Quality is visible
Stakeholders can see what is improving, failing, and costing money.
Governance has evidence
Approvals, incidents, and model changes have a record.
Next service
Voice AgentsIf AI is already live, it needs an operating model.
We can add evaluation and monitoring around an existing system or build it into a new one from day one.
Contact us