Regressions hide in production
A prompt or model change can alter customer outcomes without breaking a test.
Measure AI quality like software quality.
Continuously evaluate production conversations, catch regressions, and connect every quality signal to the evidence behind it.
The visibility gap
What your automation metrics leave out
Automation increases capacity. It can also put more distance between AI engineers and the customer experience.
Evaluation stops before deployment
Offline test sets cannot represent every customer, topic, and production edge case.
Quality has no shared definition
Teams compare systems without a consistent measure of a good support outcome.
Deflection rewards the wrong behavior
Keeping a conversation automated does not mean the answer was accurate or helpful.
Production quality regression
Find production regressions before they spread
Detect quality changes in production, isolate the affected conversations, and verify that each fix improves the customer outcome.
Continuous production evaluation
Evaluate eligible production conversations across resolution accuracy, customer effort, handoffs, and policy behavior instead of relying only on point-in-time test sets.
Regressions with context
See when quality changes across prompts, models, topics, or workflows, then isolate the conversations that explain where production behavior shifted.
Evidence to verify a fix
Trace each finding to its evaluation explanation and source conversation, make the smallest useful change, and confirm that customer outcomes recover afterward.
How ai engineers work
A practical path from finding to improvement
Treat customer-facing AI as a production system with a measurable quality loop.
A production quality dimension drops
CraftCX shows the quality change and the conversations behind it.
Compare AI configurations and failed conversations
Inspect the affected conversation type, score explanations, and policy findings.
Change the prompt, model, or workflow
Use production evidence to target the failure instead of guessing broadly.
Relevant capabilities
The parts of CraftCX closest to your work
The platform stays the same. Your view starts with the signals and decisions closest to your role.
AXIS evaluation
Measure resolution accuracy, interaction effort, and handoff quality consistently.
Continuous monitoring
Evaluate production conversations instead of relying only on test sets.
Policy compliance
Check production behavior against the guidance each agent should follow.
Regression detection
See when quality changes across prompts, models, topics, or workflows.
Confidence runs on CraftCX.
See how continuous observability gives AI engineers the evidence to improve AI-powered support.