Operate AI with clear guardrails, measurable outcomes, and a repeatable tuning cadence.
DCX helps teams define how AI should behave in production, how performance is measured, when humans remain in control, and how bots, assist tools, and automations are reviewed and improved over time.
What we build
Focused AI capabilities designed to fit into the same contact center, integration, analytics, and operating model your teams already use.
Guardrails & policies
Define approved use cases, handoff conditions, data-handling expectations, and human-review points.
Performance measurement
Track containment, escalation, success, failure, adoption, quality, and workflow-specific outcomes.
Fallback & escalation design
Create explicit retry, fallback, and handoff behavior so failures are safe and visible.
Knowledge & intent tuning
Improve prompts, utterances, knowledge, intent boundaries, and response patterns using production evidence.
Change & release governance
Establish testing, approvals, documentation, release practices, and rollback expectations.
Continuous optimization
Create a regular review cycle for transcripts, metrics, failures, feedback, and backlog prioritization.
High-impact governance and optimization practices
Governance should live inside the operating model—not in a separate document that never reaches production teams.
Use-case boundaries
Define where AI is approved, advisory, or not appropriate for the workflow.
Containment & escalation governance
Measure whether automation resolves, hands off, fails, or loops—and review those outcomes regularly.
No-match & no-input review
Identify recognition and conversational breakdowns that create poor customer experiences.
Knowledge quality review
Keep source content accurate, current, and structured enough to support reliable AI assistance.
Testing & change control
Use regression testing, approvals, UAT, and production validation around meaningful AI changes.
Operational review cadence
Turn metrics and quality findings into a prioritized backlog for tuning and improvement.
Governance is part of production readiness
For DCX, governance means the day-to-day discipline around testing, measurement, fallback behavior, data access, change control, documentation, and continuous optimization.
- Use-case governance— define approved scenarios, ownership, and where human judgment remains required.
- Data handling— document what data is used and how access is governed by the underlying platforms and agreements.
- Fallback & escalation— set retry limits, safe responses, and explicit human-handoff conditions.
- Measurement framework— define KPIs for containment, escalation, failure, adoption, quality, and customer outcomes.
- Change control— establish test cases, approvals, release practices, and documentation.
Our delivery process
Establish the operating model first, instrument the experience, then use production evidence to improve it on a repeatable cadence.
Establish
Define ownership, use-case boundaries, guardrails, success measures, and change-management expectations.
Instrument
Add the milestones, analytics, logging, and reporting required to see AI behavior in production.
Review
Evaluate failures, containment, escalation, quality, adoption, and operational feedback.
Improve
Tune prompts, intents, knowledge, workflows, and integrations, then validate and repeat.
FAQs
Quick answers to common questions about this AI capability.
Is governance just a policy document?
It should not be. Governance needs to show up in the production operating model through measurement, escalation rules, testing, ownership, change control, and regular review.
What should we measure?
The exact KPIs depend on the use case, but common measures include containment, escalation, success, failure, no-match, no-input, adoption, quality, and customer outcomes.
How often should AI be reviewed?
That depends on interaction volume and change frequency, but the important part is having an explicit cadence and backlog process rather than tuning only when something breaks.
Who should own AI after go-live?
Ownership should be defined before launch. It often spans contact center operations, platform administration, analytics, knowledge owners, and business stakeholders.
Ready to put an operating model around AI?
If your bots, assist tools, or automations are already live—or about to be—we can help define the guardrails, measurement, ownership, and optimization cadence needed to manage them responsibly in production.