For enterprise AI teams
Install a working process for AI reliability
Connect production AI, build a baseline, review what is failing, prioritize the next improvements, and give leadership clear evidence of progress.
- Connect
- Verify the application, data, and first real activity
- Baseline
- Current reliability signals and recurring failures
- Review
- Recurring technical review of what matters
- Prioritize
- Remediation backlog with owners and evidence
- Validate
- Check the same situation after the change
- Report
- Progress, gaps, and evidence for leadership
Who it is for
For AI that already matters to the business
Customer-facing AI in production
The AI is live and real people depend on the result — often across more than one workflow and more than one team.
High-impact or regulated workflows
The cost of a wrong outcome is not a support ticket.
Leadership asking for quality, risk, or ROI
Someone above you needs an answer backed by evidence, on a recurring cadence — implementation help and a review rhythm, not just a login.
What gets connected
Start with one important workflow
You do not need every source connected before value appears. One production or staging workflow is enough to establish a baseline.
- Production or staging AI workflow
- Historical conversations or traces
- Models, tools, and agent structure
- Knowledge sources
- Evaluation criteria
- User and session identifiers
- Existing engineering and governance systems
The report
A report that tells leadership what changed
Engineering gets the evidence. Leadership gets the trend. Both come out of the same activity rather than a separate reporting exercise.
- Reliability score
- Score and trend over the period
- Top failure groups
- Ranked by impact and repeat rate
- User intent changes
- What people started asking for
- Knowledge gaps
- What the AI could not answer
- Remediation
- Priorities and validation results
- Open risks
- Gaps and missing evidence
What the organization receives
Six deliverables, defined in the agreement
- 01
PRISM platform access
- 02
Guided setup and architecture review
- 03
Reliability baseline
- 04
Recurring technical review
- 05
Prioritized remediation backlog
- 06
Executive reliability report
Program boundaries
Enterprise scope is defined in the agreement. Planned capabilities, custom work, private deployment, Trust Packs, SSO, SCIM, and data-residency requirements must be confirmed before they are presented as active. The program does not automatically include unlimited custom engineering, unlimited connector development, automatic legal certification, autonomous repository changes, autonomous production remediation, or any planned capability not accepted as delivered.
Questions
Common questions
Is the AI Reliability Program a software plan?
It combines PRISM with an implementation and operating plan. The exact scope is defined in the agreement.
Can we start with one AI workflow?
Yes. The recommended path is to connect and validate one important workflow before expanding.
Does the program include compliance certification?
No. PRISM can support evidence and reporting. Legal interpretation, formal certification, and regulatory sign-off remain with the organization and qualified advisers.
Can Block Convey work with our existing observability system?
Yes, when the source and fields are supported. The connection plan should state what will be imported, what will be live, and what level of analysis is possible.
Does the program make changes to production automatically?
No. Remediation and deployment remain human-controlled unless a separately released and explicitly approved workflow says otherwise.
Understand the reliability of your production AI
Start with one workflow and a clear review of what is working, what is not, and what to improve first.