Monthly LLM evaluation and regression review
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Service
Managed AI & Optimization
PlanckCyber provides managed AI and optimization for organizations that already have AI systems in production or are preparing to operate a new one. AI applications and agents require more than uptime monitoring: model behavior can change, prompts and retrieval logic evolve, data shifts, costs move and new failure modes appear as usage expands. Managed services can include evaluation, regression testing, monitoring, prompt and model version control, incident review, cost optimization, security-control review and controlled expansion into adjacent workflows. The service can support systems built by PlanckCyber or, after technical review, existing systems that need evaluation, repair or improvement. The operating model is scoped around the risk and complexity of the system rather than a fixed public package.
Problems We Solve
When this service fits.
- An AI system is live but results are inconsistent
- Model or prompt changes create regression risk
- Usage and API costs are difficult to control
- The team lacks a repeatable evaluation process
- A successful pilot needs structured expansion and operating ownership
What We Can Deliver
Built around the requirement.
- Evaluation suite and acceptance thresholds
- Monitoring and incident workflows
- Prompt/model/version change process
- Cost and performance reviews
- Security and control checks
- Backlog for evidence-supported improvements
Use Cases
Concrete examples.
RAG retrieval-quality monitoring
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Agent exception and tool-use review
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Model migration validation
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Cost optimization across model calls and retrieval
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
How an Engagement Can Start
Use the smallest responsible starting point.
Existing-system evaluation
For an AI system that needs independent review or repair.
Post-launch operations
For a newly released system requiring ongoing control.
Expansion program
For a proven system moving into additional workflows.
Process
From definition to evidence.
- Baseline current behavior
- Define measures and thresholds
- Instrument monitoring and evaluation
- Review changes and incidents
- Improve only where evidence supports it
Data, Integration & Security
Constraints are design inputs.
- Production data handling
- Audit and change records
- Model/vendor updates
- Cost and latency budgets
- Escalation and human authority
Related Solutions
See the service applied to a business problem.
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Explore solutionFAQ
Questions buyers ask.
Can you manage AI systems built by another team?
Potentially. PlanckCyber first reviews architecture, access, documentation, security considerations and the current evaluation state to determine whether responsible support is feasible.
What do you monitor besides uptime?
Depending on the system, monitoring can include answer quality, retrieval quality, tool-use success, exceptions, latency, cost, model/version changes and business acceptance measures.
How do you decide what to optimize?
Changes should be tied to observed failures, user behavior, operating cost or new business requirements, then tested against defined acceptance criteria before release.
Start with the problem
Have a problem AI might solve?
You do not need a specification. Tell us what you are trying to improve.