Managed AI Operations
Operate AI systems with ongoing control over quality, cost and change.
KavaCore provides recurring operational ownership for production AI systems so model behavior, evaluation, usage, cost, incidents and system changes remain visible after launch.
Capabilities
What KavaCore can design, build and operate.
The engagement is shaped around the business outcome, existing systems, security requirements and operating economics.
Quality monitoring
Track representative task quality and regression signals over time.
Model operations
Manage model changes, routing decisions and provider dependencies.
Prompt & configuration control
Treat behavioral changes as versioned production changes.
Cost visibility
Monitor usage, token economics and high-cost workflow patterns.
Incident response
Investigate failures, degraded behavior and dependency issues.
AI governance support
Maintain documented controls, ownership and review paths for supported systems.
Problems we solve
Start with operating friction, not a technology label.
AI quality drifts
Behavior changes as models, prompts, data and usage evolve.
No one owns model changes
Provider upgrades and configuration changes reach production without enough testing.
Cost grows invisibly
High-volume workflows consume model spend without a clear value or routing strategy.
Failures are difficult to explain
Teams lack traces, evaluations and context to understand what happened.
Architecture
Treat AI behavior as a production surface that needs operations.
Managed AI combines evaluation, telemetry, model and prompt configuration, dependency monitoring, cost analysis and incident workflows. The goal is controlled change with enough evidence to improve the system safely.
Use cases
Applications where this capability can create meaningful leverage.
AI agents
Operate task-oriented agents with traces, intervention metrics and tool-call visibility.
RAG systems
Monitor retrieval quality, freshness and answer grounding.
Enterprise copilots
Track adoption, quality and permission-sensitive behavior.
Document intelligence
Monitor extraction accuracy, exceptions and processing cost.
AI-enabled software
Own model-backed product features as part of production operations.
Automation workflows
Measure AI decision quality before downstream actions occur.
Delivery model
Baseline
Document models, prompts, evaluations, dependencies, cost and operating risk.
Instrument
Add the telemetry and tests required for production visibility.
Operate
Review quality, changes, incidents and usage on a recurring basis.
Improve
Tune routing, prompts, retrieval, workflows and controls using evidence.
Production discipline
AI operations turns model uncertainty into a managed engineering process.
The system does not become static after launch. Managed operations create a disciplined loop for detecting change, testing improvements and controlling production behavior.
Evaluate meaningful model, prompt and retrieval changes before production release.
Track representative tasks rather than relying only on user complaints.
Use routing, caching and workflow design to align model spend with business value.
Related capabilities
Connect this capability to the broader operating system.
Start with the outcome
Give production AI a clear operating owner.
Share the AI systems already in use, how quality is measured today, where failures occur and what the business needs to trust. We can define the operating layer around them.