Demo success does not equal production reliability
AI systems need supervision once real users, permissions, data changes and business expectations enter the picture.
Managed AI Services
Keep AI systems reliable, monitored and continuously improved.
WAICE supports organizations after deployment to keep AI systems operational by monitoring usage, incidents, answer quality, costs, performance, risks, evolution and adoption.
Operating reality
An AI solution that works in a demo can become risky in production if hallucinations, API cost drift, access control, incidents, answer quality and operational ownership are not monitored.
The challenge
AI systems need more than delivery.
After deployment, AI initiatives require monitoring, support, optimization, documentation and governance to remain reliable and useful.
Costs and quality drift
API usage, model behavior, hallucinations, answer quality and performance can drift without active monitoring.
Support ownership is unclear
Without support models, runbooks and escalation paths, incidents become difficult to qualify and resolve.
Adoption must be maintained
User feedback, training needs, access issues and changing processes must feed a managed improvement cycle.
What it is
A managed service layer for AI systems in production.
WAICE implements the support model, runbooks, indicators and review cycles required to turn AI into a durable capability: monitored, documented, secured and continuously improved.
The client receives AI systems maintained over time, with support, monitoring, cost control and continuous improvement.
What WAICE provides
Usage, platform, model, agent and cost monitoring
Track adoption, availability, model behavior, agent activity, API usage, cost drift and operational health.
L2/L3 support for AI solutions and integrations
Qualify incidents, support technical resolution and coordinate expert escalation across agents, copilots, platforms and integrations.
MLOps, LLMOps, AgentOps and runbooks
Maintain operating practices for models, prompts, evaluations, agents, workflows, releases and incident response.
Answer quality and hallucination control
Review answer quality, hallucinations, drift, user feedback and test results to guide corrections and improvements.
Governance, security and compliance reporting
Report on access rights, security controls, compliance posture, adoption, incidents and ROI.
Continuous improvement reviews
Turn monitoring, incidents, user feedback and business changes into a managed improvement backlog.
How it works
A practical engagement flow.
Each engagement is shaped around the operating context and expected outputs required to move from intent to usable capability.
01
Supportability Assessment
Assess architecture, components, dependencies, monitoring, security, governance and operational risks.
02
Transition & Industrialization
Co-develop the operating model, methods, monitoring, deployment practices, documentation and team enablement over a typical 3 to 6 month ramp-up phase.
03
Managed Service & Continuous Improvement
Operate, monitor, support, secure and continuously improve AI-enabled systems with the client over time.
WAICE adapts the engagement format to the client context: assessment, workshop, roadmap, architecture review, managed service or expert session.
Next step
Keep AI systems reliable, useful and improving.
WAICE helps organizations move beyond deployment with the operating routines, support model and improvement cadence required to sustain intelligence systems over time.