Operations
- LLMOps
- MLOps
- Prompt management
- Model routing
- Dataset updates
Intelligent Automation
Keep it accurate, fast, and affordable.
Ongoing operation of AI systems in production, evaluation, prompt and model management, cost and latency tuning, observability, and incident response.
- Fixed-scope discovery before any build commitment
- Integrates with the systems you already run
- Human approval on the actions that matter
- Measured after launch, not just at handover
The business problem
Why organizations ask for this.
AI systems drift. Content changes, usage patterns shift, providers update models, and costs creep. Without operational ownership, a system that launched well quietly gets worse.
Capabilities
What is included.
Evaluation
- Model evaluation
- RAG evaluation
- Agent performance monitoring
- Regression testing
Performance
- Cost optimization
- Latency optimization
- Logging and observability
- Maintenance
- Incident response
- Continuous optimization
Use cases
Where it earns its place.
Representative examples of how this service is applied. Your version starts from your own process, systems, and constraints.
- Monthly evaluation of answer quality against a fixed test set
- Routing simple requests to cheaper models to reduce spend
- Latency tuning for a customer-facing voice agent
- Regression testing before a provider model update
- Observability dashboards for usage, cost, and failures
- A defined incident process for AI-specific failures
Integrations
Connects to what you already run.
Elevariq selects models, platforms, and infrastructure according to security, scalability, cost, integration, and ownership requirements.
- Salesforce, HubSpot, Zoho, Microsoft Dynamics
- SAP, Oracle, Odoo and custom ERP
- Microsoft 365 and Google Workspace
- Slack and Microsoft Teams
- ServiceNow and helpdesk tools
- Accounting and finance platforms
- HR and recruitment platforms
- Shopify and WooCommerce
- Data warehouses and BI tools
- Custom databases and internal APIs
Delivery approach
Six stages, defined deliverables.
- Stage 01
Discover
Document the current process end to end, including the exceptions people work around.
- Stage 02
Design
Model the target workflow, decision rules, approvals, and measurement plan.
- Stage 03
Build
Develop the automation, extraction logic, or model, and test against historical cases.
- Stage 04
Integrate
Connect source and destination systems with the right permissions and error handling.
- Stage 05
Launch
Run in parallel with the manual process until accuracy and coverage are proven.
- Stage 06
Optimize
Review exceptions, expand coverage, and retrain or re-tune on a schedule.
Controls & quality
Built to be trusted in production.
Security, permissions, and human oversight are designed in from the first release rather than added after something goes wrong.
Permission mapping
Automations inherit the access rules of the systems they touch.
Exception queues
Anything the workflow cannot confidently handle is routed to a person.
Approval checkpoints
Financial, legal, and customer-facing steps can require sign-off.
Full audit trail
Each run records inputs, decisions, outputs, and who approved what.
Confidence thresholds
Low-confidence extractions and predictions are flagged, not applied silently.
Rollback and re-run
Failed steps can be retried or reversed without corrupting records.
Questions
AI Operations, answered.
Can you take over a system someone else built?
Yes, after a review of the architecture, prompts, data flow, and current performance.
How is cost reduced without losing quality?
Through model routing, caching, prompt efficiency, retrieval tuning, and removing calls that add no measurable value, each verified against your evaluation set.
What does ongoing support include?
An agreed scope of monitoring, evaluation, tuning, updates, and response times, reported monthly.
How does an engagement start?
With a strategy conversation about the problem, the systems already in place, and the outcome you need. If the scope is unclear, we run a short assessment first and come back with options.
Can we start small?
Yes. Most clients begin with one focused use case, prove it works, and expand from there rather than committing to a full programme up front.
NEXT STEP
Ready to talk about ai operations?
Tell us what is happening today, what it costs you, and what a better version would look like. We will come back with a practical next step.
Your first conversation focuses on the problem, feasibility, priorities, and potential business value.