Cevyra AI
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HOW WE WORK

From a business problem to everyday use.

Explore Cevyra’s approach to discovery, proof of concept, pilots, integration and deployment, with business value and operational requirements at every stage.

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12 — A CLEAR PATH, TOGETHER

From first idea to working AI.

Prove value within a focused scope. Validate with real users. Then expand with confidence.

Explore the details
  1. 01

    Discovery

    We review workflows, time-consuming tasks and data sources with your teams. Together, we define success measures, access boundaries and technical requirements.

  2. 02

    Use Case Selection

    We assess candidate use cases by business value, data readiness and implementation effort. We agree on the scope and measurable objectives for the first application.

  3. 03

    Proof of Concept

    We build a working proof of concept using a limited dataset. We test answer quality, source accuracy and the technical feasibility of the selected use case.

  4. 04

    Pilot

    A selected group of users tests the system against real work questions. We review their feedback and results against the success measures agreed at the start.

  5. 05

    Integration

    We connect your ERP, CRM, databases and other relevant sources. We configure permissions, data update workflows and the human approvals each process requires.

  6. 06

    Production

    After acceptance checks are complete, we release the system for operational use. We prepare your teams and put monitoring and support processes in place.

  7. 07

    Optimization

    We review usage, unsuccessful queries and user feedback. We improve source coverage, answer quality and workflows based on measured results.

01

01–02 — Discovery & use case selection.

We work with process owners to understand current workflows, recurring tasks and data sources. The first use case is selected by business value, data readiness and risk. Baseline measures and success criteria are agreed before development begins.

  • Stakeholder and process interviews
  • Data and access assessment
  • Priority use case and success measures

Output: a defined problem, scope and measurement plan.

02

03–04 — Proof of concept & user pilot.

The proof of concept tests whether the technical approach is viable. In the pilot, real users complete their own tasks with the system. We assess answer quality, usability, failure handling and contribution to the work itself.

  • Initial validation with representative data
  • A pilot using real questions and tasks
  • A decision to proceed or refine

Output: observed results to replace untested assumptions.

03

05–06 — Integration & deployment.

We complete the required system connections, permissions, monitoring and error handling. Training, support ownership and rollout plans are established. Deployment follows the acceptance criteria agreed for the project.

  • ERP, CRM and API connections
  • Access and acceptance checks
  • Training, documentation and rollout

Output: a working system with defined operational responsibilities.

04

07 — Learn from use & improve.

Live use reveals new question types, missing sources and workflow exceptions. We review feedback alongside quality, cost and latency data, prioritising further development according to the business value demonstrated.

  • Quality and usage review
  • Source and evaluation set updates
  • Priorities for new teams and use cases

Output: an AI system that evolves for measurable reasons.

YOUR NEXT STEP

Let’s find where AI can make a real difference in your business.

Tell us about your processes, data and challenges. Together, we can identify a practical first use case.

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