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Elyadata
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05Journey engagement

Production Build & Operate

Demos don't move businesses. Systems do.

We build production-grade AI and data systems integrated into your environment, workflows and technology stack. Our focus is not only on building the model or prototype, but on delivering a system that users can rely on in real business conditions, backend, frontend, integrations, evaluation, observability, governance, and the onboarding required for actual adoption.

Format

Full delivery team · Agile sprints · Continuous deployment · Operate phase

Team

Cross-functional squad sized to the use case, Data Engineers, Data Scientists, Software Engineers, ML Engineers, UX/UI, DevOps, led by a Solution Architect and Engagement Partner.

Best for

Organizations ready to move from prototype to operational system at enterprise scale.

Questions teams bring us· before they pick this engagement
  1. Q01

    How do we move from pilot to production without breaking what works?

  2. Q02

    How do we measure reliability and reduce operational risk?

  3. Q03

    How do we integrate human oversight where it matters?

  4. Q04

    Which teams need to own the system after launch?

What we do
  • Enterprise AI assistants and copilots
  • RAG and knowledge platforms
  • Agentic business workflows
  • AI-powered customer and employee experiences
  • Data pipelines and analytics platforms
  • ML and decision-support systems
  • Backend, frontend and integration layers
  • Monitoring, evaluation and governance setup
  • Deployment, QA and production-readiness processes
What you walk away with

  • Production-ready AI system integrated into your operations

  • APIs, data pipelines and user interfaces

  • Secure and scalable technical foundation

  • Evaluation and monitoring framework

  • Documentation and handover material

  • User onboarding and adoption support

Outcome

A working AI system embedded in real operations and ready to scale.

What ‘Operate’ includes· available as part of Build & Operate or as a separate managed service

  • LLMOps & MLOps

    Model and prompt versioning, deployment pipelines, rollback, A/B evaluation between versions, structured release management.

  • Quality monitoring

    Retrieval quality, hallucination rates, answer-grounding scores, evaluation against golden datasets, automated regression checks.

  • Cost & latency

    Per-request and per-feature cost tracking, latency percentiles (p50/p95/p99), token economics dashboards, anomaly alerting.

  • Drift detection

    Data, model and concept drift detection across inputs, outputs and downstream behaviour, with alerting and triage workflows.

  • Feedback loops

    User corrections, thumbs-up/down, escalation triggers and operator annotations, feeding a continuous-improvement backlog.

  • Incident handling

    Runbooks, alerting, on-call coverage where required, post-incident reviews, SLA-backed support for business-critical systems.

  • Governance review

    Periodic audits, EU AI Act and GDPR compliance reporting, model card maintenance, access-control reviews, traceability checks.

Connected case study

CAHPP, order processing from weeks to hours (-80%). Carrefour, automated IT ticket resolution from 17% → 30%. BMW, agentic immersive showroom converting digital interest into qualified leads.

What pairs with this· engagements often combined with this one
  1. 04Journey engagement

    Solution Blueprint

    Design the system before you build it. De-risk on paper, not in production.

  2. ACross-cutting enabler

    Embedded AI & Engineering Squad

    Senior delivery capacity, embedded in your team.

  3. BCross-cutting enabler

    Data Foundation & Human-in-the-Loop AI

    Build the data foundation required for reliable AI systems.

next step

30 minutes is enough to know whether we're the right fit.

We'll come prepared, ask hard questions, and tell you honestly if you should be talking to someone else instead.

Book a 30-min discovery