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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 usbefore they pick this engagement
  1. ENTRY 001

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

  2. ENTRY 002

    How do we measure reliability and reduce operational risk?

  3. ENTRY 003

    How do we integrate human oversight where it matters?

  4. ENTRY 004

    Which teams need to own the system after launch?

What we do
  • 01

    Enterprise AI assistants and copilots

  • 02

    RAG and knowledge platforms

  • 03

    Agentic business workflows

  • 04

    AI-powered customer and employee experiences

  • 05

    Data pipelines and analytics platforms

  • 06

    ML and decision-support systems

  • 07

    Backend, frontend and integration layers

  • 08

    Monitoring, evaluation and governance setup

  • 09

    Deployment, QA and production-readiness processes

What you walk away withtangible deliverables from this engagement

  • 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.

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 ‘Operate’ includesavailable 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.
What pairs with thisengagements often combined with this one
  1. 04

    Solution Blueprint

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

  2. A

    Embedded AI & Engineering Squad

    Senior delivery capacity, embedded in your team.

  3. B

    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