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DATAKOOCONSULTING
Data, AI & cloud consulting · Paris

Your data.
Clear decisions.
A step ahead.

We connect business challenges with the power of data: meaningful indicators, practical AI applications and platforms designed to evolve.

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From strategy to delivery For business and technical teams
Six complementary disciplines

The right expertise.
Where it matters.

From assessment to operations, shape an engagement around your organisation’s needs and maturity.

01

Data analytics & Business Intelligence

Reliable indicators. Clearer decisions.

Explore this expertise ↗ 02

Data science & artificial intelligence

Turn AI potential into a useful application.

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Data engineering & cloud

A data chain designed to evolve.

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Data governance & strategy

Make data a shared business asset.

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DataOps, MLOps & DevOps practices

Deliver, operate and improve with method.

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Data platforms & infrastructure

A platform aligned with your needs.

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From intention to practical use

A clear path
through the project.

Explicit stages, shared decisions and documentation that supports the deliverables.

01 — Understand

Frame the need

Business objectives, users, data sources and constraints. Define scope and success criteria before selecting tools.

02 — Design

Choose a direction

Scenarios, architecture, priorities and dependencies. A roadmap makes decisions and trade-offs visible.

03 — Build

Deliver in stages

Development, testing and user feedback. Evaluate deliverables against agreed acceptance criteria.

04 — Transfer

Prepare continuity

Documentation, knowledge transfer and operating requirements. Define post-delivery support in the mission scope.

Build today.
Enable tomorrow.

A solution must be understood, used and maintained. Knowledge transfer is a topic to define from the start.

For team training, explore the programmes offered by DATAKOO Training in data analysis, programming and artificial intelligence.

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Business applications

A concrete need.
A project you can evaluate.

Illustrative project scopes, to be adapted after assessment. These are not completed client projects or promises of results.

Commercial performance

Align sales reporting across teams.

Input data
CRM, orders, revenue and product reference data.

Deliverables
Shared KPI definitions, a documented model and a management dashboard.

Evaluation criteria
Reconciliation with source totals, freshness and user acceptance.

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Demand forecasting

Anticipate demand while understanding forecast uncertainty.

Input data
Historical demand, seasonality, promotions and stock availability.

Deliverables
A baseline, an evaluated model and an operational forecast view.

Evaluation criteria
Forecast error by segment, baseline comparison and model drift.

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Automated reporting

Replace fragile manual consolidations with monitored data flows.

Input data
Business applications, files and agreed ingestion interfaces.

Deliverables
Versioned pipelines, quality tests, orchestration and operating documentation.

Evaluation criteria
Data completeness, delivery time, error handling and recoverability.

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Document search with generative AI

Help teams find information in an authorised document collection.

Input data
Approved documents, access rules and representative questions.

Deliverables
A retrieval prototype, source citations and an evaluation protocol.

Evaluation criteria
Answer relevance, source traceability, access controls and unsupported answers.

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Trusted business indicators

Resolve conflicting definitions and ownership of key information.

Input data
Existing reports, business definitions and source inventories.

Deliverables
A glossary, ownership matrix and prioritised quality plan.

Evaluation criteria
Definition coverage, ownership clarity and agreed quality checks.

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Data platform modernisation

Plan an evolution without losing control of dependencies and costs.

Input data
Current architecture, workloads, access requirements and cost assumptions.

Deliverables
Scenario comparison, target architecture and staged migration plan.

Evaluation criteria
Migration reconciliation, operating readiness and estimated running costs.

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Engagement framing

A clear scope.
Explicit responsibilities.

The delivery model is discussed according to your needs, team structure and project maturity.

Assessment & roadmap

Clarify priorities, available data and dependencies before committing to implementation. Define the assessment scope, interviews and decision documents.

Project delivery

Agree scope, milestones, deliverables, acceptance criteria and the change process. Pricing and schedule depend on the assessed scope.

Team collaboration

Define required skills, working arrangements, responsibilities and knowledge transfer. Availability and collaboration terms are confirmed in the proposal.

Before delivery: named stakeholders, access rules, confidentiality requirements, assumptions, acceptance process and operating responsibilities.

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Your next chapter

A business challenge.
A conversation to move forward.

Tell us about your context, priorities and open questions. Let’s define the right starting point.

Discuss my project

Learn how we helped 100 top brands gain success