Data and AI are strategic priorities for most organisations.
The main obstacles are rarely technological.
Successful Data and AI programmes depend on four foundations: clear scoping, reliable data, effective governance and sustained business adoption. Meritis helps organisations address each of these areas, from initial assessment through to production.
Why do AI proofs of concept fail to reach production?
Meritis assesses AI code against more than 300 engineering rules across eight domains. Within two to three weeks, you receive a clear view of the blockers, technical risks and actions required to move towards production.
→ Explore our assessment and scoping services
How can organisations protect AI investment and establish effective data governance?
Meritis defines a value-led AI strategy, prioritises use cases and establishes the governance needed to manage data, generative AI and LLM usage securely and responsibly.
→ Explore our strategy and governance services
How can an organisation assess whether its data is ready for AI?
Meritis assesses data quality, accessibility, governance and architecture, then designs AI-ready Data Platforms that support Machine Learning, analytics and generative AI from the outset.
→ AI awareness and ideation
How can organisations build reliable generative AI solutions that employees will adopt?
Meritis designs and develops secure RAG solutions, business AI agents and Machine Learning models, from feasibility assessment and prototyping through to production monitoring and user adoption.
→ Explore our AI development services
How can organisations build AI awareness and adoption across their teams?
Meritis delivers executive briefings, ideation workshops and tailored AI training that align leadership and business teams before investment decisions are made.
→ Explore our AI awareness and ideation services
How can organisations secure generative AI and measure AI return on investment?
Meritis defines LLM and RAG usage policies, GDPR controls, risk indicators and value KPIs during scoping, so that AI investment can be governed, measured and linked to business outcomes.
→ Explore our strategy and governance services
What makes a Data & AI project succeed?.
Data and AI projects are most likely to succeed when they begin with a clearly defined business need, measurable value and a realistic path to adoption. Technology matters, but strategy, data quality, governance and production readiness determine whether an initiative delivers lasting results.
- 1. A clear strategy linked to measurable business valueBefore resources are committed, organisations need to define the business problem, expected value, decision criteria and success metrics. This makes use cases easier to prioritise, fund and govern, and prevents disconnected AI experiments.
- 2. Reliable, accessible and AI-ready dataAI-ready data must be sufficiently accurate, representative, accessible, documented and governed for the intended use case. Data quality directly affects model performance, reliability and regulatory risk.
- 3. Production engineering, governance and adoptionMoving from proof of concept to production requires secure architecture, integration, MLOps, monitoring, change management and clear ownership. Meritis combines consulting, engineering and adoption support to cover this transition end to end.
Business outcomes supported by our Data & AI approach
- Focus your investment on the highest-impact use cases
- Avoid proofs of concept that go nowhere through clear scoping from the outset
- Strengthen decision-making through appropriate Data & AI governance
- Improve the reliability of your data, architectures and implementation conditions
- Accelerate the transition from experimentation to production
- Enable responsible AI adoption by giving teams the guidance they need to use it methodically and with sound judgement
- Embed AI into day-to-day practices to make it a sustainable driver of performance
Meritis is a Data & AI consultancy and engineering partner helping organisations define strategy, govern risk, modernise data foundations, develop AI solutions and move them into production.
Meritis Data & AI consulting and engineering services
Meritis supports organisations at every stage of their Data and AI journey, from initial diagnosis and strategy to solution development, Data Platform engineering, production deployment and adoption. Each service can be delivered independently or as part of an end-to-end programme.
1. Data & AI assessment and AI code audit
You may not know where to begin, or you may already be making progress without a clear view of the actual state of your data and AI projects. Our assessment covers both dimensions: the maturity and quality of your data, and the technical robustness and engineering of your AI project.
Within two weeks, you receive a clear view of what is holding you back and a roadmap for moving forward with confidence.
This assessment is suitable both for organisations starting their AI journey and for teams reviewing an existing initiative before further investment.
- Data & AI maturity assessment
- AI code audit (300+ rules across eight domains)
- AI Engineering maturity matrix
- Feasibility assessment and data prerequisites
- Functional and technical scoping
- AI risk assessment
- Short and medium-term maturity roadmap
Banking
Code assessment: generative AI project
Assessment against more than 300 engineering rules and an AI Engineering maturity matrix covering eight domains.
Short and medium-term productionisation roadmap for the internal AI Lab.
Wealth management
Feasibility assessment and Machine Learning proof of concept
Assessment of data prerequisites, solution design and a proof of concept using three ML algorithms for an investment recommendation system.
2. Data & AI strategy, governance and change management
Launching multiple AI initiatives without a shared architecture creates strategic debt. We help organisations move from a fragmented portfolio of projects to a coherent Data & AI strategy driven by business value and supported by secure usage practices.
Our work can include a Data & AI master plan, use-case portfolio, AI governance, an LLM usage framework, GDPR and security controls, change management and ROI measurement.
- Data & AI master plan
- AI governance and LLM usage framework
- Use-case prioritisation
- GDPR compliance and security
- Change management
- ROI measurement and adoption
Social housing
Three-year AI roadmap
Definition of a progressive AI pathway aligned with business strategy and risk management. Support for senior leadership in decision-making and the governance of AI use.
Online gaming (French market leader)
Cloud Data Platform architecture
Design of a scalable Data Platform for descriptive and predictive analytics and fraud detection. Target data operating model and implementation roadmap.
3. Generative AI, RAG, AI agents and Machine Learning development
Meritis develops secure generative AI and Machine Learning solutions using your data, architecture and security requirements. Our Innovation team, which includes PhD-qualified AI specialists and researchers, designs RAG architectures, business AI agents and predictive models from feasibility assessment through to production.
- Secure RAG using internal data
- Agents IA & chatbots métier
- Machine Learning & modèles prédictifs
- POC & MVP IA
- LLM fine-tuning
- MLOps & monitoring production
OpenRAG: Meritis’ open-source RAG benchmarking framework
Meritis developed OpenRAG, an open-source framework for benchmarking Retrieval-Augmented Generation systems, and published it on GitHub.
OpenRAG supports transparent and reproducible evaluation of RAG systems. Publishing the framework openly also demonstrates the engineering and research standards applied by the Meritis Innovation team.
Our approach is technology-neutral: we build auditable, maintainable solutions based on open standards rather than locking clients into a proprietary product.
Download OpenRAG from GitHub
Insurance
Secure RAG for internal document repositories
Design and development of a modular RAG tool with differentiated processing based on data confidentiality.
Deployment to business teams, with adoption measured.
Cosmetics
RAG for internal knowledge transfer
RAG prototypes for preserving and retrieving internal expertise. Change management and training for user teams.
4. AI awareness, executive education and use-case ideation
AI adoption depends on shared understanding, relevant use cases and clear ownership. Meritis helps leadership and operational teams understand what AI can achieve, identify feasible use cases and build a prioritised backlog owned by the organisation.
Programmes can be designed for executive committees, senior managers or operational teams and may include briefings, Design Thinking workshops, prioritisation sessions and bespoke training.
- Executive conferences and workshops
- Design Thinking and AI ideation
- Bespoke thematic training
- AI eligibility matrix
- Prioritised use-case backlog
- Progressive AI roadmap
Social housing
AI awareness programme for executive leadership
Themed conferences and Design Thinking workshops with 40 managers. Identification and prioritisation of AI use cases. Three experiments launched into production. A sustainable internal training programme established.
Maritime transport
AI awareness and agile Data Science coaching
Support for the Acceleration Team on AI culture, tool adoption and the effectiveness of Data Science projects in production.
5. AI-ready Data Platforms, Data Engineering and BI & Analytics
Meritis designs Data Platforms around the analytics, Machine Learning and generative AI use cases they need to support. AI-ready architecture, governed pipelines and reliable data access are built in from the outset.
Our BI and analytics specialists turn governed data into dashboards, business analysis, consolidated reporting and predictive insights. We work across AWS, Google Cloud and Microsoft Azure, as well as leading modern data platforms. Technology choices are driven by business use cases, security requirements and the target operating model.
- Data Lake, Lakehouse and Data Warehouse
- ETL/ELT pipelines and streaming
- Data quality and governance
- BI dashboards and reporting
- Predictive analytics
Construction (leading independent French group)
Data Platform audit and Snowflake proof of concept
Target data architecture, governance plan, target operating model and a Snowflake proof of concept validating the technology choices while supporting team capability development.
Energy (multinational)
Multi-source AWS Data Lake
Development of an AWS Data Lake with ingestion from multiple sources, including Oracle, Salesforce and SAP, alongside pipeline implementation and transformation-layer design.
Not sure where to begin?
A 30-minute discussion with a Meritis expert is enough to clarify your situation and identify the first practical actions to take.
Why choose Meritis for Data & AI?
Meritis combines Data & AI consulting, research, engineering and hands-on delivery within one organisation. This allows clients to move from strategy and governance to development, production deployment and adoption without changing partners.
Data & AI Consulting Team
Strategy, architecture and governance. They assess your situation, formalise the vision and steer complex projects throughout their lifecycle.
Generative AI Innovation Team
PhD-qualified AI specialists and researchers, R&D activity and scientific publications. Creators of OpenRAG. Cutting-edge research translated into secure, operational solutions.
Data & AI Practices
Structured communities of expertise across BI, Data Engineering and Data Science. Delivery specialists available for assignments, team augmentation or project leadership.
Hands-on Data & AI Consultants
They work within your teams, using your tools and operating in your real-world environment. Available for assignments or team augmentation to build and deploy solutions over the long term.
Examples of Data & AI projects delivered by Meritis
The following examples illustrate how Meritis supports organisations across strategy, AI engineering, Data Platforms, governance and adoption.
Social housing
AI awareness programme and three-year roadmapForty managers supported. Three AI use cases launched as experiments. A progressive AI roadmap aligned with business strategy.
Energy (multinational)
AI automation for international document managementDevelopment of an AI tool to standardise invoice processing across multiple countries and regulatory environments. Time savings for employees and greater consistency in the international brand experience.
Banking
Code assessment for a
generative AI projectAI Engineering maturity matrix covering more than 300 rules across eight domains. Productionisation roadmap for the internal AI Lab.
Insurance
Secure RAG for internal document searchModular RAG tool with differentiated processing based on confidentiality. Improved efficiency for business teams working with document repositories.
Financial services
Automation of customer scoring and KYC onboardingAn agentic framework embedded within existing KYC processes, providing multi-source scoring, weak-signal detection and actionable insights for compliance teams.
Let us discuss your
Data & AI priorities
An initial 30-minute discussion with a Meritis expert to clarify your situation, identify the first practical actions and direct you towards the right approach.
Data & AI white papers and resources
Frequently asked questions about Data & AI consulting
A Data & AI maturity assessment should review data quality and accessibility, architecture, governance, internal capabilities, AI engineering practices, risk management and adoption. Meritis offers a five-day rapid assessment or a comprehensive audit lasting two to four weeks. The output identifies priority prerequisites, accessible use cases and the actions required before further investment.
A practical starting point is an AI awareness and ideation programme. This clarifies what AI can achieve in the organisation’s context, identifies feasible and valuable use cases and creates a prioritised backlog. Meritis typically completes this stage within two to three weeks, producing a roadmap validated by business teams and leadership.
An AI proof of concept tests feasibility and potential value within a limited scope. A production-ready AI solution is secure, scalable, monitored, integrated with existing systems and supported by clear ownership and operating processes. The transition requires architecture, MLOps, security, governance and change management.
Secure generative AI requires a clear usage policy, access controls, data protection, output evaluation, human oversight, auditability and appropriate architecture. Meritis helps organisations implement private-data RAG, locally hosted open-source models where relevant, GDPR controls and AI governance. Our Innovation team also developed OpenRAG, an open-source RAG benchmarking framework.
The exact prerequisites depend on the use case, but most AI projects require representative data, sufficient quality, reliable access, documented ownership, security controls and an architecture that supports the target workload. A data maturity assessment identifies gaps and priority actions before development begins.
Data and AI ROI can include direct gains such as productivity, error reduction, automation and quality improvement, as well as indirect gains such as better decision-making, lower risk and competitive advantage. Meritis defines value indicators during scoping, builds a value-versus-effort matrix and creates a monitoring model suited to the organisation.
We work across all sectors, including banking, insurance and asset management, financial services, energy, social housing, industry, cosmetics, retail, online gaming and the public sector. Through Neofin Advisory, the Group’s specialist entity, we also support financial services businesses with AI solutions tailored to regulatory and compliance requirements.
A Data & AI consultancy focuses on strategy, governance, operating models and business value. A technology integrator focuses primarily on implementation. Meritis combines both capabilities, supporting clients from assessment and strategy through to engineering, production deployment and adoption.
An end-to-end Data & AI partner should demonstrate capability across strategy, data, development, productionisation, governance, compliance and change management. Organisations should also look for evidence of solutions that have reached production, transparent delivery methods and the ability to transfer knowledge to internal teams.
Speak to a Data & AI expert
1 Arun Chandrasekaran, Gartner, “Why 50% of GenAI Projects Fail – And How to Beat the Odds”, January 2026.