Stop experimenting. Start delivering

According to McKinsey (State of AI 2025), 88% of organizations use AI in at least one business function, but only 7% have truly scaled it across the enterprise. The diagnosis is familiar: scattered initiatives, committees without accountable owners, and experiments that go nowhere. Plenty of activity, but little direction.

AI is no longer an R&D topic; it is a governance issue. An AI strategy depends on three conditions: a clear prioritization framework, an accountable owner, and measurement discipline. While others launch scattershot requests for proposals, we set a clear direction. A company’s AI strategy cannot be outsourced to a vendor; it must be built alongside the teams who will carry it forward.

We structure your approach in stages: an AI and automation maturity audit to map the current state; a scoping workshop to identify the highest-leverage use cases; a technical feasibility study to assess the environment; and an outsourced AI steering committee to provide ongoing governance. Every deliverable is quantified, dated, and defensible before an executive committee.

You leave with a structured AI roadmap, evidence-based priorities, and an engaged owner. AI strategy stops being a slogan and becomes a leadership discipline.

According to BCG (AI at Work 2025), 72% of executives and managers regularly use generative AI, compared with just 51% of frontline employees—a glass ceiling that has remained unchanged for two years. The pattern is familiar: underused licenses, teams bypassing the official tool, and leaders discussing AI in executive committee meetings while no one uses it day to day. Only one in four frontline employees believes they receive enough support from management on the subject.

A successful AI transformation depends as much on the quality of the tools as on the maturity of their users. Building AI literacy among leaders so they can make informed decisions, training operational teams to develop the right habits, and supporting technical specialists so they can run production reliably: three audiences, three distinct learning approaches. While some offer a generic webinar, we orchestrate a journey tailored to each layer of the organization.

We work on four fronts: training for executives and leadership teams (strategic issues, real capabilities, and limitations); training for operational teams (prompting, security, and role-specific best practices); technical training for data and development teams (RAG, fine-tuning, agents, and MLOps); and AI change management (internal communications, adoption measurement, and resistance management). Every format is tailored to your real-world use cases.

Your teams embrace AI instead of having it imposed on them, tools are used instead of abandoned, and the transformation is driven from within. Adoption is not a side effect of deployment; it is the condition for success.

According to McKinsey (A Future That Works, updated in 2025), around 50% of today’s work activities can already be automated with existing technologies—a figure that rises to 60–70% when the capabilities of generative AI are included. Yet most organizations still leave dozens of high-potential processes untouched: manual invoicing, hand-managed customer follow-ups, reports rebuilt every month, and point-and-click synchronization across tools. IT spending soars while teams run out of time.

Automation is no longer a tools question; it is an architecture question. No-code workflows for simple automations, RPA for legacy systems without APIs, AI-workflow hybrids for processes that require judgment, and multi-system orchestration to synchronize the IT environment: each use case needs the right building block in the right place. While some stack tools, we build a coherent pipeline.

We deploy the scope that matches your maturity: no-code automations (Make, n8n, Zapier, Power Automate) for simple flows; RPA for legacy ERP systems and non-API interfaces; hybrid workflows combining LLMs and business rules for lead qualification, email routing, and document summarization; and multi-system orchestration to synchronize CRM, ERP, accounting, and marketing tools. Every automation is documented, tested, and monitored.

You free up your teams for higher-value work, improve data reliability, and measure ROI. Automation is not an IT expense; it is an operational asset that compounds over time.

According to Gartner and IDC, 80–90% of the data generated by businesses is unstructured: invoices, contracts, quotes, résumés, meeting minutes, correspondence, and scans. Yet barely 18% of organizations truly capture its value (IDC, Untapped Value, 2024). The rest is entered by hand, filed with a mouse, and summarized in meetings. The friction is invisible but constant.

Intelligent document processing is no longer just about OCR; it is about the pipeline. Structured extraction with modern OCR, classification and routing with specialized models, summarization and synthesis with LLMs, and human review through a dedicated interface: every stage of the pipeline requires the right technique. Where conventional OCR merely processes plain text, a well-architected document pipeline returns structured, validated, usable data. Our work on Validature, an electronic-signature platform built on Mistral OCR, taught us one thing: technical sovereignty starts with the choice of building blocks.

We cover the full chain: OCR and structured extraction using Mistral OCR or Google Document AI; automatic classification and routing of incoming flows; summarization and synthesis of dense material (reports, meeting minutes, and public procurement documents); contract analysis with clause extraction and anomaly detection; and document-translation pipelines with human post-editing and domain glossaries. Every pipeline is tested on your real data before deployment.

You process documents faster, improve data-entry quality, and make your archives usable. Documents stop being an administrative burden and become a source of actionable data.

According to Gartner (Predicts 2025: Agentic AI), 33% of enterprise applications will incorporate AI agents by 2028, up from less than 1% in 2024. The shift is dramatic: what was a proof-of-concept demo yesterday is becoming the standard software interface. Gartner nevertheless notes that more than 40% of agentic AI projects will be abandoned by 2027 because they lack sufficient business scoping or governance.

An AI agent is neither a chatbot nor a copilot; it is a new interface. An internal knowledge agent to query a proprietary document repository, an external customer-facing chatbot for first-line support, a voice agent to replace rigid interactive voice response systems, a business copilot embedded in the CRM or ERP, a writing copilot for marketing teams, or a development copilot to accelerate software delivery. While some deploy an off-the-shelf generic agent, we architect a conversational interface aligned with your data and safeguards.

We deploy the right agent in the right place: a rigorously engineered internal RAG agent (chunking, hybrid retrieval, and reranking) for support and business knowledge; an external customer-facing chatbot with safeguards and human escalation; a voice agent built on modern components (Retell, Vapi, or local infrastructure) for next-generation IVR; a business copilot integrated into your existing tools (CRM, ERP, CMS, and helpdesk); a writing copilot calibrated to your editorial guidelines; or a development copilot that integrates Codex, Cursor, Copilot, or Claude Code into your engineering teams. Every agent is tested, measured, and governed.

Your teams interact with their tools in natural language, your customers find answers without waiting, and your developers deliver faster. An agent is not a conversational gimmick; it is the new interface for enterprise software.

According to the Wavestone survey (Data and AI Leadership Executive Survey 2024, ex-NewVantage Partners), only 24% of Fortune 1000 companies consider themselves truly data-driven, even though more than 90% cite it as a strategic priority. The gap between rhetoric and practice comes down to a simple fact: the data is there, and so are the models, but decision-making is not becoming any smarter. Leadership teams invest in dashboards and continue to make calls on gut feel.

Predictive AI is not a breakthrough; it is a long-established discipline—statistics, econometrics, and classical machine learning—that businesses underuse. Predictive scoring to qualify a lead or anticipate churn, forecasting to adjust inventory or cash flow, anomaly detection to identify fraud or data-entry errors, and clustering to understand the true structure of a customer base. Generative AI creates excitement; predictive AI delivers returns.

We build every model from your business data: predictive scoring (churn, propensity, lead qualification, and risk scoring); forecasting (sales, inventory, cash flow, and traffic); anomaly detection (fraud, data-entry errors, suspicious behavior, and industrial process drift); and clustering and segmentation (customer segmentation, data-driven personas, and cohort analysis). Every model is validated on real-world cases, documented, and integrated into your management tools.

You make decisions based on quantified predictions instead of late intuitions, anticipate rather than react, and measure the return on every action. Predictive data does not replace judgment; it augments it.

According to Grand View Research, the global computer vision market is projected to grow from $19.82 billion in 2024 to $58.29 billion in 2030—an annual growth rate of 19.8%. Three sectors are driving the shift: manufacturing, which is automating quality control; retail, which is measuring customer journeys; and healthcare, which is expanding diagnostic capabilities.

Computer vision is no longer a laboratory topic; it is a production capability. Visual recognition for quality control, automated counting, and object classification; real-time video analytics to measure flows or detect events; and multimodal document vision to process text, diagrams, plans, and photographs together. Three families, three applications, three levels of complexity. While some promise generic visual AI, we calibrate specialized models for your real-world use cases.

We deploy to match the need: custom visual recognition (image classification, object detection, industrial quality inspection, and agricultural or logistics counting); real-time video analytics (flow counting, in-store behavior analysis, and security-event detection); and multimodal document vision (advanced OCR combined with analysis of diagrams, technical drawings, and product photographs—a natural extension of Document Intelligence). Every model is trained, validated, and deployed on infrastructure suited to your constraints.

You automate what human vision could not do at scale, measure what once relied on intuition, and make your controls more reliable. Computer vision does not replace expert eyes; it augments them with constant vigilance.

According to a RAND Corporation study (The Root Causes of Failure for Artificial Intelligence Projects, August 2024), more than 80% of enterprise AI projects fail to reach production—twice the failure rate of conventional IT projects. The number-one cause is not the model; it is the data: missing, poorly annotated, unversioned datasets contaminated by hidden biases. Gartner confirms that 63% of organizations either lack—or do not know whether they have—the right data-management practices for AI.

AI engineering is not about using APIs; it is about controlling the entire model lifecycle, from training data to production oversight. Building a proprietary dataset to create the foundational asset, annotating it at scale to establish quality, governing and versioning it for long-term use, fine-tuning an open model (Mistral, Gemma, Qwen, or Llama) on your business data, continued pretraining to go further, distillation to reduce inference costs, and proprietary evaluations and benchmarks to measure real performance. While others call an API, we build a durable technology asset.

We cover the entire chain: proprietary dataset creation (collection, cleaning, deduplication, and data cards); large-scale annotation (manual, semi-automated, and active learning); governance and versioning (DVC, DataHub, and traceability); fine-tuning of open models (LoRA, QLoRA, SFT, and DPO); continued pretraining on domain corpora (legal, medical, or industrial); model distillation (size reduction while preserving performance); and proprietary benchmark development with comparative multi-model evaluation. For exceptional cases, we also train models from scratch under a dedicated engagement.

You own a model that speaks the language of your business, traceable and compliant data, and a durable evaluation infrastructure. The AI model is no longer a service you consume; it becomes a proprietary technology asset.

According to Gartner, at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 because they lack production architecture, monitoring, or a maintenance plan. S&P Global’s 2025 survey confirms the trend: 42% of companies abandoned most of their AI initiatives this year, compared with just 17% one year earlier. The paradox is clear: organizations invest heavily in AI experimentation but little in scaling it for production. The result is a graveyard of proofs of concept.

Scaling AI for production is not an optional technical step; it is what keeps a project alive. Advanced RAG architecture for agents that query your data, multi-agent architecture for complex tasks, integration with existing tools to avoid duplication, an internal AI platform to share core components, GPU sizing to optimize inference costs, an MLOps pipeline for deployment and maintenance, and monitoring to detect drift. Every component has a role, and their coherence makes the whole resilient. While some deliver a model, we build a platform.

We work across seven fronts: advanced RAG architecture (graph RAG, hybrid retrieval, reranking, and intelligent chunking); multi-agent architecture (orchestration of specialized agents collaborating on complex tasks); AI integration into the existing IT environment (connections to CRM, ERP, CMS, and helpdesk systems through APIs or connectors); internal AI platform development (a unified, multi-use-case interface for business teams); GPU sizing and optimization (selection of H100, A100, or RTX hardware, quantization, and cost-per-token optimization); a complete MLOps pipeline (continuous deployment, model versioning, and model retraining); and AI monitoring and observability (drift, hallucinations, perceived quality, and usage costs). Every architecture is documented, testable, and ready for handover.

You move from proof of concept to production at scale, gain control of operating costs, and can hand over maintenance. AI stops being an expensive experiment and becomes a managed platform.

The European Union’s AI regulation (AI Act, Regulation (EU) 2024/1689), adopted on June 13, 2024 and phased in through August 2026, provides under Article 99 for penalties of up to €35 million or 7% of worldwide annual revenue for prohibited practices—a higher ceiling than under the GDPR. Organizations that have not begun mapping their AI systems against European risk levels have only a few months left before the first binding deadlines.

AI governance is no longer a legal-only concern; it is a cross-functional discipline. An internal AI policy to frame usage, AI Act compliance to classify systems by risk level, application security to counter prompt injection, jailbreaks, and data exfiltration, data governance aligned with the GDPR, external AI audits to demonstrate compliance, and human-in-the-loop oversight for critical cases. Another choice is becoming strategic: where to host the models. While some accept dependence on US cloud providers, we offer a path to French and European sovereignty—OVH virtual private servers, Scaleway sovereign cloud, open-weight models deployed locally, and on-premises infrastructure. Sovereignty is not a slogan; it is an architecture.

We cover the full scope: internal AI policy (charter, usage rules, governance, and ethics committee); AI Act compliance (risk-level classification, technical documentation, and impact assessment); AI application security (prompt injection, jailbreaks, data leakage, and model poisoning); data governance for AI (mapping, anonymization, pseudonymization, and GDPR alignment); external AI audit and certification (independent audits for insurance, public tenders, and certification); human-in-the-loop oversight (validation interfaces, escalation workflows, and active learning); sovereign AI and local hosting (open-weight models deployed on controlled infrastructure); migration from public to sovereign cloud (AWS, GCP, or Azure to OVH, Scaleway, or on-premises); and edge and on-device deployment (embedded models for full sovereignty).

You turn a regulatory constraint into a commercial advantage, keep critical data beyond the reach of foreign jurisdictions, and obtain the documentation required by insurers, customers, and public-sector contracting authorities. AI sovereignty is not a defensive cost; it is a differentiating asset for 2026.