AI, LLMs & Automation

AI, LLMs & Automation for companies

We support companies in their AI projects, from strategic framing to production rollout. We design useful, safe and auditable systems: LLMs, RAG over internal knowledge, machine learning, deep learning, computer vision, connected agents, IoT and supervised automations.

LLM RAG Machine learning Deep learning Vector search Computer vision IoT MLOps
AI & Automation
Step 1

Frame the right use case

Business workshops, data analysis, prioritization by ROI, risk and feasibility to avoid attractive but unusable POCs.

Step 2

Prototype with guardrails

RAG, prompts, agents, interfaces and integrations tested on real flows, with quality measures and human validation.

Step 3

Industrialize and transfer

Progressive rollout, observability, security, costs, runbooks and training so your teams stay in control.

AI and systems capabilities

We do not reduce AI to a conversational interface. We also work on models, data, architecture and real-world constraints.

Machine learning & deep learning

Predictive models, classification, scoring, NLP, time series, training, fine-tuning and evaluation.

Vectorization & document search

Embeddings, vector indexes, hybrid search, RAG, chunking, reranking, citations and relevance measurement.

Computer vision

Image analysis, detection, OCR, quality control, PyTorch/TensorFlow pipelines and business validation.

Architecture & infrastructure advisory

Target architecture, cloud/on-premise, MLOps, security, costs, scalability, system integration and operations.

IoT, edge & hardware

Connected devices, sensors, firmware, edge AI, Raspberry Pi, Jetson and cloud synchronization.

Governance & production

Continuous evaluation, logs, traceability, human-in-the-loop, compliance, runbooks and team handover.

We start from business objectives and real data

We clarify the problems to solve, expected value and constraints (regulatory, security, confidentiality, adoption). We assess available data, its quality, sensitivity and lifecycle. Use cases are prioritized by impact, risk and feasibility: internal assistant, augmented search, meeting-note generation, document extraction, computer vision, predictive scoring, support agent, IoT or business automation.

Every system is instrumented to measure real effect: accuracy, coverage, time saved, user satisfaction, human correction rate, cost per task and source quality. Decisions are based on clear metrics and documentation of business alignment. The goal is not a technology demo but tangible process improvement.

We build explainable, robust LLM systems

We combine prompt engineering, RAG, data pipelines, moderation, business tools and filtering to obtain reliable outputs. Sources are traceable, answers are contextualized and errors are detectable. We manage secrets, encrypt sensitive data and apply least privilege. Prompts, evaluation sets and model configurations are versioned, evaluation is reproducible, and drift is monitored over time.

Architecture stays pragmatic: reusable components, clear contracts, controlled costs. We choose building blocks (open source, cloud models, local models, vector tools, ML pipelines, edge devices, orchestration) according to your constraints and avoid vendor lock‑in when it does not create value. Product integrations are designed for accessibility, privacy and UX.

We industrialize AI at your organization’s pace

We align AI delivery with the product cycle: dedicated environments, CI/CD for prompts and pipelines, feature flags, progressive rollout and rollback procedures. User feedback fuels iteration (example collection, annotations, business reviews), and risks are controlled by specific acceptance tests: security, hallucinations, bias, compliance, confidentiality and business rules.

We leave the team ready to run the solution: runbooks, annotation guidelines, observability dashboards, token budget, architecture documentation and clear responsibilities across business, data, product and engineering. Your organization keeps control, understanding and compliance over time.

Examples
  • LLM assistant to classify, summarize and route support tickets.
  • Retrieval‑augmented search over an internal document base with citations.
  • Business agent connected to the CRM or ERP with human approvals.
  • Document extraction and controls with an audit trail.
  • Computer vision for detection, OCR or quality control.
  • IoT / edge AI pipeline connected to cloud infrastructure.
Typical deliverables
  • Prioritized AI roadmap with risks, ROI and success criteria.
  • LLM/RAG prototype connected to your data and tools.
  • Target architecture, infrastructure choices, MLOps and rollout strategy.
  • Evaluation sets, dashboards, runbooks and governance documentation.
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