AI implementation

From prototype to production

From a first prototype to a production system with monitoring. Our ML engineers handle the full stack.

Application development

MVPs and validated prototypes built with agile, experiment-driven workflows. Custom AI components (models, pipelines, APIs) designed for your stack. Full-stack delivery including backend and frontend where needed.

Data analysis

Analyses of what happened and why, forecasts, and recommendations for action. Interactive dashboards and detailed reports. Data integration, cleaning, and warehousing so your data is analysis-ready.

Deployment and operations

Rigorous evaluation and explainability in every deliverable. Deployment into your infrastructure. Monitoring for data drift and model degradation with clear handover documentation.

Project types

Examples of what we build

PIA chatbot showing a patient question and a grounded answer — Making knowledge accessible, in-house or as first-level support.

LLM-powered document search and chatbots

We build retrieval-augmented generation (RAG) systems that let users ask questions of large document repositories in natural language. Suitable for knowledge management, customer support, and clinical information delivery. Our PIA chatbot (see case study) is one published example.

Data analysis: predictive maintenance and error detection.

Predictive maintenance and error detection

Statistical and ML models that forecast outcomes or flag problems before they become costly. Applied to maintenance schedules, quality control, demand planning, and financial risk.

Image and video analysis: flow analysis and detection of specific blood cell variants.

Computer vision

Object detection, counting, and tracking in images and video. Used in industrial inspection, life-science imaging, traffic monitoring, and environmental studies.

Line chart: measured CO2 at Mauna Loa from 2014 to 2026, with a forecast from 2021 onwards and its 90% interval, closely following the measurements — Illustration on public data: monthly CO₂ at Mauna Loa (NOAA). A seasonal model trained on data up to 2020 forecasts 2021 to 2026 with a mean error of 0.5 ppm.

Time-series forecasting

Forecasting pipelines for financial, operational, or environmental data. Handles noisy or incomplete time series. Real-time inference and dashboards for interpreting predictions.

We build AI applications inside your business