Artificial Intelligence & Automation

We start from business objectives

We clarify the problems to solve, expected value and constraints (regulatory, security, confidentiality). We assess available data, its quality and lifecycle. Use cases are prioritized by impact, risk and feasibility. We define guardrails (human‑in‑the‑loop, confidence thresholds, logging policies) and an experimentation plan to learn fast without endangering production.

Every system is instrumented to measure real effect: accuracy, coverage, time saved, user satisfaction. Decisions are based on clear metrics and documentation of business alignment. The goal isn’t technological demonstration but tangible process improvement.

We build explainable, robust solutions

We combine prompt engineering, RAG, data pipelines, moderation 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. Models and datasets are versioned, evaluation is reproducible, and drift is monitored over time.

Architecture is pragmatic: reusable components, clear contracts, controlled costs. We choose building blocks (open source/SaaS) according to your constraints and avoid vendor lock‑in. Product integrations are designed for accessibility, privacy and UX.

We industrialize AI at product pace

We align AI delivery with the product cycle: dedicated environments, CI/CD for prompts and pipelines, feature flags, progressive deployment channels and rollback procedures. User feedback fuels iteration (simple RLHF loop, example collection), and risks are controlled by specific acceptance tests (security, bias, compliance).

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