01
LLM & agentic AI
Conversational assistants, internal copilots and automations that connect models to the tools already in use.
- RAG on corporate corpora
- Multi-agent workflows
- Function-calling & tools
- Eval & guardrails
Artificial Intelligence
03 / 05
We bring AI to where it really matters: processes, products and decision-making. We identify high-impact use cases and put them into production, delivering measurable results.
01 - What we do
Four areas, one direction.
We build the prototype that demonstrates an idea and integrate it into the processes where it will operate every day.
02
Machine learning
Predictive, classification and recommendation models: when the problem is statistical rather than linguistic, they remain the most appropriate and least costly solution.
- Churn & LTV
- Demand forecasting
- Recommendation systems
- Anomaly detection
03
AI productisation
We take a prototype through to production, keeping under control the three factors that usually delay deployment: latency, cost per query and response quality.
- Prompt & context design
- Inference cost engineering
- Latency & caching
- Human-in-the-loop UX
04
MLOps & responsible AI
Deployment, monitoring, retraining and auditing: the practices that allow us to update a model over time and always know which data it was trained on.
- CI/CD for models
- Drift & bias monitoring
- Model registry & versioning
- AI policy & EU AI Act
02 - How we work
An order that does not change.
We identify the use case before choosing the technique, and assess the technique before building.
01
Discover
Use case mining
Workshops with operational teams to identify where automation would deliver a measurable benefit.
02
Frame
Feasibility & ROI
We assess costs, expected accuracy and timescales together, so that the decision on whether to proceed is based on defined metrics.
03
Design
Model architecture
We choose between fine-tuning, RAG and machine learning based on the available data and the level of control required for the specific use case.
04
Build
Pilot & eval
A model that works with the company's real data.
05
Activate
Go-live
Integration into workflows, training for the people who will use it, and adoption KPIs.
06
Operate
MLOps & retraining
We monitor drift, accuracy and cost per query, using these metrics to determine when retraining is required.
04 - The stack
Tools and frameworks
These are the tools for which we have trained people and projects already in production, and they are our starting point when we assess a new case.
Foundation OpenAIFoundation Anthropic ClaudeFoundation LlamaFoundation MistralOrchestration LangChainOrchestration LlamaIndexCloud Vertex AICloud Azure OpenAIML MLPyTorchVector Pinecone
Other practices