What We Build
Four practice areas
Specialists in each layer of the AI stack — from raw data to deployed model to business outcome.
Generative AI & LLM Engineering
Design and deploy LLM-powered applications — RAG pipelines, AI agents, function-calling workflows, and multi-modal systems. We handle prompt engineering, context management, cost optimisation, and production hardening.
RAG & Knowledge Systems
Build retrieval-augmented generation systems that make your private data queryable. Vector search, hybrid retrieval, chunking strategies, and re-ranking — tuned for accuracy and latency in production.
ML Platform & AI Governance
Stand up the infrastructure teams need to move fast without breaking models in production. Feature stores, experiment tracking, model registries, CI/CD for ML, drift monitoring, and bias/compliance guardrails.
Predictive ML & Recommendations
Structured ML pipelines for forecasting, anomaly detection, churn prediction, and recommendation engines. End-to-end delivery: data prep, training, evaluation, deployment, and scheduled retraining.
Case Studies
Work that shipped
Selected projects from our AI practice — real results, anonymised clients.
Client
Romanian on-demand delivery platform
Scope
Real-time demand forecasting
Replaced manual courier dispatch logic with an ML pipeline forecasting demand by zone and hour. Reduced late deliveries by 23% and cut idle courier time during off-peak windows.
Client
Global music rights & metadata company
Scope
LLM-assisted rights classification
Deployed a retrieval-augmented system over 40M+ rights records. Legal operations team cut manual classification time from days to hours; accuracy exceeded the prior rule-based system by 18 percentage points.
Client
Enterprise IT service management platform
Scope
AI-driven ticket triage & routing
Integrated an LLM layer into the existing ITSM workflow to classify, enrich, and auto-route incoming tickets. First-contact resolution rate improved by 31%; L1 volume dropped 40%.
How We Engage
Three ways to work with us
From a two-week diagnostic to a fully embedded team — structured around where you are.
2 weeks · Fixed price
AI Readiness Assessment
A structured audit of your data, infrastructure, and use-case pipeline. Delivered as a prioritised roadmap with cost and effort estimates for your top 3 AI initiatives.
6–16 weeks
Project Delivery
Fixed-scope delivery of a defined AI system — RAG pipeline, ML model, LLM integration, or MLOps platform. Clear milestones, weekly check-ins, handover with documentation.
Ongoing
Embedded Engineering
Senior AI engineers embedded in your team. Ideal for organisations with an existing ML programme that needs specialist depth in LLMs, MLOps, or data infrastructure.
Our Standards
What we don't do
We work with companies that are serious about shipping AI. That means being direct about the patterns we avoid.
Off-the-shelf AI wrappers dressed up as strategy
Proof-of-concepts that never reach production
Vendor lock-in hidden behind abstraction layers
AI for its own sake — every system we build has a measurable business outcome