The infrastructure under your AI features — built for production, not demos.
We design and ship LLM applications, RAG pipelines, ML platforms, and predictive systems — with the engineering rigor your business actually needs.
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 optimization, 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, anonymized 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
Four ways to work with us
Start with a fixed-scope audit, scale into a project, keep us on to run it. Engineers on every call — there is no sales team.
2 weeks · Fixed price
AI Readiness Assessment
A structured audit of your data, infrastructure, and use-case pipeline. Delivered as a prioritized 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.
Monthly · inside your team
Embedded Platform Engineers
One engineer or a full squad — sized to the roadmap, month by month. Senior engineers join your team, your tools, and your rituals, led by the same architect from day one.
Monthly · we run it
Managed Platform Operations
Senior engineers own your platform in production: on-call with a 30-minute response for incidents, upgrades, security patching, capacity and cost reviews, and a monthly written report. Your team keeps shipping features.
When the platform is the constraint
We also build the product on top.
APIs, services, and internal tools built by the same senior engineers who run your platform.
Delivered inside an existing engagement — never sold as a stand-alone body shop.
Production-grade by default: CI/CD, observability, and cost visibility from the first commit.
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