Every layer of the AI stack, covered.
Nine focused practice areas, organized the way real AI work actually splits up. Each one layers onto the systems you already run. Pick where you need depth, we'll handle the seams.
AI Strategy & Advisory
Roadmap, readiness and ROI modelling for enterprise AI.
- Readiness audit
- ROI modelling
- Operating model
- Responsible AI
Generative AI & LLM
Custom LLMs, RAG, copilots and autonomous agents.
- LLM fine-tuning
- RAG systems
- Enterprise copilots
- Agent workflows
AI Platform Engineering
The substrate beneath every AI product, built to scale.
- LangChain / LlamaIndex
- Vector DBs
- GPU orchestration
- Model gateways
Machine Learning
Models that learn from your data and ship to production.
- Recommenders
- NLP / NLU
- Forecasting
- Deployment
Data Science & Analytics
Decisions backed by predictive and prescriptive analytics.
- Forecasting
- Segmentation & CLV
- EDA & viz
- Big-data pipelines
Computer Vision
Pixels to decisions at warehouse, retail and factory scale.
- OCR & document AI
- Quality inspection
- Object detection
- Video analytics
AI-Enabled Automation
RPA upgraded with intelligence for end-to-end workflows.
- Document intelligence
- Smart bots
- Process discovery
- Anomaly detection
AIaaS
Production AI delivered as APIs, fast to integrate, easy to govern.
- LLM integration
- Voice & speech AI
- Custom AI APIs
- Predictive services
MLOps & AI DevOps
Continuous training, monitoring and lifecycle management.
- MLflow / Kubeflow
- CI/CD for AI
- Drift detection
- Feature stores
Three layers, one accountable team
Strategy, advisory, governance
The work before any code. Readiness audits, ROI modelling, responsible-AI frameworks, and operating-model design.
GenAI, ML, vision, platforms
The models and the substrate they run on: fine-tuning, RAG, vector DBs, GPU orchestration, all wired into your stack.
MLOps, automation, AIaaS
The part nobody photographs but everybody needs: continuous training, drift detection, monitoring, lifecycle management.