01
AI Strategy Consulting
The Discovery phase sold as a standalone engagement. AI readiness assessments, use case identification, and roadmapping for companies not yet ready to build.
Project-based fee
Services
From a vague idea to a product your team owns.
SOW covers the full AI stack, so clients don't coordinate between vendors for different parts of the technology. One contract, one team, one accountable outcome.
Core service
The SOW Product Development Lifecycle
Six phases that take an AI product from idea to deployment. A typical engagement runs 10–26 weeks depending on complexity. We select the right AI approach for each client — traditional ML, deep learning, agentic AI, or a hybrid — based on the actual problem, not the latest trend.
01
Discovery
Understand the business problem, assess AI feasibility, define scope, identify data requirements, estimate ROI.
Feasibility reportScope documentData requirementsROI model
1–2 weeks
02
Design
System architecture, model selection strategy, UX/UI design, data pipeline design, API specifications.
Architecture diagramWireframesTech stack recommendationProject plan
1–3 weeks
03
Data
Data collection, cleaning, labeling, augmentation, pipeline setup, quality assurance.
Clean datasetsData pipelineLabeling guidelinesQuality metrics
2–4 weeks
04
Development
Model training, backend and frontend engineering, API development, integration with existing client systems.
Trained modelsApplication codeAPI endpointsIntegration layer
4–12 weeks
05
Deployment
Cloud or edge deployment, CI/CD pipelines, monitoring, load testing, security hardening.
Deployed productMonitoring dashboardsCI/CD pipelineSecurity audit
1–3 weeks
06
Handover & Support
Full documentation, team training, knowledge transfer, and an optional maintenance retainer.
Technical docsTraining sessionsMaintenance SLA
1–2 weeks
Technical capability
Six capability areas under one roof
Clients don't need to coordinate between multiple vendors for different parts of the technology stack.
| Capability | What it covers | Representative use cases |
|---|---|---|
| Traditional ML & Data Science | Predictive analytics, classification, clustering, regression, NLP, recommendation engines, time-series forecasting | Churn prediction, fraud detection, demand forecasting, customer segmentation, pricing optimisation |
| Deep Learning & Computer Vision | CNNs, RNNs, transformers, image classification, object detection, semantic segmentation, medical imaging, document processing | Quality inspection, medical screening, document OCR, facial recognition, satellite imagery analysis |
| Agentic AI & LLM Solutions | AI agents with reasoning and tool use, RAG systems, multi-agent orchestration, LLM-powered copilots, workflow automation | Customer service agents, internal knowledge bases, code assistants, automated research, process automation |
| IoT + AI Integration | Edge computing, sensor fusion, embedded ML, real-time monitoring, hardware-software integration | Smart agriculture, industrial monitoring, health devices, environmental sensing, predictive maintenance |
| MLOps & Infrastructure | Model deployment, monitoring, retraining pipelines, cost optimisation, cloud architecture, CI/CD | Production ML systems, model drift detection, A/B testing, scalable inference |
| Data Engineering | Data pipeline design, ETL/ELT, data quality frameworks, warehouse architecture, real-time streaming | Data-readiness for AI, data migration, analytics infrastructure, regulatory compliance |
Supporting services
Engagements that feed into — or follow from — a build
02
MLOps & Maintenance Retainers
Ongoing model monitoring, retraining, performance optimisation, and system maintenance after product handover.
Monthly retainer
03
AI Training & Upskilling
Corporate workshops, executive briefings, and hands-on bootcamps for engineering teams — so your people can work confidently with the products we build.
Per-session or package
04
AI Safety, Ethics & Compliance
Bias audits, explainability tooling, and regulatory compliance — EU AI Act, GDPR, and local data protection law.
Project or retainer
Next step
Not sure where to start?
Most engagements start the same way — a short Discovery, and an honest answer about whether AI is the right tool for your problem.