Proof of Value (PoV)
Rapidly validate a high-impact use case with a working prototype and clear success metrics.
- Deliverables: prototype, experiment plan, measurement dashboard
- Timeline: 2–6 weeks
- Outcome: validated ROI and go/no-go recommendation
AI Readiness Assessment
We evaluate your data, tooling, models, and organizational readiness to execute safe, valuable AI projects.
What we cover
- Data quality & availability audits
- Use-case prioritization & ROI sizing
- Governance, risk & compliance review
Customized AI Projects
Beyond readiness, we design and deliver tailored AI projects that move from prototype to production. Examples below show common engagement types, expected deliverables, and typical timelines.
Data Platform & Feature Engineering
Establish reliable data pipelines, feature stores, and tooling to support model development at scale.
- Deliverables: ETL pipelines, data contracts, feature catalogs
- Timeline: 4–10 weeks
- Outcome: repeatable, auditable inputs for ML models
MLOps & Productionisation
Take models from prototype to production with CI/CD, deployment automation, and rollback strategies.
- Deliverables: model CI pipelines, deployment runbooks, monitoring
- Timeline: 6–12 weeks
- Outcome: resilient production models with observable SLIs
LLM Customisation & RAG Integration
Fine-tune or adapt large language models and integrate retrieval-augmented generation to deliver accurate, contextual responses.
- Deliverables: embeddings, vector store, prompt templates, tuned model artifacts
- Timeline: 3–8 weeks
- Outcome: higher-precision, domain-aware LLM behaviour
Model Governance & Security
Implement policies, access controls, and evaluation workflows to reduce risk and meet compliance needs.
- Deliverables: governance policy, auditing hooks, risk register
- Timeline: 2–6 weeks
- Outcome: traceable decisions and compliant model lifecycle
Human-in-the-loop & UX for AI
Design workflows where humans and models collaborate safely and effectively for higher-quality outputs.
- Deliverables: interaction flows, acceptance criteria, training materials
- Timeline: 3–8 weeks
- Outcome: operationalised review loops and improved model precision
How engagements typically run
- Discovery sprint — align objectives, success metrics and scope (1–2 weeks).
- Delivery sprints — iterative development, reviews and pilot releases (2–12 weeks depending on scope).
- Handover & ops — runbooks, monitoring, and knowledge transfer with SLAs and support options.
Engagement models
We offer fixed-price PoVs, time-and-materials delivery for production work, and long-term support retainers for ongoing optimisation.
Service Categories
Explore targeted offerings designed to meet common organisational needs across strategy, delivery and adoption.
AI Custom Buildouts
End-to-end delivery of bespoke AI systems including data ingest, model development, integration, and production deployment. Ideal for companies wanting a turnkey solution tailored to domain-specific needs.
Training & Workshops
Hands-on workshops for executives, product managers and engineers covering AI strategy, responsible AI practices, prompt engineering, and model evaluation techniques.
User Experience Consulting
Designing human-centred AI interactions, prompt UX, and human-in-the-loop patterns that improve trust, safety and task completion rates.
Data Strategy & Integration
Data governance, integration patterns and analytics strategy to create reliable, auditable inputs for AI systems.
MLOps & Production Support
Operational best-practices for model CI/CD, monitoring, drift detection and incident response to keep models healthy in production.