AI Strategy Consulting &
Machine Learning
We diagnose where AI returns the highest ROI in your specific operation, then deliver a concrete build plan — not a slide deck. Every recommendation we make, we can also build and deploy.
Find out where automation pays before you spend anything building it.
Most AI strategy work produces a document. You pay for a study, receive a deck, and are left with the same problem you started with plus a plan you now have to find someone to execute.
We think the useful version answers three questions with numbers: which of your processes cost the most in hours and errors, which of those could realistically be automated, and what each one would be worth if it were. Anything beyond that is padding.
In practice, most clients learn more from our $5,000 pilot than from any assessment, because building one real thing surfaces the problems a study never finds. If you need the formal engagement for a board or a funding case, we do that too, but we will say when the pilot is the better buy.
What you actually get
- ✓ A ranked list of your processes by what they actually cost you
- ✓ A straight answer on which ones AI can help with and which it cannot
- ✓ A cost and a likely return for each, so you can prioritise honestly
- ✓ A 90-day plan someone can actually execute, not a slide deck
- ✓ A recommendation to do nothing, where that is the right answer
See what one pilot produced for a 40-doctor healthcare network: 85% of bookings automated.
Everything below this point is the technical detail: the frameworks, models, and architecture we use. If that is not your area, skip it and send us a note instead.
What a consulting engagement covers.
Seven structured pillars — from bottleneck diagnosis and data architecture through governance, MLOps, and change management. Every pillar ends with an actionable deliverable, not a framework.
AI Readiness Assessment
We audit your data infrastructure, tech stack, and operational workflows to identify high-impact AI opportunities. This includes CRISP-DM process mapping, data maturity scoring, and vendor-neutral tool selection across OpenAI, Anthropic, and Google Gemini ecosystems. Our model selection guidance covers the current generation: Gemini 3.5 Flash for high-throughput agentic tasks (4x faster than Gemini 3.1 Pro, $1.50/1M input tokens); Gemini 3.5 Pro for complex reasoning and long-context document processing (2M token context window); and Gemini 3.1 Pro for SVG generation, 3D interactive code, and multimodal workflows. The right model tier for each use case directly determines operating cost and system performance at production scale.
- ✓ Process & data gap analysis
- ✓ Vendor-neutral LLM evaluation incl. Gemini 3.5 Flash / Pro / 3.1 Pro
- ✓ ROI opportunity mapping
Strategic AI Roadmapping
We craft a phased 12-to-36-month adoption plan aligned with your KPIs. Roadmaps cover Agentic AI prioritisation, LLMOps toolchain selection (LangSmith, Weights & Biases, Arize AI), and governance under the EU AI Act and GDPR frameworks.
- ✓ Phased agentic rollout plans
- ✓ LLMOps observability strategy
- ✓ Compliance-first governance
Data Architecture & RAG Design
A robust AI strategy lives or dies on its data foundation. We design enterprise RAG pipelines using Pinecone, Weaviate, and pgvector, backed by FastAPI ingestion layers and Anthropic Model Context Protocol (MCP) integrations to connect you to live business data.
- ✓ Vector DB selection & design
- ✓ MCP-first tool integration
- ✓ Data sovereignty by default
Governance & Risk Management
We establish AI governance councils, bias monitoring pipelines, and audit logging frameworks. Our approach integrates with Microsoft Semantic Kernel and LangChain guardrails to enforce responsible AI in production, not just on paper.
- ✓ Bias detection pipelines
- ✓ EU AI Act compliance
- ✓ Audit-ready AI workflows
MLOps & Deployment Strategy
Transitioning from pilot to production requires engineering discipline. We implement MLOps pipelines with CI/CD for models, automated retraining triggers, and performance dashboards using Vertex AI, AWS SageMaker, and Azure ML.
- ✓ Model CI/CD pipelines
- ✓ Automated drift detection
- ✓ Vertex AI & SageMaker support
Change Management & AI Literacy
Technology without adoption is wasted investment. We run AI literacy workshops, establish internal AI champions programmes, and create prompt engineering guides tailored to your teams, ensuring your workforce amplifies AI rather than avoids it.
- ✓ Prompt engineering training
- ✓ Internal AI champions programme
- ✓ Executive AI briefings
SKILL.md Strategic Guardrails
We advise on the adoption of the 2026 SKILL.md open standard for enterprise AI. By standardizing how skills are documented and executed, you ensure your AI agents are reliable, auditable, and easily portable across your business units.
- ✓ SKILL.md adoption strategy
- ✓ Standardized skill documentation
- ✓ Auditable agentic workflows
Strategy that leads to deployment.
Our consultants are practising AI engineers who ship production systems. That means every recommendation is grounded in what works at scale — and we can build it ourselves if needed.
Frequently asked questions.
What does an AI strategy consulting engagement actually include?
Our engagements are structured around seven pillars: AI readiness assessment, strategic roadmapping, data architecture and RAG design, governance and risk management, MLOps deployment strategy, change management, and SKILL.md standardization. Every recommendation comes with a build plan — not just a slide deck.
How is your AI consulting different from a traditional management consultancy?
Our consultants are practising AI engineers who build production systems — not career consultants who have never shipped code. Every recommendation we make, we can also build. That means our advice is grounded in what actually works in production, not what looks good in a PowerPoint.
Do you offer machine learning consulting as well as general AI consulting?
Yes. Our machine learning consulting covers the full ML lifecycle — data preparation and feature engineering, model selection and fine-tuning, MLOps pipeline design, and production deployment. Machine learning consulting engagements are typically scoped after an initial AI readiness assessment.
How do you measure ROI on AI investments?
We establish baseline metrics during the assessment phase — processing times, error rates, labor costs, throughput volumes — then track improvements after implementation. Typical ROI metrics include: hours of manual work eliminated, error rate reduction, cost per transaction reduction, and customer satisfaction improvements. Clients see 30–60% operational cost reduction with payback within 3–6 months.
What does AI consulting cost?
Almost everyone starts with our $5,000 pilot: one fixed-scope workflow, built end to end, with the before-and-after numbers measured. That is the entry point for every service we offer, and most clients learn more from it than from any assessment. If you need a formal strategy engagement instead, readiness assessments run $10,000–$20,000, full strategy engagements with implementation planning run $25,000–$50,000, and ongoing advisory retainers are typically $5,000–$15,000 per month. We provide a fixed-price scope after an initial discovery call.
Not ready to book a call?
Tell us the one manual process eating the most time in your business. We will reply with whether it is automatable, roughly what it would take, and what it would be worth. No deck, no pitch.
Thirty minutes.
We'll tell you exactly
where your ROI is.
No sales deck. No 50-page report you have to pay for before anything gets built. Just a direct conversation about which of your workflows are costing the most and whether AI can fix them. If there's no compelling answer, we'll say so. And it's a conversation with Kash, our founder, not a rep reading from a script, because the person who built this business is the one who should understand yours.