Getting AI vendor pitches but no clear framework for evaluating which use cases actually create value?
Leadership has approved AI investment but the team isn't sure where to start or how to sequence?
AI Consulting Services
Most organisations have more AI opportunity than they can act on. The constraint isn't access to AI -- it's knowing which use cases to pursue, in what order, with what approach, and how to build the internal capability to sustain AI development over time.
We provide AI consulting that produces decisions, not presentations. Use case identification, feasibility assessment, architecture design, vendor evaluation, and roadmap -- the strategic and technical clarity you need before committing to a build.
AI use case identification and prioritisation against business outcomes
Technical feasibility assessment: can AI solve this, and at what cost?
Build vs. buy vs. configure evaluation for your specific context
AI roadmap with sequenced investments and measurable milestones
RaftLabs provides AI consulting services that help organisations identify, prioritise, and plan AI initiatives. We assess AI use cases against business outcomes and technical feasibility, evaluate build vs. buy vs. configure options, design AI architecture for specific requirements, select models and infrastructure, and build the sequenced roadmap for AI investment. Our consulting engagements lead to a decision and a plan -- not a 50-slide strategy deck with no clear next step.
AI strategy that leads somewhere
The output of AI consulting should be a decision: what to build, in what order, with what approach, and at what cost. If it ends with a presentation and no clear next step, the consulting didn't work.
Every engagement we run ends with a concrete plan and a recommended first move.
What we cover
AI use case identification
Structured identification of AI use cases across your organisation: process mapping to find where AI can reduce cost or improve quality, customer journey analysis to find where AI can improve experience, data inventory to find what you have that AI could use, and competitive analysis to find where AI is already changing your market. Prioritisation against business value, technical feasibility, data availability, and implementation risk. Most organisations have 15-30 viable AI use cases -- the value is in picking the right three to start with.
Technical feasibility assessment
Honest assessment of whether AI can solve a specific problem at the required quality and cost. We evaluate: is the task learnable from available data? What accuracy can realistically be achieved? What does failure look like, and is it acceptable? What is the likely inference cost at production volume? What data preparation is required? We run feasibility assessments before recommending builds -- because the right answer is sometimes "not yet" or "not with AI."
Build vs. buy vs. configure
Evaluation of whether you should build custom AI, configure an AI product (Salesforce Einstein, Microsoft Copilot, HubSpot AI), or use an AI API directly. Custom build is right when your use case is specific enough that generic products can't solve it, or when the data and workflow integration requirements exceed what a configured product supports. We evaluate the options honestly -- including recommending against a custom build when a configured product is the better fit.
AI architecture design
Architecture design for production AI systems: model selection, data pipeline design, retrieval strategy (for RAG systems), orchestration approach (for agent systems), evaluation framework, monitoring infrastructure, and integration with your existing systems. Architecture decisions made before build are cheap to change. Architecture decisions made mid-build are expensive. We invest the time to design the right architecture upfront.
Vendor and model evaluation
Structured evaluation of AI vendors and models for your specific use case: defining evaluation criteria (accuracy, latency, cost, data residency, compliance), building a representative evaluation dataset, running model comparisons on your data, and scoring vendors against your criteria. Includes major frontier models (GPT-4o, Claude, Gemini, Llama) and specialised models where relevant. Evaluation takes the decision out of the vendor's hands and puts it in yours.
AI roadmap and sequencing
A 12-24 month AI roadmap with sequenced investments, dependencies, and milestones. Sequencing logic: which use cases build capability for subsequent use cases? Which share infrastructure? Which need data that other initiatives will produce? Which have the best ROI at your current data maturity? The roadmap is a decision-making tool -- not a static document. We design it to be updated as you learn from each initiative.
Not sure where to start with AI?
Tell us the business problems you're trying to solve and what you've explored so far. We'll assess the landscape and tell you where we'd start.
Related services
Generative AI Consulting -- consulting focused specifically on LLM use cases
AI Development -- building the AI systems identified in consulting
AI MVP Development -- fast first version to validate AI use cases
Machine Learning Consulting -- ML-specific technical consulting
Custom AI Development -- end-to-end custom AI product development
Frequently asked questions
AI consulting covers the strategic and technical decisions that precede building: which use cases to pursue (and which to deprioritise), whether a use case is technically feasible with AI (and at what cost and quality), what approach to take (RAG, fine-tuning, agents, custom ML, or off-the-shelf tools), what infrastructure and models to use, how to sequence multiple AI initiatives to maximise learning and value, and what internal capability you need to sustain AI development. We don't sell AI strategy as an end product -- consulting feeds into a build decision.
Generative AI consulting (see our Generative AI Consulting page) focuses specifically on large language model use cases: what to build with GPT-4o, Claude, Gemini, or Llama, and how. AI consulting is broader -- it covers the full AI landscape including traditional machine learning, predictive analytics, computer vision, NLP, and decision intelligence, alongside generative AI. If your use case is clearly a generative AI application, start with generative AI consulting. If you're evaluating AI across a wider set of business problems, start with AI consulting.
A focused AI consulting engagement runs 3-6 weeks: current state assessment (what data you have, what systems exist, what problems the business is experiencing), use case identification workshops with relevant business unit leaders, feasibility assessment for the top 3-5 use cases (technical approach, estimated cost, expected quality, data requirements), build vs. buy vs. configure analysis for each, and a sequenced 12-18 month AI roadmap with investment levels and success metrics. Output is a decision document and a concrete next step -- usually a proof of concept for the highest-priority use case.
We provide practical guidance on AI governance requirements: data privacy and consent for training data, GDPR and CCPA implications for AI systems that process personal data, model transparency requirements in regulated industries (financial services, healthcare), human oversight requirements for automated decisions, and documentation for AI audits. We are not a compliance firm -- for legal sign-off on AI compliance, you need your legal team. We help you understand the technical implications of compliance requirements and build systems that can meet them.
A focused AI consulting engagement (use case assessment, feasibility, and roadmap for a defined problem area) runs $15,000--$35,000. A broader strategic AI assessment covering multiple business units runs $35,000--$80,000. Advisory retainers for ongoing technical AI guidance run $5,000--$15,000 per month. Consulting cost is typically recouped within the first month of the resulting build by avoiding the wrong technology choice or the wrong scoping decision.