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
Recent outcomes
AI strategy · Healthcare SaaS
Identified 3 high-priority AI use cases from 22 candidates and delivered a sequenced 18-month roadmap. First build went live in 12 weeks.
12 weeks to first production AIBuild vs. buy · Fintech
Feasibility assessment saved a mid-size fintech from a $280K custom ML build. Off-the-shelf configuration achieved the same accuracy at 15% of the cost.
$280K build cost avoidedAI chatbot · Operations
Scoped and architecture-designed a conversational AI system. 70% of routine queries handled without human intervention after 12 weeks of development.
70% query deflection rateRecognition
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?
The short answer
RaftLabs provides AI consulting for businesses in the US, UK, and Australia. We identify use cases, run feasibility assessments, evaluate build vs. buy, design architecture, and deliver a sequenced roadmap. Engagements run 3-6 weeks and cost $15,000-$35,000.
AI Development · Updated July 2026
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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.
Scope
Structured identification of AI use cases across your organisation using four parallel analyses: process mapping, customer journey analysis, data inventory matching, and competitive analysis. The output is a prioritisation matrix scored on business value, technical feasibility, data availability, time to value, and implementation risk. Most organisations surface 15-30 viable use cases. The value is a disciplined framework for picking the right three to start with, not the most exciting ones.
Honest assessment of whether AI can solve a specific problem at the required quality and cost, before committing engineering budget to find out the hard way. Every assessment answers five questions: Is the task learnable from historical data? What accuracy is acceptable and achievable? What does a wrong prediction cost? What is the inference cost at production volume? What data preparation comes first? AI projects rarely fail on the model. They fail on an unchallenged assumption about one of these five.
Structured evaluation of whether to build custom AI, configure an AI product, or integrate an AI API, driven by your requirements, not by what is most interesting to build. Products like Salesforce Einstein, Microsoft Copilot, and Intercom Fin win when your workflow maps to what they support. Building wins when proprietary data creates an advantage, integration needs exceed product APIs, or data sovereignty rules apply. We score both paths against the same criteria and recommend honestly, including against a custom build.
Architecture design for production AI systems, covering the decisions that are expensive to reverse mid-build. For RAG: chunking strategy, embedding model, and vector database selection (Pinecone, pgvector, or Weaviate), with retrieval quality tested before the LLM response layer is wired in. For agents: orchestration framework, tool design, and failure handling. Evaluation via RAGAS metrics, monitoring via LangSmith or Langfuse. The architecture document is a working specification for the engineering team, not a diagram.
Structured model and vendor evaluation that replaces vendor-led demos with data-driven comparison on your actual inputs. We build a benchmark of 100-200 labelled production examples, then run candidates against it: GPT-4o, Claude Opus, Gemini Pro, self-hosted Llama 3, and specialised models where relevant. A weighted rubric scores accuracy, latency, cost per 1,000 requests, context window, and data residency. The output is a recommendation backed by scored comparison data, not a vendor pitch deck.
A 12-24 month AI roadmap with sequenced investments, data infrastructure dependencies, and milestone gates, designed as a decision-making tool, not a static presentation. Data infrastructure deploys first. Quick wins come before complex use cases, and high-risk regulated decisions wait until the organisation has shipped in lower-stakes contexts. Each initiative is scored on its own ROI plus the infrastructure reuse it enables. Quarterly review gates carry explicit go/no-go criteria, because good roadmaps are designed to be updated.
How we work
Every engagement follows the same four phases. Output is a decision document and a recommended first move, not a presentation.
We map your data, systems, and workflows. We interview business unit leaders to surface the problems that consume the most time or create the most cost. You leave week 1 knowing what we found and what we're evaluating next.
Structured workshops to identify AI use cases across your organisation. We assess the top candidates against five feasibility criteria: data availability, required accuracy, cost of errors, inference cost at volume, and data preparation requirements. Most organisations surface 15-30 candidates. We help you pick the right 3.
For each shortlisted use case, we compare custom build against configured AI products and API integrations. The recommendation is determined by your requirements, not by which option is most interesting to build. We recommend against a custom build when a configured product fits.
A sequenced 12-24 month AI roadmap with investment levels, data infrastructure dependencies, and milestone gates. Quarterly go/no-go criteria at each stage. Most clients move directly into development with RaftLabs for the first build.
Consulting is only useful if it changes what you do next. Every engagement is scoped around concrete deliverables, each one a document your leadership and engineering teams can act on without us in the room. The table below maps what we assess to what you receive and how it feeds the build decision.
| Deliverable | What it assesses | Where it fits |
|---|---|---|
| AI readiness assessment | Data availability and quality, existing systems, team capability, and the workflows where AI has visible baseline cost | Sets the boundary on which use cases are viable now versus later |
| Use case shortlist and prioritisation matrix | 15-30 candidate use cases scored on business value, feasibility, data availability, time to value, and risk | Narrows the field to the right three to start with, not the most exciting ones |
| Feasibility assessment | For the top 3-5 use cases: is it learnable, what accuracy is achievable, what a wrong prediction costs, inference cost at volume, and data preparation effort | Surfaces the failure assumptions before engineering budget is committed |
| Build vs. buy vs. configure analysis | Custom build against configured AI products (Salesforce Einstein, Microsoft Copilot, Intercom Fin) and API integrations | Determines the approach per use case, including recommending against a custom build |
| Architecture design | For the priority use case: RAG chunking and retrieval, model selection, vector database, orchestration, and evaluation and monitoring | A working specification the engineering team builds from, not a diagram |
| Sequenced AI roadmap | A 12-24 month plan with investment levels, data infrastructure dependencies, and quarterly go/no-go gates | The decision-making tool that governs sequencing and reuse across initiatives |
The models, frameworks, and infrastructure we assess against are the same ones we build with: models such as GPT-4o, Claude, Gemini, and Llama; frameworks such as PyTorch, TensorFlow, and LangChain; vector databases such as Pinecone, Weaviate, and pgvector; and cloud and MLOps on AWS, Google Cloud, or Azure. We evaluate each against your actual inputs, not vendor demos, and document the rationale so the choice survives a change of team.
We scope every engagement before pricing it, so you know the number before we start. Consulting cost is typically recouped within the first month of the resulting build by avoiding the wrong technology choice or the wrong scoping decision.
| Engagement type | Scope | Cost range |
|---|---|---|
| Focused AI consulting engagement | Use case assessment, feasibility, and roadmap for a defined problem area | $15,000–$35,000 |
| Strategic AI assessment | Broader assessment covering multiple business units | $35,000–$80,000 |
| Advisory retainer | Ongoing technical AI guidance, priced per month | $5,000–$15,000 / month |
What moves cost within these ranges: the number of business units in scope, how much data preparation the feasibility work requires, and whether architecture design is needed for one priority use case or several. Most clients then move directly into a proof of concept or build with RaftLabs for the highest-priority use case.
The AI consulting framework is the same across industries. What changes is the use case landscape, the data environment, the accuracy thresholds that make a use case viable, and the compliance constraints that govern deployment. We have run AI consulting engagements across the following verticals.
Industries
Healthcare and life sciences
The highest-ROI AI use cases in healthcare we assess most often: prior authorisation automation (3-5 staff hours per request reduced to under 10 minutes via LLM-assisted clinical documentation extraction), clinical documentation assistance (charting time reduction of 30-60%), patient risk stratification models trained on EHR data, and clinical decision support tools surfacing relevant guidelines at the point of care. Compliance constraints (HIPAA, FDA SaMD classification, ONC interoperability requirements) are assessed as part of feasibility, not after. See our healthcare software development and AI for healthcare pages.
Financial services and fintech
Document extraction for lending (bank statements, tax returns, pay stubs processed without manual keying), fraud detection and AML anomaly detection (classification models on transaction data), credit risk scoring from historical lending data, customer support automation, and regulatory document summarisation. Constraint that shapes feasibility: AI decisions in underwriting and credit require explainability documentation in most regulated markets, which limits which model types are viable and how outputs can be used. See our AI for fintech page.
Logistics and supply chain
AI use cases with measurable cost impact in logistics: demand forecasting (inventory holding cost reduction of 15-25% when models outperform naive methods), route optimisation (fuel and driver cost savings), dynamic ETA prediction (reducing inbound support call volume), exception detection (alerting on freight anomalies before delays escalate), and carrier selection automation. Data requirement that comes up in every feasibility assessment: 12-24 months of clean historical order, route, and inventory data. See our AI for logistics page.
Insurance
Claims triage and automated severity classification, FNOL document extraction, subrogation opportunity identification in claims data, underwriting risk scoring from third-party data sources, and churn prediction for policy renewals. The feasibility question that matters most in insurance AI: whether the model's decision needs to be explainable to a regulator or customer, because that determines the permissible model type and the human-review requirement on top of it. See our AI for insurance page.
Retail and e-commerce
Personalised product recommendations (3-8% average order value lift is the benchmark to validate against your baseline), dynamic pricing on competitive SKUs, demand forecasting for inventory (stockout and overstock reduction), review sentiment analysis at scale, and customer support deflection. Feasibility constraint: recommendation models require transaction history volume to outperform simple heuristics, so we quantify the minimum data threshold before recommending a custom build over a configured product like Salesforce Einstein. See our AI for retail page.
Legal and professional services
Contract clause extraction and comparison, legal research acceleration via RAG over case law and regulatory databases, matter cost prediction from historical billing data, privilege review acceleration, and AI-assisted client intake. The use case the feasibility assessment most often limits: autonomous contract review for execution. Accuracy requirements for redlines are higher than most current LLMs can meet without structured human review. Use cases with genuine ROI are the ones where AI accelerates and organises human review rather than replacing it. See our AI for legal page.
Services
AI use case and strategy consulting
Use case identification, feasibility assessment, build vs. buy analysis, and a sequenced roadmap. The core engagement described on this page, ending in a decision document and a recommended first move rather than a deck.
Generative AI consulting
Consulting focused on LLM use cases: what to build with GPT-4o, Claude, Gemini, or Llama, which approach fits (RAG, fine-tuning, or agents), and how to control inference cost at production volume.
Machine learning consulting
Technical consulting for predictive and classification problems: whether a task is learnable from your data, what accuracy is achievable, and what the data preparation effort really is before any model training starts.
AI governance and compliance
Practical guidance on the technical implications of AI governance: data privacy for training data, GDPR and CCPA exposure, model transparency in regulated industries, and human oversight requirements for automated decisions.
AI proof of concept development
The recommended next step after most consulting engagements. A focused proof of concept for the highest-priority use case, built to validate feasibility, accuracy, and cost before committing to a full build.
AI development
Building the AI systems the consulting identified, from data pipelines and model selection through to production deployment and monitoring, once the roadmap is agreed.
AI product engineering
End-to-end engineering for AI-native products where the AI capability is the product, not a feature bolted onto an existing system, taking the roadmap through to a shippable product.
Why us
The engineers who assess your problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, SaaS, mobile, automation, and enterprise platforms in healthcare, fintech, logistics, and hospitality.
GDPR, HIPAA, SOC 2 - compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant systems for US healthcare clients and GDPR-compliant products for European markets.
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.
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.
AI Governance
practical governance, transparency, and compliance guidance for AI systems.
AI Proof of Concept
a focused PoC to validate the priority use case before the full build.
Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.
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.
We use a five-question framework for every feasibility assessment: Is the task learnable from historical data? What accuracy level is acceptable and achievable? What does a wrong prediction cost the business? What is the inference cost at production volume? What data preparation is required before training can start? Most AI project failures trace back to an incorrect assumption about one of these five dimensions, discovered only after significant engineering investment. We surface those assumptions before any code is written.
The engagement ends with a decision document and a recommended first move, typically a proof of concept for the highest-priority use case. Most clients move directly into development with RaftLabs for that first build. If you have an internal team that can build from the roadmap, we hand over the architecture documentation, model selection rationale, and data requirements so they can proceed without us. We are happy with either outcome. See our AI development services and AI agent development for what comes next.
Work with us
We scope AI Consulting Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Go deeper
Healthcare AI
HIPAA-compliant AI for patient monitoring, clinical decisions, and care workflows.
FinTech AI
Fraud detection, document processing, and compliance intelligence.
Logistics AI
Route optimisation, demand forecasting, and exception handling.
Insurance AI
Claims automation, underwriting risk scoring, and compliance monitoring.
Retail AI
Personalisation, demand forecasting, and inventory optimisation.
Hospitality AI
Revenue AI, guest communication, and booking automation.
Where AI consulting drives results
Healthcare
AI strategy and build for HIPAA-compliant clinical workflows, RPM, and care ops.
Manufacturing
Predictive maintenance, computer vision QC, and yield optimisation strategy.
FinTech
AI use-case selection for fraud detection, credit scoring, and document extraction.
Logistics
Demand forecasting, route optimisation, and exception detection scoping.
Retail
Recommendation engine strategy, dynamic pricing, and churn prediction scoping.