AI consulting that ends in a decision, not a deck.

• 01

  AI use case identification
  Structured identification of AI use cases across your organisation using four parallel analyses: process mapping, customer journey analysis, data inventory matching, and competitive analysis, scored on a prioritisation matrix. The value is a disciplined framework for picking the right three to start with, not the most exciting ones.
• 02

  Technical feasibility assessment
  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 about learnability, achievable accuracy, error cost, inference cost at volume, and data preparation, because AI projects rarely fail on the model, they fail on an unchallenged assumption about one of those five.
• 03

  Build vs. buy vs. configure
  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. Off-the-shelf products win when your workflow maps to what they support; building wins when proprietary data creates an advantage or data sovereignty rules apply. We score both paths against the same criteria, weighing configured products such as Salesforce Einstein, Microsoft Copilot, and Intercom Fin, and recommend honestly, including against a custom build.
• 04

  AI architecture design
  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 with retrieval quality tested before the LLM response layer; for agents, orchestration framework, tool design, and failure handling. The vector layer runs on Pinecone, pgvector, or Weaviate, with retrieval quality scored in RAGAS and traced in LangSmith or Langfuse. The architecture document is a working specification for the engineering team, not a diagram.
• 05

  Vendor and model evaluation
  Structured model and vendor evaluation that replaces vendor-led demos with data-driven comparison on your actual inputs. We build a benchmark of labelled production examples, run candidates against it, and score them on a weighted rubric covering accuracy, latency, cost per 1,000 requests, context window, and data residency. Candidates typically include GPT-4o, Claude Opus, Gemini Pro, and Llama 3. The output is a recommendation backed by scored comparison data, not a vendor pitch deck.
• 06

  AI roadmap and sequencing
  A 12 to 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 precede complex use cases, and each initiative is scored on its own ROI plus the infrastructure reuse it enables, with quarterly go/no-go review gates.

• 01

  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) and clinical documentation assistance (cuts per-encounter charting time, sized against your own EHR data in the feasibility assessment). Also common: 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.

• 02

  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.

• 03

  Logistics and supply chain

  AI use cases with measurable cost impact in logistics: demand forecasting (lower inventory holding costs when models beat naive forecasting, sized to your current forecast-error rate) and route optimisation (fuel and driver cost savings). Also common: 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.

• 04

  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.

• 05

  Retail and e-commerce

  Personalised product recommendations (an average-order-value lift over your baseline, validated before you commit to a build), dynamic pricing on competitive SKUs, demand forecasting for inventory (stockout and overstock reduction), review sentiment analysis, 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.

• 06

  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.

• 01

  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.

• 02

  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.

• 03

  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.

• 04

  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.

• 05

  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.

• 06

  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.

• 07

  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.

1. Week 1
   01

   Current state assessment

   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.

2. Weeks 2-3
   02

   Use case identification and feasibility

   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.

3. Week 4
   03

   Build vs. buy vs. configure evaluation

   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.

4. Week 5-6
   04

   Roadmap and handoff

   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.

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