A generative AI consulting company that gets you past the demo and into production.

Proof

• 01

  Use case assessment and prioritisation
  Structured evaluation of your proposed generative AI use cases against four dimensions: value, feasibility, risk, and effort. Most assessments reveal 1-2 use cases with clear ROI worth building next quarter, a few worth waiting on, and several that don't survive honest feasibility analysis.
• 02

  Model selection and evaluation
  Comparative evaluation of frontier and open-source models against your use case: output quality on your actual inputs, latency at your expected volume, and cost per query. The recommendation covers a primary model, a fallback for availability events, and the conditions where a cheaper or faster model is acceptable. We model this across GPT-4o, Claude, Gemini, and Llama, with token cost modelling and a primary-plus-fallback selection.
• 03

  RAG and knowledge system design
  Architecture design for retrieval-augmented generation systems before you build them, because chunking, embedding, and vector database choices are expensive to change once production queries are running. Most enterprise RAG systems fail because they retrieve the wrong context, not because the model writes a poor answer. The design covers chunking and embedding strategy, vector database selection, and a retrieval evaluation framework.
• 04

  Agent and multi-agent architecture
  Architecture design for AI agent systems that plan, use tools, and execute multi-step tasks. Failure mode analysis is the most valuable part: agents that loop, hallucinate tool arguments, or take unsafe actions each need a specific guardrail before deployment. The output is an architecture document your team can build from, specifying tool definitions, memory architecture, a LangGraph or custom state machine, and multi-agent orchestration.
• 05

  Production readiness review
  Structured assessment of an AI system in development against what production requires: evaluation coverage, latency and cost at realistic traffic, hallucination handling, graceful degradation when the model API fails, monitoring completeness, and data privacy compliance. The output is a prioritised fix list with severity ratings, ordered by the probability and consequence of each issue failing in production.
• 06

  AI governance and evaluation framework
  Design of the repeatable processes that let you ship model, prompt, and pipeline changes without quality regressions. Golden test sets, CI/CD evaluation, and LLM-as-judge scoring back every model and prompt update, RAGAS metrics gate RAG deployments, and output guardrails enforce content policies and PII redaction before responses reach users.

• 01

  OpenAI GPT-4o and GPT-4o mini

  GPT-4o for general reasoning, instruction-following, and multimodal tasks (text, image, and audio input). GPT-4o mini for cost-sensitive workloads where latency and per-token cost matter more than peak reasoning capability. Typical production pattern: GPT-4o mini handles 80% of requests at 10-20x lower cost; GPT-4o handles the complex 20% requiring deeper reasoning.

• 02

  Anthropic Claude (Sonnet and Haiku)

  Claude 3.5 Sonnet for complex multi-step reasoning, long-context tasks (up to 200K tokens), and use cases requiring careful instruction-following with minimal hallucination. Claude Haiku for high-volume, low-latency workloads. Prompt caching cuts input token cost by 90% on cache hits for high-repetition system prompts, materially reducing cost at scale.

• 03

  Google Gemini 1.5 Pro and Flash

  Gemini 1.5 Pro for teams already on Google Cloud or Google Workspace infrastructure. One million token context window makes it useful for long-document analysis tasks where other models require chunking. Gemini Flash for latency-sensitive applications requiring sub-second response times.

• 04

  Meta Llama 3 and open-source models

  Self-hosted Llama 3 for use cases with data sovereignty requirements that prohibit routing data through a third-party API. Inference on AWS (EC2 G5, p4d instances) or GCP (A100, H100). Open-source models eliminate per-token cost at volume but introduce GPU infrastructure and MLOps operational overhead. We assess the cost crossover point before recommending self-hosting.

• 05

  Mistral and specialised models

  Mistral models for European data residency requirements (Mistral AI is based in France and operates EU infrastructure). DeepSeek for coding-specific tasks. Domain-specific fine-tuned models where general frontier models underperform on technical vocabulary or domain-specific output formats.

• 06

  Orchestration: LangChain, LangGraph, and cloud provider AI services

  LangGraph for stateful multi-step agents with cyclic workflows and human-in-the-loop checkpoints. AWS Bedrock for teams wanting managed access to multiple frontier models without managing API keys for each provider. Azure OpenAI for organisations with existing Microsoft Enterprise Agreements and data residency within Azure regions. Evaluation and monitoring via LangSmith, Langfuse, or Weights and Biases.

• 01

  Healthcare

  Clinical documentation assistance (reducing physician charting time by 30-60%), patient triage and intake chatbots, prior authorisation automation (pulling clinical documentation from EHR records and drafting payer submissions), clinical decision support tools grounded via RAG in medical knowledge bases, and radiology report summarisation. Compliance requirements: HIPAA data handling for PHI in LLM prompts, FDA SaMD classification for clinical AI outputs, and human-review requirements for AI-assisted clinical decisions.

• 02

  Financial services and fintech

  Document extraction and processing for lending (bank statements, pay stubs, tax returns processed without manual keying), regulatory document summarisation, investment research synthesis, customer support automation for account queries, and contract review. Compliance requirements: GDPR and CCPA for personal financial data in LLM prompts, explainability documentation for AI-assisted credit decisions in regulated markets.

• 03

  Legal and professional services

  Contract clause extraction and comparison, legal research acceleration (RAG over case law and regulatory databases), matter cost prediction, privilege review acceleration, and client-facing Q&A tools grounded in firm knowledge bases. Key constraint: accuracy requirements for privilege determinations are higher than most LLMs can meet without human review, we scope use cases where AI accelerates review rather than replaces it.

• 04

  Logistics and supply chain

  Automated exception notifications (translating operational anomalies into plain-language alerts for ops managers), carrier communication summarisation, customs documentation drafting, RFQ response generation, and internal knowledge retrieval for complex operational queries. The highest-value use cases are ones where unstructured operational data (emails, PDFs, event logs) needs to be synthesised quickly.

• 05

  SaaS products and enterprise software

  AI features embedded in existing SaaS products: document analysis, search augmentation with semantic retrieval, automated report generation, AI-assisted onboarding flows, and in-product AI assistants grounded in the user's own data. Architecture decision that matters most: whether the AI feature is a thin wrapper on a frontier model API or a RAG system grounded in the user's proprietary data, the second is more defensible and harder to replicate.

• 06

  Retail and e-commerce

  Product description generation at scale, personalised email copy generation, customer support deflection for order status and policy queries, review summarisation and sentiment analysis, and AI-assisted merchandising decision support. Cost modelling at retail scale (millions of product SKUs, high-volume customer queries) is a critical part of feasibility assessment, per-token cost at volume determines whether the unit economics work.

1. Week 1
   01

   Discover and scope

   We map the problem, the data, and the existing systems. You leave week 1 with a written scope document and a fixed-price quote covering the full engagement. No work starts without your sign-off.

2. Weeks 1-3
   02

   Assess and select

   Use case evaluation against value, feasibility, risk, and effort. Model selection tested on a sample of your actual inputs, not a generic benchmark. A focused single-use-case assessment or a production readiness review wraps here, in 1 to 2 weeks.

3. Weeks 2-5
   03

   Design the architecture

   RAG or agent architecture, the evaluation framework, and the cost model, specified before any production code gets written. This is the work that decides whether the system holds up past the demo.

4. Weeks 4-6
   04

   Deliver and hand off

   Written recommendations, a prioritised build roadmap, and the evaluation framework handed to your team. If you want us to build it next, the same team carries it into production, scoped separately.

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