Most enterprise AI programmes fail between proof-of-concept and production. We work specifically at that gap — from RAG pipelines and Vertex AI fine-tuning to the governance architecture that makes AI deployable in a regulated environment.
Most enterprise AI programmes fail between proof-of-concept and production. The technology works in the sandbox; it stalls when it meets a procurement committee, a compliance review, or an infrastructure team that was not involved in the design. AmehX works specifically at that gap.
We have shipped RAG pipelines that cut analyst reporting time by 60%, fine-tuned LLMs on Vertex AI and OpenAI endpoints for regulated B2B SaaS platforms, and built MLOps infrastructure that a client's internal team can actually maintain after we leave.
In regulated environments — FCA-supervised firms, Gambling Commission licensees, GDS-assessed government services — AI deployment requires a governance architecture that most organisations have not built yet. We design that architecture before we write the first line of production code.
Google Gemini · Vertex AI · OpenAI · Microsoft Azure AI · Pinecone · Weaviate · Jupyter · Python · LangChain · LlamaIndex
The FCA, Gambling Commission, and GDS have not banned generative AI. What they require is a governance architecture — explainability, data lineage, human-in-the-loop design, and audit trails — that most organisations have not yet built. We design that architecture first, then deploy.
Financial Services · iGaming & Lottery · UK Government · Enterprise SaaS · Insurance
FCA AI/ML guidance · GDS Service Standard · Gambling Commission · EU AI Act · ICO guidance on AI
Read our practitioner's account of deploying generative AI inside a regulated business — covering FCA, GDS, and Gambling Commission requirements in practice.
Read the insightAI in iGaming requires Gambling Commission-compliant governance. We design responsible gambling AI features and KYC automation against regulatory standards.
View capability →AI products require operating model changes as well as technical delivery. We design the product team structure, funding model, and governance forums alongside the AI system.
View capability →AI copilots and embedded intelligence inside SAP, D365, and Salesforce require the same production rigour as standalone AI products. We deliver both.
View capability →AI product management consulting helps regulated organisations move from AI experiments to production systems. We work at the gap between proof-of-concept and production — defining strategy, building governance architecture, and delivering AI pipelines that a regulated business can operate and audit.
We design the governance architecture before we build anything: explainability requirements, data lineage documentation, human-in-the-loop design, and audit trail architecture. The FCA, Gambling Commission, and GDS have not banned generative AI — they require that existing obligations around fairness, explainability, and consumer harm prevention apply to AI-assisted decisions. We ensure they do.
RAG (Retrieval-Augmented Generation) retrieves from a defined document corpus and generates responses grounded in retrieved evidence, rather than from training weights alone. Every output can be traced to source documents — making it auditable. For regulated contexts where you need to know why the model said what it said, RAG is almost always the right architecture over fine-tuning alone.
Typically 3–6 months for an initial production deployment in a regulated environment. The governance architecture — risk classification, data lineage baseline, model evaluation framework, human-in-the-loop design — takes 4–6 weeks before build starts. Organisations that skip this step typically call us 6 months later to help unwind a deployment that has attracted regulatory attention.
Yes. We embed alongside internal teams rather than replacing them. Our role is to provide product strategy, governance architecture, and delivery management that accelerates what the internal team can build. The system the internal team inherits should be one they can maintain and extend without us.
Tell us where your AI programme is stalling and we will be direct about whether we can help.
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