A data consultancy london teams can hold to an outcome: governance, lineage and model decisions from engineers who have put retrieval platforms, screening models and analytics into production under audit — in your hours, from Ahmedabad, with the recommendation written down and a team that can build it.
This is the London version of our data and AI practices’ advisory work — an ai consultancy london firms can act on, not a slide deck. What changes here is UK GDPR, the FCA’s and ICO’s expectations of models and data, and how a UK firm contracts; the engineering is on the parent pages.
Book a data and AI conversationHow we work here
What a data and AI engagement with us includes, and how it runs from Ahmedabad for a UK firm.
London is four and a half to five and a half hours behind Ahmedabad, so your nine o’clock is our afternoon and our day runs into your early afternoon. Stand-ups, reviews and decisions happen live in your hours every working day; incident cover is agreed in the contract, not assumed.
Every engagement ends in a document your board and your DPO can read: the current state of the data and the models, the options with costs and risks, and one recommendation with a sequence that ships something in a quarter. No retainers that drift.
Every personal-data flow is mapped in discovery and the platform designed to UK GDPR and the Data Protection Act 2018 — lawful basis, retention, international transfer arrangements for the work we do from India, UK-region hosting where you or your customers require it — with the record kept for your DPO.
The people who advise you have built a production RAG platform, screening and transaction-intelligence models and the data platforms under them — so the recommendation is what we would do if it were our build, and the same hands can build it if you want.
What we build here
The data and AI advisory work UK businesses commission — the shape an ai agency london buyers compare us to would sell as projects — each row exiting to the practice page that goes deep on it.
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A reading of what you have — sources, pipelines, warehouse, lineage, quality, ownership — against what the business and UK GDPR require, with the gaps named and a target stack costed. Three to five weeks, one document.
02
Where AI pays in your business, what the data is ready for, what governance a UK deployment needs, and a roadmap whose first quarter ships — from engineers who have run retrieval and prediction models in production.
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Why the dashboards are not trusted, which metrics the business decides on, and a reporting architecture — embedded or standalone — that finance and operations use instead of spreadsheets.
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For firms whose models face the FCA, the PRA or the ICO: documentation, explainability, monitoring for drift and bias, human oversight and the evidence trail a regulator asks for — designed into the platform, not written after the finding.
The market's own rules
The regimes a data and AI consultancy in London has to account for before recommending anything.
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Lawful basis, purpose limitation and data-protection impact assessments for analytics and models, the rules on automated decision-making, and international transfer arrangements for work done from India — the baseline any recommendation must satisfy.
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The regulator’s expectations on fairness, transparency, explainability and accountability in AI systems processing personal data — mapped onto the model lifecycle before a model is chosen.
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Model risk management, consumer-outcome duties and third-party risk for regulated firms — the standard our screening and monitoring work is built to, and the one a UK financial firm’s AI has to meet.
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Where a warehouse, a vector store or a model endpoint may run, given your customers’ contracts and your regulator — UK regions, private endpoints and key management decided in the review, costed.
Consultancy that ignores this map produces recommendations a UK firm cannot implement. Ours starts from it.
Work
3 published data and AI builds — none for a London client, each the shape of what this page advises on.
ManufacturingDigiWagon built a production-grade RAG platform for a global CPG leader, using Azure AI to help R&D and compliance teams quickly search, synthesise, and generate insights from millions of scattered documents.
ManufacturingAn AI/ML-enabled prediction engine connecting plant data to automated forecasts, reducing human error, speeding up decisions, and scaling seamlessly across plants.
FinTechConsolidated AML screening, automation, and case management into a single AI-powered platform, enabling faster onboarding, reduced false positives, and real-time compliance visibility for financial institutions.
Industries
The industry pages behind the firms that bring us data and AI decisions.
01
Screening, monitoring and decision models for regulated firms, with the governance the FCA and the ICO expect.
FinTech software development02
Compliance-technology firms building AI into screening and monitoring products for UK institutions.
RegTech software development03
Operational data, predictive maintenance and knowledge platforms for UK manufacturers and their supply chains.
Manufacturing software developmentAlso from here
The other London pages, from the same team.
01
Bespoke platforms for London teams, built to UK GDPR and the FCA's expectations.
Read the London page02
Commerce builds that handle UK VAT, cross-border rules and peak-season load.
Read the London page03
Sequencing, trade-offs and what to modernise first — a plan that ships.
Read the London pageInsights
How we think about the decisions London firms bring us: building the modern data stack, the governance gap in designing auditable AI for compliance in Europe, and why BI dashboards fail.
FAQ
Direct answers to what London teams ask before a data and AI consultancy engagement.
A written reading of your data estate and its governance, a target architecture with costs, a metrics definition the business signs off, and one shipped thing — a governed pipeline, a first model in production or a dashboard people use — so the roadmap is proven, not presented. Ninety days ends with a decision, not a deck.
Yes, as the baseline of every recommendation: lawful basis and impact assessments for analytics and models, the rules on automated decision-making, retention and the transfer arrangements for work done from India. Your DPO gets the record; production data stays in your environment wherever the work allows it.
A consultancy decides what to build and why — architecture, governance, metrics, model choice — and puts it in writing; an engineering team builds it. We do both, separately scoped: the advisory is a document you own and can take to any vendor, and the build is a second decision, made only if you want the same hands.
Yes: retrieval-augmented systems grounded in your own documents, guardrails, evaluation and logging, private endpoints and the data-protection impact assessment the processing warrants — the shape of the production RAG platform we have published. We also say when a model is the wrong tool and a query would do.
By fixed scope, never by open-ended retainer: we agree what will be reviewed, who will be interviewed and what the document will contain, then quote a figure with its assumptions in conversation. Programmes are staged, each stage scoped on its own, so you can stop at any document you find sufficient.
UK GDPR and the ICO’s AI and data-protection guidance, and the FCA’s and PRA’s expectations of model risk, consumer outcomes and outsourcing, from having built screening and monitoring platforms held to them. We work with your counsel on the legal position; we own the technical answer.
We will tell you in the first conversation whether it is a three-week review or a quarter’s programme, and what the document will contain.
