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Data & AI consultancy · UK

Modern data. Practical AI. Built AI-native.

Dataframe designs and builds modern data platforms, migrates legacy estates with AI, and delivers machine learning and AI solutions that make a measurable difference to how your organisation works.

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dataframe.services
serviceoutcome
01AI-Native Data & AnalyticsOne trusted version of the truth
02AI-Driven Data MigrationLegacy retired, faster and with less risk
03Machine Learning EngineeringModels in production, monitored
04Bespoke AI SolutionsAI built around your business
05AI Adoption & EnablementMeasurable value from AI tools

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Our approach to AI

We don’t just build AI. We build with it.

Most consultancies treat AI as something to sell. For us it is also how we work. We use AI across our delivery to analyse legacy code, write and review data transformations, generate tests and documentation, and validate data at scale. Experienced engineers still review every line.

So projects move faster, cost less, and end up better tested and better documented than traditional delivery usually allows. And because we use these tools every day, our advice on adopting AI comes from real experience, not slideware.

  • Faster

    AI takes on the repetitive engineering, so our people can focus on design, judgement and your business problem.

  • Higher quality

    Automated testing, documentation and reconciliation are built in from day one, not left to the end.

  • AI-ready by design

    Everything we build is structured, documented and governed, so AI can use your data safely, now and as the technology changes.

What we do

Five ways we help you get value from data and AI

You might be modernising your data platform, putting your first model into production, or rolling out AI across the business. We can lead the work end to end, or join your team and work alongside them.

01

AI-Native Data & Analytics

Modern data platforms, pipelines and reporting, built faster and to a higher standard with AI used throughout delivery.

A modern data platform your business can trust

We design and build complete data platforms on the modern data stack. That covers everything from getting data in and transforming it, through a governed semantic layer, to dashboards and self-service analytics. AI speeds up every stage of our delivery. The platform itself is built for both people and AI: well-modelled, documented and governed data that analysts, business users and AI assistants can all rely on.

What we deliver

  • Data platform strategy, architecture and tool selection
  • Data ingestion and pipelines, batch and near real-time
  • Data modelling and transformation, following analytics engineering best practice
  • A semantic layer with consistent definitions for business metrics
  • Dashboards, reporting and self-service analytics
  • Questions in plain English answered from your data (“ask your data”)
  • Data quality, testing, monitoring and CI/CD
  • Governance, access control, lineage and documentation

Outcomes

  • One trusted version of the truth across the organisation
  • Faster from question to answer
  • A foundation that scales for machine learning and AI

Typical technologies

  • Snowflake
  • Databricks
  • BigQuery
  • Microsoft Fabric
  • dbt
  • Fivetran
  • Airbyte
  • Airflow
  • Dagster
  • Power BI
  • Tableau
  • Looker

02

AI-Driven Data Migration

Move off legacy warehouses, ETL and BI onto the modern data stack, faster and with less risk, with AI doing the heavy lifting.

Leave legacy behind faster, at lower cost and with less risk

Legacy migrations are known for being slow. There are thousands of stored procedures, undocumented ETL jobs, and reports that nobody fully understands. We use AI to do the heavy lifting: mapping your existing estate, translating code to modern tools, generating documentation and tests, and automatically checking that old and new systems produce the same results. Our engineers own the design and review every change. You reach a modern platform sooner and can switch off legacy systems with confidence.

How it works

  1. 1

    Assess. Automated discovery of your estate: code, dependencies, lineage, usage and complexity.

  2. 2

    Plan. Target architecture, migration waves, and a realistic plan based on evidence.

  3. 3

    Convert. AI-assisted translation of SQL, stored procedures, ETL and reports, reviewed by engineers.

  4. 4

    Validate. Automated data reconciliation and parallel running to prove the results match.

  5. 5

    Cut over. A controlled switch-over, user onboarding, and decommissioning of legacy systems.

We commonly migrate from

  • SQL Server (SSIS, SSRS, SSAS)
  • Oracle
  • Teradata
  • Informatica
  • Talend
  • Hadoop
  • SAP BusinessObjects
  • Cognos
  • QlikView
  • Processes built on spreadsheets and Access

Outcomes

  • Shorter migration timelines and lower delivery cost
  • A tested, documented codebase in place of knowledge held in people’s heads
  • Lower licence and infrastructure costs once legacy systems are retired

03

Machine Learning Engineering

Machine learning models designed, built and deployed to production, then monitored so they keep performing.

Machine learning that makes it to production, and stays there

Many models never leave the notebook. We take machine learning from idea to production. We start with the business decision the model needs to improve. Then we design, build, validate and deploy the model, using MLOps practices that keep it accurate, explainable and cost-effective long after go-live.

What we deliver

  • Use-case framing, feasibility and value assessment
  • Data preparation and feature engineering
  • Model development, evaluation and benchmarking
  • Explainability, bias and fairness checks
  • Deployment as batch pipelines or real-time APIs
  • MLOps: CI/CD, model registry, monitoring, drift detection and retraining

Common use cases

  • Demand and sales forecasting
  • Customer churn and propensity
  • Pricing optimisation
  • Fraud and anomaly detection
  • Recommendations and personalisation
  • Customer segmentation
  • Predictive maintenance
  • Document and image classification

Typical technologies

  • Python
  • scikit-learn
  • XGBoost
  • LightGBM
  • PyTorch
  • MLflow
  • Databricks
  • AWS SageMaker
  • Azure Machine Learning
  • Google Vertex AI

04

Bespoke AI Solutions

Custom AI applications, assistants and agents built around your data, processes and systems.

AI built around your business, not the other way round

When off-the-shelf tools don’t fit, we build AI solutions around your data, processes and systems. These include applications powered by large language models (LLMs), assistants grounded in your own knowledge, and AI agents that carry out multi-step work across your tools. We build them for production from the start, with evaluation, guardrails, security and cost control included.

Examples of what we build

Knowledge assistants
Accurate answers drawn from your own documents, policies and data.
Document processing
Extracting, classifying and validating data from contracts, invoices, claims and forms.
Conversational analytics
Questions in plain English answered from your governed data.
AI agents and workflow automation
Triage, case handling, reporting and back-office processes.
Customer-facing assistants
Built into your website, app or service channels.

What we deliver

  • Use-case discovery and rapid prototyping
  • Solution architecture and model selection
  • Retrieval-augmented generation (RAG) and knowledge pipelines
  • Agent design and integration with your systems and APIs
  • Evaluation frameworks, guardrails and human review steps
  • Secure deployment, monitoring and cost management

Model-agnostic. We work with leading models from Anthropic, OpenAI, Google and others, as well as open-weight models. Where data residency matters, we deploy them through your own cloud provider.

05

AI Adoption & Enablement

Get real, measurable value from off-the-shelf AI tools, securely and across your whole organisation.

Turn AI tools into everyday business value

Most organisations already have access to powerful AI tools such as Microsoft Copilot, ChatGPT, Claude and Gemini. But adoption is patchy and the value is hard to prove. We help you choose the right tools, roll them out securely, and put sensible governance in place. We also help your people build the skills and habits that make AI part of everyday work, with results you can measure.

What we deliver

  • AI readiness and opportunity assessment
  • Finding use cases and ranking them by value and feasibility
  • Tool selection, configuration and secure rollout
  • AI policy, governance and risk management
  • Training for leaders, teams and power users
  • Workflow redesign, playbooks and AI champions programmes
  • Measuring adoption and impact

Outcomes

  • Confident, safe and consistent use of AI across teams
  • Measurable productivity gains in priority workflows
  • A clear, prioritised roadmap for what comes next

How we work

From first conversation to lasting capability

  1. Step 1

    Discover

    We get to know your goals, data, systems and constraints, and agree what success looks like in measurable terms.

  2. Step 2

    Prove

    We show value quickly with a focused prototype or proof of value, so your decisions rest on evidence.

  3. Step 3

    Build

    We deliver production-grade solutions in short iterations, sped up by AI and grounded in engineering best practice.

  4. Step 4

    Embed

    We hand over documented, tested work and train your team. We stay on to support and improve things where you need us.

Why Dataframe

Why organisations work with Dataframe

  • AI-native delivery

    AI is part of how we work, not just what we sell. That means faster delivery and practical advice from first-hand experience.

  • Engineering first

    Version-controlled, tested, documented and monitored. Built to run in production, not just to demo.

  • Independent advice

    We recommend the tools and models that fit your needs and budget.

  • Focused on outcomes

    Every engagement starts with the business problem and a clear measure of success.

  • You own it

    Your code, your data, your IP. We pass on the knowledge so your team can run and extend what we build.

  • Secure and compliant

    Designed around UK GDPR, data residency and least-privilege access from day one.

Engagements

Ways to work with us

Discovery and assessment
A short engagement with a fixed scope. We assess your data and AI landscape and produce a prioritised, costed roadmap.
Project delivery
A Dataframe team delivers agreed outcomes end to end, on a fixed-price or time-and-materials basis.
Embedded experts
Experienced data, ML and AI engineers join your team to speed up delivery and build skills.
Training and workshops
Hands-on sessions for leadership, technical teams and business users.
Ongoing support
Monitoring, maintenance and continuous improvement for the platforms and solutions we build.

Technology

Built on the tools you already know

We’re technology-agnostic and work across all major clouds and the modern data and AI ecosystem.

areatechnologies
CloudAWS · Microsoft Azure · Google Cloud
Data platformsSnowflake · Databricks · BigQuery · Microsoft Fabric
Ingestion and orchestrationFivetran · Airbyte · Airflow · Dagster
Transformation and qualitydbt · SQL · Python
BI and analyticsPower BI · Tableau · Looker
ML and MLOpsscikit-learn · PyTorch · MLflow · SageMaker · Azure ML · Vertex AI
Generative AIAnthropic Claude · OpenAI · Google Gemini · open-weight models

Contact

Let’s talk about your data

Tell us what you’re working on: a platform to modernise, a model to deploy, or an idea you’d like to test. We’ll get back to you within one working day.

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Email

Email us

CompanyDataframe Limited
Company number13975651
Registered inEngland and Wales
Registered office167–169 Great Portland Street, 5th Floor, London, W1W 5PF