Understand & Design
Gather data, pinpoint needs, define success metrics and agree the architecture before anything is built.
- Data and systems audit
- Use-case scoring and success metrics
- Solution architecture and delivery plan
ML Data House helps organizations plan and deliver AI transformation: AI agents and web automation, data analytics and custom machine learning, taken from idea to a production-ready system in about three months.
AI transformation is the process of using artificial intelligence, machine learning and automation to change how your business makes decisions, serves customers and runs its everyday work.
It is less about buying a tool and more about choosing the right problems, preparing your data and putting working systems into production. We help you do that one practical step at a time, so every phase delivers something you can see and measure.
Read how our 3-month framework worksMost businesses begin with one clear problem. These are the areas where we most often help.
Web automation and AI agents that handle lead generation, data entry, CRM updates, reporting and document processing, so your team spends its time on higher-value work.
Explore web automation & AI agentsInteractive dashboards and analytics that bring data from many sources into one view of your KPIs, with real-time reporting for decision-makers.
Explore data analysis & visualizationCustom machine learning for demand forecasting, churn and retention scoring, risk assessment, fraud detection and recommendations.
Explore custom machine learning modelsChatbots and AI agents for customer engagement, notifications and automated decisions, plus language models for knowledge search and smart email replies.
See AI agents & chatbotsStructured data collected from websites, forms and APIs and turned into analytics, alerts and market intelligence.
See web scraping & data extractionIntegration and orchestration across platforms, APIs and databases, and custom web applications that bring AI into the tools your team already uses.
See web application developmentNo 18-month transformation. Each month produces something real that you can review, test and decide on.
Gather data, pinpoint needs, define success metrics and agree the architecture before anything is built.
Develop pipelines, models and automations, connect them to your tools and iterate with weekly demos.
Harden, deploy and monitor the system, then train your team so it keeps working after we step back.
Get insights and automation without building a large data team.
Reduce manual effort and cost by automating workflows and reporting.
Improve product performance and user experience with predictive analytics.
Embed AI and analytics into core systems in a reliable, scalable way.
Turn experimental data into clear, reproducible insights.
We adapt analytics, machine learning and automation to the way your sector works.
AI transformation means using artificial intelligence, machine learning and automation to change how a business works: how it makes decisions, serves customers and runs everyday processes. It is less about buying a tool and more about choosing the right problems, preparing your data and putting working systems into production.
Start with one use case that has a clear owner, data you can reach and a result you can measure, such as automating a manual reporting process or forecasting demand. Our AI readiness checklist and 3-month framework help you choose a first use case and prepare for it.
It depends on scope and data readiness. Our framework is designed to take a first use case from scoping to a production-ready system in about three months. Larger programmes are split into phases so that each phase delivers value on its own.
The cost depends on the use case, the state of your data and the number of systems to connect, so we scope it together before any commitment. Schedule a short meeting and we will talk through your goals and give you a clear plan for the first phase.
No. Auditing the data and fixing what matters is part of the first month. Having access to the data and an owner who can make decisions makes the start much faster, and the readiness checklist shows what to prepare.
Traditional automation follows fixed rules. AI agents can work with variable inputs, such as reading messages or documents, and decide the next step within guardrails you define. We build both, and recommend the simplest approach that solves the problem.
Yes. We connect to existing CRMs, ERPs, databases and internal tools through APIs and data connectors, and we only recommend replacing a system when it is actively blocking progress.
We work with medical and healthcare, finance and banking, retail and e-commerce, manufacturing and supply chain, and education and research organizations.
Tell us about your goals, data and systems and we will help you plan the first phase.