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
- Home
- 3-Month Enterprise AI Framework
The 3-month enterprise AI framework.
A practical enterprise AI delivery guide for teams that want working systems, not pilots. It lays out how ML Data House takes a data, machine learning or automation use case from scoping to a production-ready system in about three months.
What you get
ChecklistA production-ready system, not a pilot.
- TimelineA 3-month delivery plan
- Check-insWeekly demos
- ChecklistPre-project readiness
Is your team ready to start?
Score your data, systems, people and compliance in a few minutes.
Open the checklistThe delivery framework
Three phases, one working system.
No 18-month transformation. Each month produces something real that you can see, test and decide on.
Build & Integrate
Develop pipelines, models and automations, connect them to your tools and iterate with weekly demos.
- Data pipelines and feature engineering
- Models, agents or dashboards built end to end
- Integration with your existing systems
Deploy & Hand Over
Harden, deploy and monitor the system, then train your team so it keeps working after we step back.
- Security and performance review
- Production rollout and monitoring
- Documentation, training and a next-phase roadmap
What you take away
What you have at the end of month three
A working system in production
Not a slide deck or prototype: a system your team can use on day one.
Documented architecture
Data flows, pipelines and design decisions written down for your team.
Dashboards and monitoring
Visibility into performance, data quality and results against agreed metrics.
A trained team
Hands-on sessions so your people can operate and extend what was built.
Measured against your goals
Results compared with the success metrics agreed in month one.
A roadmap for what is next
Clear next-phase options based on what we learned during the build.
The timeline
From data audit to production in three months.
The same cadence on every project: understand, design, build for production, then deploy and hand over, with weekly demos throughout.
- Month 1
Understand & Design
Gather data, pinpoint needs, define success metrics and agree the architecture before anything is built.
Weeks 1–4 - Month 2
Build & Integrate
Develop pipelines, models and automations, connect them to your tools and iterate with weekly demos.
Weeks 5–8 - Month 3
Deploy & Hand Over
Harden, deploy and monitor the system, then train your team so it keeps working after we step back.
Weeks 9–12
Where it applies
One framework, four service lines
Whether the outcome is a dashboard, a custom machine learning model, an AI agent or a web application, the three-month cadence stays the same. See all of our AI transformation services.
FAQ
Questions before you start
What is the 3-month enterprise AI framework?
It is the phased delivery approach ML Data House uses for data, machine learning and automation projects: understand and design in month one, build and integrate in month two, then deploy and hand over in month three. Every phase ends with something you can review and approve.
Why three months?
Three months is long enough to put a real system into production and short enough to keep scope focused and momentum high. Larger programmes are split into phases so each one delivers value on its own. The exact duration depends on scope, data readiness and the number of systems to integrate.
Can you work with the systems we already have?
Yes. We connect to the tools you already use through APIs and data connectors, and we only recommend replacing a system when it is actively blocking progress.
What do we need to provide?
Access to the relevant data and systems, a business owner who can make decisions, and a few hours each week for demos and feedback. The AI readiness checklist helps you check these things before kickoff.
What happens after month three?
You receive documentation, monitoring and training so your team can run the system. If you want ongoing help, such as new features or a next phase, we agree that separately.
How are security and compliance handled?
Security and privacy are part of the architecture from the first weeks: access controls, data protection and regulations such as GDPR are considered up front rather than added at the end. See our Privacy Policy and GDPR pages for how we handle data.
Ready to scope your first 3-month build?
Tell us about your data, systems and goals and we will help you plan the first phase.