

We define data strategies that make AI, and all positive change, possible, aligning architecture, governance and operating models to turn ambition into safe, scalable outcomes.
We define data strategies that make AI, and all positive change, possible, aligning architecture, governance and operating models to turn ambition into safe, scalable outcomes.
We design and deliver modern data and AI platforms that transform trusted data into real time insight, prediction and action using appropriate mixes of ML, GenAI, and multi-modal AI.
We design and deliver modern data and AI platforms that transform trusted data into real time insight, prediction and action using appropriate mixes of ML, GenAI, and multi-modal AI.
From strategy to scale, we operate and optimize data and AI capabilities so they continue to enable innovation as your business evolves.
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Data Strategy
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Master Data Management
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Amplifi has strategic partnerships with a number of leading technology solutions, enabling us to deliver measurable results through expert consultancy and best-of-breed technology.
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We're the go-to data consultancy that delivers measurable results for our clients. How? Through our team of dedicated data experts.
About Us
Amplifi helps you connect the data your business depends on to the analytics and AI that move it forward. As a Databricks partner, we combine expertise in data management and governance with engineering, analytics, and AI delivery to build solutions around your business priorities.


Databricks brings data engineering, analytics, and AI together on a unified platform. Its lake house architecture allows teams to work from a shared data foundation, while Unity Catalog provides governance across data and AI assets.
Amplifi connects that platform to the realities of your business: the systems you run, the meaning behind your data, and the decisions you need to make. We help you bring reliable data into Databricks and put it to work in reporting, applications, and AI workflows.
We start by understanding the decision, process, or customer experience you want to improve. Then we assess the data, architecture, and governance needed to support it, shaping a delivery plan around your existing environment.
Our teams connect strategy with implementation, bringing data management and AI expertise into the same engagement. We build business definitions, ownership, and quality controls into the solution, then test it against the use case and prepare your teams to operate and extend it.
We work with enterprise data, technology, and business leaders who need to modernize complex data environments and deliver useful analytics and AI. Our experience in master data management, governance, and data quality helps us address the foundations that these initiatives depend on.
Whether you are evaluating Databricks, expanding an existing environment, or designing an AI application, we help you connect platform decisions to business needs. We bring understanding of organizations with complex customer, product, and operational data.

When priorities compete and AI initiatives lack direction, it is hard to know where to invest. Our Data & AI Strategy work connects your business goals to practical use cases, architecture decisions, and a phased Databricks roadmap.
Disconnected systems and inconsistent definitions undermine confidence in your data. Through Trusted Data Foundations, we bring integration, data quality, master data management, and governance together, so your Databricks environment has reliable data and clear business context.
When every team prepares its own data, reporting becomes difficult to reconcile and maintain. Our Enterprise AI & Analytics services help you build reusable data pipelines and models on Databricks, connecting trusted information to dashboards, predictions, and operational decisions.
An AI prototype needs more than a working model to become a dependable business tool. Our Practical Generative & Agentic AI work brings data pipelines, model access, evaluation, and orchestration together on Databricks, with controls designed around how the solution will be used.
Planning a new platform, strengthening your data foundations, or taking an AI use case into production? Tell us what you want to achieve. Our team will help you explore the next step with Databricks.
Your needs continue to evolve after implementation. Amplifi’s managed services help you support and improve your data and AI capabilities over time. Talk to us about ongoing support, optimization, and enhancements around your Databricks environment.
Amplifi helps you connect the data your business depends on to the analytics and AI that move it forward. As a Databricks partner, we combine expertise in data management and governance with engineering, analytics, and AI delivery to build solutions around your business priorities.
What can Amplifi help us do with Databricks?
We help you plan and deliver data engineering, governance, analytics, and AI solutions on Databricks. Our work connects your platform to existing systems and business priorities, from preparing trusted data to building applications that use it.
Can you help if we already use Databricks?
Yes. We can help you strengthen data quality and governance, connect additional data sources, and develop analytics and AI use cases that extend the value of your existing environment.
How does Databricks fit with our MDM platform?
Databricks can prepare and validate data for an MDM platform and make mastered data available for analytics and AI. Amplifi helps define those integrations and the ownership and quality controls around them, so the platforms work together.
What role does Unity Catalog play?
Unity Catalog provides controls for access, discovery, and lineage across data and AI assets in Databricks. Amplifi helps connect those capabilities to business definitions, ownership, and governance processes so teams can understand and use data appropriately.
Can you help us move an AI prototype into production?
Yes. We help design the data pipelines, model access, evaluation, and operational workflows needed to support the use case. The starting point is understanding how the application will be used and what it needs to deliver reliably.
Do we need to move all our data at once?
No. We can plan a phased approach around priority use cases, dependencies, and existing investments. That lets you establish the foundations for one workload and build on them as your needs develop.