MODUS CREATE AND DATABRICKS
Turn data and AI into products people use
Modus Create combines Databricks’ data and AI platform with product strategy, design, and engineering to turn data into real business value.
OUR PARTNERSHIP
How Modus Create and Databricks work together
Databricks provides a unified foundation for data engineering, analytics, governance, and AI. Modus Create combines the Databricks Data Intelligence Platform with product strategy, design, and engineering expertise to help organizations turn enterprise data into secure, scalable digital products and applications.
Together, we help organizations unlock the value of their data, modernize workflows, support better decision-making, and deliver digital experiences that meet evolving business needs.
Databricks provides the data and AI foundation. Modus Create turns it into products that deliver business value.
HOW MODUS CREATE HELPS
Get more from your Databricks investment
A Databricks platform gives teams the tooling. Modus Create covers the work around it, from deciding which use cases justify the investment to putting the result in front of the people who use it.
Discovery workshops
We define the use-case portfolio, establish data baselines, map user personas, and build the business value model that ranks what gets built first. Stakeholders get a shared, evidence-based view of where Databricks will pay off.
Data foundation and governance
We configure Unity Catalog access controls, automated data contracts, and pipelines, and set up Lakebase operational data syncs. Analytical and operational systems then work from the same governed data.
Custom app and GenAI engineering
We build interactive Databricks Apps, Mosaic AI agents, and natural-language workflows with Genie. These are designed for the people who use them and connected to the applications and processes they already rely on.
OUR APPROACH
From use case to production: how we deliver on Databricks
We usually structure Databricks projects around five stages: Align, Foundation, Build, Launch, and Scale, each with a defined outcome.
1 — Align
We identify the highest-value opportunities, assess the data landscape, define user needs, and build the business case.
Outcome: an agreed scope, success metrics, and a prioritized first-release backlog.
2 — Foundation
Next comes the data architecture, governance, security, access model, and pipelines the use case depends on.
Outcome: an approved architecture, an access model, and measurable acceptance criteria.
3 — Build
This stage covers the data pipelines, AI models or agents, APIs, and user experience. Each is validated through data-quality checks, AI evaluations, performance benchmarks, and user testing.
Outcome: a validated solution backed by functional, usability, data-quality, and AI evaluation results.
4 — Launch
We prepare the release, complete security and governance reviews, secure production approval, and monitor the deployment.
Outcome: a production release that meets the agreed acceptance criteria and is stable enough for operational use.
5 — Scale
Once the solution is live, we optimize cost and performance, extend it to additional use cases, transfer knowledge, and retire superseded systems where it makes sense.
Outcome: a clear ownership handoff, an optimization plan, and documented retirement of replaced assets where applicable.
WHY MODUS CREATE
One partner across data, AI, and product engineering
End-to-end technical capabilities
Connect data engineering, cloud and platform engineering, AI, product strategy, UX/UI design, and application development through an integrated delivery approach.
Industry depth where it matters
Hundreds of products delivered across life sciences, automotive, financial services, and retail. That includes regulated environments, where HIPAA, GxP, and 21 CFR Part 11 shape how data is governed and how AI is validated before anyone relies on it.
Certified across the delivery stack
As an AWS Premier Tier Partner, Atlassian Platinum Solution Partner, and GitHub Verified Partner, we build on cloud, collaboration, and DevOps foundations that enterprise teams already trust. Your Databricks pipelines, AI solutions, and applications get the performance, security, and scalability that come with that expertise.
LET'S GET STARTED
Talk to Modus Create
Big challenges need bold partners. Let’s talk about where you want to go — and start building the path to get there.
"A lot of startups would benefit from the experience Modus Create brought to the table. It has set a very solid foundation on which we can grow now."
"From a business value point of view, the AVP ensures cost savings of about 30%, and the time to market has been reduced from weeks to hours."
"Modus Create understood our challenges and was committed to provide a solution that met our goals."
"Modus Create was an amazing partner for our GitHub migration to the GitHub cloud. We needed someone that could come in with expertise and show us the way, not just supplement our capacity."
Frequently asked questions
What teams ask before starting a Databricks project
What is Databricks used for?
Databricks is a data and AI platform used to process and manage enterprise data, perform analytics, develop machine learning and generative AI solutions, and build data-driven applications. It provides a foundation for bringing data engineering, analytics, governance, and AI capabilities together.
When should an organization bring in a Databricks partner?
When the work around the platform stalls, not the platform itself. Typical signs are disconnected source systems feeding the pipelines, enterprise data not governed well enough for AI to rely on, dashboards and models that few people use, and AI initiatives stuck at the pilot stage. In regulated environments, HIPAA, GxP, or 21 CFR Part 11 requirements add another trigger. A partner can also help before any build starts, by assessing the data landscape and defining the business case.
Does Modus Create work only on new Databricks builds, or also on existing environments?
Modus Create works on both new Databricks builds and existing environments. For organizations already invested in Databricks, the work centers on modernizing the architecture around the platform, linking it with existing systems across environments, and then preparing data for AI, building digital products, and embedding AI in business workflows. For new builds, engagements start by assessing the data landscape and defining the business case, before the architecture, governance, and pipelines are set up.