Data and AI are siloed
Data lake on one side, data warehouse on the other, BI in a third and the model in a fourth. Every move between them produces a copy, and each new copy is one more version of the number someone will defend in a meeting.
Databricks partnership
Data and AI on one foundation,
with governance before scale.
Bunker is part of the Databricks Consulting and SI Partner Program. The Data Intelligence Platform brings data warehouse and data lake into one open foundation, and the data stays under its owner control.
Databricks by the numbers
Choosing the partner
You are looking for someone who implements Databricks properly. Let us get to it.
You already know the platform, and you chose well: the lakehouse architecture became the market standard because it settles the split between the data you store and the data you analyze. What changes from vendor to vendor is the method. How the scope is written before anyone touches code, who answers for each architecture decision, what stays documented, and what happens to that knowledge when someone leaves the team. This page shows Bunker's method, the five stages of the journey, and what you receive in writing at each one.
Open foundation. Unified governance. AI in production.
Bunker
Where Bunker comes in
The platform democratizes access to analytics and intelligent applications by marrying the customer data with AI models tuned to the business own characteristics. It is built on a lakehouse foundation, with open data formats and open governance, so that the data stays entirely within the control of whoever owns it.
Our work starts before that: which decision has to hold up, who decides, and on what number. Working backwards from the decision, we define the data model, the integration with the ERP already running, and what the platform has to support. We come in with delivered history behind us.
- Member of the Databricks Consulting and SI Partner Program
- Salesforce partner listed on AppExchange
- In-house practice in pricing, margin, and forecasting
- Delivery and support from a team in Brazil
The problem
Three known barriers, and one that shows up on the bill
The journey below exists to bring these four barriers down. The first three are the ones Databricks itself names as what blocks the data and AI vision. The fourth is the one we see most in practice.
Data privacy and control are challenged
Once data spreads across third-party tools, nobody knows where it sits or who opened it. The governance written in the policy does not hold up in the real environment.
Dependence on highly technical staff
Every business question depends on whoever can write the query. The queue grows, and the decision waits on one person calendar.
Workload cost with no owner
Consumption grows month after month and nobody knows which workload, team, or business question is paying the bill. This is the symptom that reaches finance first and governance last.
The journey
From foundation to democratization, in five stages
The progression is the platform own: first the open, unified foundation, then data and AI at scale, and finally data and AI democratized across the whole organization. Each stage delivers value on its own.
0
Foundation: Open Data Lake, Delta Lake, and Unity Catalog
All raw data in one place, unified storage for reliability and sharing, and unified security, governance, and cataloging on top. This is the stage that decides whether the other four have any ground to stand on.
Example: a single product and customer catalog, with a declared owner per domain and auditable permissions.
I
I. Ingestion and quality with Delta Live Tables
Data from the ERP, the CRM, and the operation comes in through declarative pipelines, with automated quality and reprocessing. The script only one person could run leaves the picture.
Example: order lines and invoices in one base, with explicit quality rules and reprocessable loads.
II
II. Orchestration and analytics with Workflows and Databricks SQL
Loads gain orchestration with cost optimized from past runs, and the query layer delivers the metric the business actually uses: margin, portfolio, forecast.
Example: contribution margin by product line, same rule from plan to actual.
III
III. Production agents with Agent Bricks
An agent built, evaluated and served where the decision repeats and the cost of error is known: document reading, price suggestion, demand classification. More than 100,000 agents have already been built on the platform, and what separates a pilot from production is automated evaluation and a declared guardrail.
Example: reading an order from a file and returning it structured for human review.
IV
IV. Natural language questions with Genie
The ontology layer learns the semantics of the business from the data itself, and the natural language question reaches governed data. Dependence on highly technical people starts to fall at this stage.
Example: a manager asks for the month margin and gets an answer traceable back to source.
V
V. State, lifecycle and cost with an owner
An agent in routine needs somewhere to keep state, and the serverless Postgres operational layer plays that role. With it comes what sustains the routine: monitoring, versioning, model governance and consumption with a declared owner. Without that, the agent stops keeping up with the operation and nobody notices.
Example: a metric on an operational panel, with model version history and consumption attributed by team.
The shift
What changes when there is only one foundation
Before
Silos, copies, and guesswork
- Data lake, warehouse, and BI each with its own version of the number
- Data spread across third-party tools, with no idea who opened it
- A business question stuck in a specialist queue
- An AI pilot that never leaves the slide deck
- Consumption growing with nobody accountable
After
Open foundation, decisions with evidence
- One official number, with a written definition and traceable origin
- Data under its owner control, in open formats
- Natural-language questions reaching governed data
- Models in routine, with guardrails, versioning, and an owner
- Consumption attributed to a team and a business question
Frequently asked questions
Databricks and Bunker
Is Bunker an official Databricks partner?
Yes. Bunker is part of the Databricks Consulting and SI Partner Program, with an active Partner Portal registration. This page exists because the program guidelines ask partners to publish their own Databricks page on their website.
Does Bunker already run AI in production for clients?
Yes. Bunker has AI engines running in client production today: reading orders from files and returning them structured for review, pricing with margin traced from plan to actual, and revenue forecasting over the portfolio. That practice, already in operation, is what we bring onto the platform.
Do you replace the ERP or CRM already running?
No. We work on top of what is already there. In most projects the ERP stays as the system of record, and the data platform becomes where analysis and AI happen.
Do we have to migrate everything at once?
No. The journey is designed in stages that deliver value on their own. It is common to start with a single data domain, prove the path, and only then widen it.
How long until the first visible result?
It depends on the state of the source data, and we measure that during framing before promising a date. A single domain with available data usually yields a useful read in weeks, not quarters.
What language do you work in?
Portuguese, with a team in Brazil. Delivery, documentation, and support run from the same time zone as our clients.
Does the platform lock my data into a closed format?
No. The foundation is a lakehouse on open data formats with open governance, and the design exists precisely so the data stays entirely within the control of whoever owns it. The platform runs on AWS, Azure, and Google Cloud, and that choice is usually already made by the client.
How does it start?
With a short framing: which decision has to hold, which data it requires, and where that data sits today. That framing produces the scope of the first stage, with an effort estimate.
Every quarter without a foundation is a quarter of deciding by opinion.
The cost does not show up on the cloud invoice. It shows up in the decision made on the wrong number that nobody could audit afterwards.
Want to see the standard we hold data projects to? See Fit to High Standards.
Related: Databricks On-Demand | Data and AI capabilities