Enterprise data lake consulting services
Data lake consulting that ends with a governed lakehouse you actually run in production, not a slide deck of recommendations someone else has to build.
What you actually get
A data lake that's already trustworthy the day it goes live, not something you spend the next year patching for governance gaps.
What you actually get
A data lake that's already trustworthy the day it goes live, not something you spend the next year patching for governance gaps.
01
A lakehouse you can prove is compliant
Access logs, lineage, and purpose tagging answer the audit question in seconds instead of weeks.
02
Faster delivery than a typical build
Production-ready in around three weeks on your own Databricks workspace, not a multi-quarter program.
03
Governance that doesn't live in someone's memory
Policies enforced once, at the source, applied consistently across every team and every tool that touches the data.
04
A foundation that supports what comes after
Once the data is governed and trustworthy, it's actually usable for the AI and analytics work most data lake projects were meant to enable in the first place.
Built across financial and regulated environments
Alternative asset management
Specialty lending
Wealth management
PE-backed platforms
Experience with clients backed by
Built across financial and regulated environments
Experience with clients backed by
What we actually build
A governed lakehouse on infrastructure you control, not a data lake as a metaphor for "somewhere we dumped the files."
What we actually build
A governed lakehouse on infrastructure you control, not a data lake as a metaphor for "somewhere we dumped the files."
Medallion architecture done properly
Raw data ingested into bronze, cleaned and typed in silver, business-ready tables in gold, with lineage tracked the whole way through.
Governance built into the platform, not the dashboard
Column masks and row filters applied at the source, so every tool querying the data sees the same enforced policy.
Pre-built connectors where they exist
MySQL, PostgreSQL, Redshift, Salesforce, file and API sources wired up without custom integration work for every single one.
How delivery actually works
A data lake that takes a year to go live has usually already failed. We work in weeks, not quarters, and we show progress early.
You see a working demo in the first meeting
Not slides. An actual look at how the architecture will handle your data, before the engagement even starts.
Production readiness in around three weeks
On your own Databricks workspace, live and governed, not a staging environment that quietly never gets promoted.
The audit trail comes with it, not after
Who touched what data, when, and why, answerable as a query instead of a six-week internal investigation.
We stay accountable to the KPI, not the ticket count
Data readiness, delivery speed, and audit resolution time are the numbers that matter, and we track all three.
You see a working demo in the first meeting
Not slides. An actual look at how the architecture will handle your data, before the engagement even starts.
Production readiness in around three weeks
On your own Databricks workspace, live and governed, not a staging environment that quietly never gets promoted.
The audit trail comes with it, not after
Who touched what data, when, and why, answerable as a query instead of a six-week internal investigation.
We stay accountable to the KPI, not the ticket count
Data readiness, delivery speed, and audit resolution time are the numbers that matter, and we track all three.
You see a working demo in the first meeting
Not slides. An actual look at how the architecture will handle your data, before the engagement even starts.
Production readiness in around three weeks
On your own Databricks workspace, live and governed, not a staging environment that quietly never gets promoted.
The audit trail comes with it, not after
Who touched what data, when, and why, answerable as a query instead of a six-week internal investigation.
We stay accountable to the KPI, not the ticket count
Data readiness, delivery speed, and audit resolution time are the numbers that matter, and we track all three.
You see a working demo in the first meeting
Not slides. An actual look at how the architecture will handle your data, before the engagement even starts.
Production readiness in around three weeks
On your own Databricks workspace, live and governed, not a staging environment that quietly never gets promoted.
The audit trail comes with it, not after
Who touched what data, when, and why, answerable as a query instead of a six-week internal investigation.
We stay accountable to the KPI, not the ticket count
Data readiness, delivery speed, and audit resolution time are the numbers that matter, and we track all three.
Building Data-First AI in Production for regulated and data-intensive industries?
Assess your AI readinessHow we take this on
Most data lake projects stall because nobody defined what "done" looks like before the build started. We start there.
How we take this on
Most data lake projects stall because nobody defined what "done" looks like before the build started. We start there.
Design the architecture before touching infrastructure
Bronze, silver, gold layering, lineage, and access control decided up front, not retrofitted once the lake already has a mess in it.
Draw the line on ownership early
We own the architecture and the build. You own the business context and what the data actually needs to support.
Set a target before writing a single pipeline
A working demo early, a production timeline that's actually realistic, and a KPI both sides agree matters.
Look at what's actually in the way
Source systems, existing pipelines, governance gaps, whatever is slowing your data down before it can be trusted or used.
Design the architecture before touching infrastructure
Bronze, silver, gold layering, lineage, and access control decided up front, not retrofitted once the lake already has a mess in it.
Draw the line on ownership early
We own the architecture and the build. You own the business context and what the data actually needs to support.
Set a target before writing a single pipeline
A working demo early, a production timeline that's actually realistic, and a KPI both sides agree matters.
Look at what's actually in the way
Source systems, existing pipelines, governance gaps, whatever is slowing your data down before it can be trusted or used.
Design the architecture before touching infrastructure
Bronze, silver, gold layering, lineage, and access control decided up front, not retrofitted once the lake already has a mess in it.
Draw the line on ownership early
We own the architecture and the build. You own the business context and what the data actually needs to support.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
PARTNER + CERTIFICATE
Recognized by Platform Leaders. Trusted in Production.
Frequently asked questions
Most engagements end with a recommendation. Ours ends with a governed lakehouse running in production on your own infrastructure, with lineage and access control already enforced.
No. We build the lakehouse layer on top of your existing sources, whether that's a warehouse, operational databases, or a mix of both.
Column-level tagging and purpose declarations are built into the platform, so violations get flagged before an auditor or a breach finds them, not after.
Every column is tagged with what it's allowed to be used for, every policy is enforced at the source rather than in individual dashboards, and every access event is logged and queryable.
Yes. Tableau, Power BI, Excel, and embedded analytics all connect to the same governed gold tables, so the policy enforcement follows the data regardless of what's querying it.
We start with a working demo against your actual use case, then define the delivery timeline and the KPIs the engagement is accountable to before any infrastructure work begins.
Turn bottlenecks into running systems
Pick a process where work is slowing down. We’ll help you turn it into a system that runs with minimal manual effort.
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