Company
Built by people who understand the systems around the data.
AtlasLayer is a senior-led enterprise data and AI engineering consultancy. Data platforms do not operate independently of the company. They sit between databases, applications, identity, analytics, APIs, and the workflows that actually run the business.
The Thesis
The lakehouse fails the same way an operational platform fails.
Unclear ownership. Brittle integrations. Inconsistent definitions. Security added after the fact. Treating a data platform as a Spark runtime misses all of that.
AtlasLayer designs the platform as infrastructure that has to coexist with CRM, ERP, APIs, identity, analytics, automation, and the software that runs the company. Pipelines are necessary. They are not the whole design.
Business systems
How CRM, ERP, finance, and operational applications store and move information.
Data architecture
Grain, contracts, domains, and the difference between a table and a data product.
Integrations
The seams where enterprise systems exchange state, and where pipelines quietly break.
Cloud
Identity, networking, environments, and the constraints a workspace inherits from the account around it.
Analytics
The semantic layer leadership will actually use.
Automation
Jobs, orchestration, and operational workflows that have to run without a hero.
Applications
Versioning, testing, interfaces, and the production standard notebooks often skip.
AI
Models and agents as production workloads that inherit data, permissions, and observability.
Leadership
Senior practitioners stay close to the work.
AtlasLayer engagements are senior-led. The person who understands the operating model is the person designing the architecture, and that person stays close to implementation. Specialists join when the scope requires them. There is no handoff chain between the conversation and the work.
Karson Barrett
Founder
LinkedInKarson founded AtlasLayer to do systems work with a production standard: architecture first, implementation that can be operated, and recommendations that stay connected to how the business actually runs.
His background spans digital strategy, CRM architecture, custom applications, process automation, API integrations, analytics, data quality, two-way data synchronizations, fullstack engineering, and data-mining systems. Databricks is treated as part of that enterprise, not as a standalone analytics island.
Architecture
- Data architecture
- Application architecture
- Integration architecture
Systems
- CRM
- Operational applications
- Business process automation
Engineering
- APIs & integrations
- Fullstack applications
- Data pipelines & synchronization
Intelligence
- Analytics
- Data quality & modeling
- Data-mining systems
How We Work
A small firm, close to the architecture.
Senior-led
Architecture and delivery stay with the people responsible for the technical decisions.
Architecture-first
Platform choices follow the operating model, data landscape, and workloads they must support.
Production-focused
Recommendations must have a practical path to secure, governed, observable production.
Client-owned
Accounts, code, and documentation belong to the client from day one.
Outcome-measured
Important implementations have a baseline and success criteria before they scale.
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused