The best-in-class tools across cloud infrastructure, data platforms, AI models, and analytics — selected and integrated by our team to build the platform your business runs on.
Our platform is built on proven, best-in-class technology at every layer — from cloud infrastructure for your data lake to the AI models powering your agents. We're tool-agnostic where it matters and opinionated where experience has shown us what works. The result is a platform built on the right tools for each job, integrated and governed as a single coherent system.
The foundation of every data platform we build is a cloud-native data lake hosted on one of the three major cloud providers. We deploy on whichever platform your business already uses — or recommend the right fit based on your requirements. All three offer the storage, security, and compliance capabilities required for a governed enterprise data lake.
The world's most widely adopted cloud platform. We use AWS as the foundation for data lakes built on S3, with native integration into the broader AWS data ecosystem. The preferred choice for organizations already running workloads on AWS or seeking maximum ecosystem breadth.
The enterprise cloud of choice for organizations running Microsoft workloads. Azure Data Lake Storage Gen2 provides a highly scalable, secure foundation with deep integration into the Microsoft identity and security ecosystem — making it the natural fit for businesses already in the Azure or Microsoft 365 environment.
Google's cloud platform brings unmatched AI and ML capability alongside a best-in-class data lake in Google Cloud Storage. The preferred choice for organizations that want tight integration between their data platform and Google's AI ecosystem — including Vertex AI and Gemini.
On top of the data lake sits the compute and transformation layer — where raw data gets normalized, governed, and modeled into a trusted, query-ready central layer. We build this using best-in-class platforms that handle compute at scale, define the business logic, and serve data to every consumer downstream.
Snowflake's cloud data platform delivers near-unlimited scale and concurrency for analytics workloads. We use Snowflake as the compute and warehousing layer on top of your data lake — handling the normalization, transformation, and governed access that turns raw data into trusted business intelligence.
Databricks unified analytics platform combines data engineering, data science, and ML in a single collaborative environment built on Apache Spark and Delta Lake. We use Databricks for complex transformation workloads, large-scale ML pipelines, and environments where Python and data science capability are central to the platform.
dbt is the standard for building and governing the transformation layer on top of your data warehouse or lakehouse. We use dbt to define the business logic that turns normalized source data into reliable, tested, documented data models — with full lineage and version control built in.
The AI agents and analytics capabilities we build on top of your data platform are powered by the world's leading AI models. We are model-agnostic — we use whichever model performs best for a given use case, and we connect them to your governed data so their outputs are grounded in your actual business data, not general knowledge.
Claude is our primary model for building data agents that reason carefully over complex business data. Claude's strengths in long-context reasoning, data analysis, and instruction-following make it exceptionally well-suited for finance agents, operations agents, and any use case requiring nuanced interpretation of structured or unstructured business data.
OpenAI's GPT-4 family powers a wide range of agent and analytics use cases in our platform. We integrate GPT-4 for natural language querying, automated report generation, and customer-facing AI experiences where OpenAI's broad capability and ecosystem integrations align with your existing tooling.
Perplexity brings real-time web search and research capability into your data agents. We integrate Perplexity for use cases where your internal data needs to be contextualized against current market information, competitive intelligence, or external data sources — grounding your agents in both your proprietary data and the world outside it.
Google's Gemini models power AI workloads in GCP-native environments and bring strong multimodal capability to data platforms built on Google Cloud. We integrate Gemini for organizations running on GCP, and for use cases requiring deep integration with Google Workspace, Google Analytics, or Vertex AI.
Every platform we build connects to the BI and analytics tools your teams already use — or we stand up the right tool if you're starting fresh. All visualization tools connect to the same governed semantic layer, so every dashboard and report pulls from the same source of truth regardless of which tool your team prefers.
Tableau is the industry standard for visual analytics. We connect Tableau directly to your semantic layer for governed, self-service analytics across your organization — from executive dashboards to detailed operational reporting. Tableau's drag-and-drop interface makes complex data exploration accessible to non-technical business users.
Power BI is the natural choice for organizations running on Microsoft Azure and Microsoft 365. We build governed Power BI deployments connected to your data platform — with DirectQuery live connections, row-level security aligned to your Azure AD roles, and automated report distribution via Power BI service.
Hex is a collaborative analytics workspace that combines SQL, Python, and no-code analysis in a single interface. We use Hex for data science teams and advanced analysts who need to go deeper than a standard BI dashboard — blending SQL queries, Python models, and interactive visualizations into shareable data apps your stakeholders can explore.
Looker (Google Cloud) brings a code-first, governed approach to BI through its LookML modeling layer. We build Looker deployments for organizations that want a developer-friendly semantic layer with strong governance and embedded analytics capability — particularly well-suited to GCP-native environments and API-first reporting use cases.
Our team holds certifications across every layer of the platform — from cloud infrastructure to data engineering, AI, and analytics.