
FinOpsly is an AI-native control plane for managing Cloud, Data, and AI spend at enterprise scale.
Built for organizations operating across multiple clouds and data platforms, FinOpsly shifts FinOps from passive reporting to active, governed execution. The platform connects cost, usage, and business context into a unified operating model—allowing teams to anticipate spend, enforce guardrails, and take automated action with confidence.
FinOpsly brings together infrastructure (AWS, Azure, GCP), data platforms (Snowflake, Databricks, BigQuery), and AI workloads into a single decision and execution layer. With explainable AI agents operating under policy-based controls, teams can safely automate optimization, trace cost drivers to real workloads, and stop budget drift before it becomes a problem.
Key capabilities include:
Business-aware cost attribution across products, teams, and services
Predictive insight into cost drivers with clear, explainable reasoning
Policy-controlled automation to optimize spend without disrupting performance
Early detection and prevention of overruns, inefficiencies, and financial drift
FinOpsly enables engineering, finance, and platform teams to operate from the same source of truth—turning cloud and data spend into a controllable, measurable part of the business.
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BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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LangGrant
LangGrant seamlessly integrates advanced language models with your operational databases for reliable and governed data insights. Rather than offering isolated responses, it generates enduring, versioned Data Plans that elucidate the methodology and reasoning behind each outcome. Teams can assess, verify, and repurpose these plans while keeping the data intact. A single inquiry can amalgamate information across various databases, and LangGrant automatically adjusts to the unique schemas involved. It is compatible with models such as Claude, OpenAI, and Google Gemini, and accommodates platforms like Snowflake, SQL Server, Oracle, PostgreSQL, BigQuery, Redshift, Azure SQL, Databricks, and MySQL. These Data Plans progress through a structured lifecycle akin to software development (including stages like testing, approval, and staging) with built-in access controls, protection for personally identifiable information, compliance checks, and comprehensive audit trails. By leveraging previously established reasoning instead of incurring costs for each individual query, it enhances cost-effectiveness, fosters collaboration, and strengthens organizational knowledge. This innovative solution comes from the team behind Windocks, which has received recognition from Gartner for its contributions. Ultimately, LangGrant empowers organizations to harness their data more effectively and strategically.
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Numbers Station
Speeding up the process of gaining insights and removing obstacles for data analysts is crucial. With the help of intelligent automation in the data stack, you can extract insights from your data much faster—up to ten times quicker—thanks to AI innovations. Originally developed at Stanford's AI lab, this cutting-edge intelligence for today’s data stack is now accessible for your organization. You can leverage natural language to derive value from your disorganized, intricate, and isolated data within just minutes. Simply instruct your data on what you want to achieve, and it will promptly produce the necessary code for execution. This automation is highly customizable, tailored to the unique complexities of your organization rather than relying on generic templates. It empowers individuals to securely automate data-heavy workflows on the modern data stack, alleviating the burden on data engineers from a never-ending queue of requests. Experience the ability to reach insights in mere minutes instead of waiting months, with solutions that are specifically crafted and optimized for your organization’s requirements. Moreover, it integrates seamlessly with various upstream and downstream tools such as Snowflake, Databricks, Redshift, and BigQuery, all while being built on dbt, ensuring a comprehensive approach to data management. This innovative solution not only enhances efficiency but also promotes a culture of data-driven decision-making across all levels of your enterprise.
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