Your data holds the answers. The problem is how long it takes to reach them. Decision Foundry builds AI agents that monitor your data, investigate anomalies, run analysis, and deliver decision-ready insights to every team, grounded in governed data and kept under human control.
Most enterprises aren't short on data or dashboards. They're short on time to investigate what the numbers mean.
Enable business users to ask questions in natural language and receive data-backed answers with supporting queries and visualizations. Using natural language-to-SQL and governed semantic layers, these copilots make analytics accessible through tools like Slack, Teams, or BI platforms. See this in action with our GA Data Bot.
Monitor key metrics continuously to identify unusual changes, distinguish meaningful shifts from normal fluctuations, and investigate potential causes across connected data sources. Agents alert the right stakeholders with relevant context, helping teams respond before issues escalate.
Turn complex data into clear, role-specific narratives that highlight performance trends, key findings, and recommended actions. From executive summaries to channel-level action lists and financial variance reports, agents help teams understand what matters. Our MemoSight 2.0 applies this approach to marketing insights.
Make forecasting, scenario modeling, and marketing mix optimization more accessible through on-demand analytical workflows. Agents help teams evaluate potential outcomes, compare scenarios, and identify opportunities using statistical models. MIX-IQ demonstrates how data science can support evidence-based marketing budget recommendations.
Improve data reliability by automating validation, identifying broken data feeds, and standardizing inconsistent fields before they affect downstream analytics. These agents help maintain cleaner, more consistent data across reporting workflows. CampTag addresses campaign taxonomy standardization.
Connect analytical insights to business operations by translating approved recommendations into actions across systems such as Salesforce, advertising platforms, and ERP tools. Configurable approval steps and human oversight help ensure that consequential actions remain controlled and aligned with business rules.
Have a use case in mind? Let's explore how agentic AI can fit into your data ecosystem, streamline analytics workflows, and deliver measurable business value.
Identify high-value analytics workflows, define business objectives, and establish measurable success criteria for the AI agent.
Evaluate data quality, metric definitions, existing platforms, integration requirements, and access permissions to establish a reliable foundation.
Build the agent workflow, connect relevant data sources and analytics tools, and configure data retrieval, analysis, and response generation.
Validate analytical accuracy, test outputs against trusted metrics, verify access controls, and refine agent behavior across real-world scenarios.
Deploy the validated solution, monitor performance and usage, address issues, and optimize workflows before expanding to additional use cases.
See how Decision Foundry applies AI to automate analysis, uncover performance issues, and help teams make faster, data-driven decisions.
See how Decision Foundry applies AI to solve business challenges across different use cases. Explore our AI portfolio →
Expand analytics capacity without adding headcount. Give business teams faster access to reliable insights with the right governance in place.
Understand what drives cross-channel performance, spot emerging trends, and identify opportunities to optimize marketing spend.
Automate variance analysis and financial commentary to uncover what's driving changes in revenue, costs, and forecasts.
Reduce repetitive ad hoc requests and focus on higher-value work, from data modeling and architecture to complex analysis.
With 20+ years of experience and 1,000+ projects delivered, we build on strong foundations in data pipelines, dashboards, and data models to deliver reliable analytics agents.
With 50+ AI projects delivered and solutions like MemoSight, MIX-IQ, CampTag, and GA Data Bot, we bring practical experience beyond experimentation.
We are a Certified Services Partner in the Claude Partner Network, a Salesforce and Snowflake Select Partner, and a Databricks partner. Our teams hold 25+ vendor certifications.
Our approach incorporates role-based access, PII protection, audit trails, and human oversight. We are SOC 2 compliant and GDPR capable, with CISSP- and CRISC-certified specialists supporting security and risk requirements.
Whether you use Agentforce, Claude, OpenAI, Azure AI Foundry, or open-source technologies, we select the tools that best fit your data architecture, business objectives, and governance requirements.
Talk to Decision Foundry about building AI agents that automate analysis, uncover patterns, and help your teams make better decisions.
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We assess your data architecture, analytics tools, and access requirements before designing the integration. Agents can connect to supported data warehouses, BI platforms, and business systems through APIs, connectors, or other approved interfaces.
A dashboard reports what happened, while a chatbot answers questions when prompted. An analytics agent goes further by monitoring metrics, investigating changes across data sources, identifying potential causes, and recommending or initiating next steps. Read our article on agentic AI to understand how it differs from traditional AI tools.
Yes. Agents can be configured with defined permissions, approved tools, query restrictions, and execution boundaries. Sensitive operations can require human approval, while logging helps maintain visibility into agent activity.
The requirements depend on your use case. We assess data quality, pipeline reliability, metric definitions, and source accessibility. Where needed, data integration, cleansing, and semantic modeling help establish a reliable foundation for agent-driven analysis.
Ongoing management can include tracking response accuracy, query failures, latency, usage, and operating costs. Monitoring, evaluation, and updates help address changing data schemas, business rules, and analytical requirements.
Yes. A focused pilot can validate technical feasibility and business value before the solution expands to additional data sources, teams, or workflows. A modular architecture and clearly defined success metrics help guide that expansion.
