AURA — Advanced Unified Response Analytics — is MathCo's Databricks-native accelerator for moving from isolated Genie pilots to a governed, multi-agent enterprise analytics operating model.
AURA delivers on-demand KPIs through a conversational web app — routing user queries across domain-specific Genie spaces, custom visualization and statistics agents, and governed metric views. Watch a working session below.
Business users need governed access across domain-specific data products, metric views, and functional workflows — but existing deployments create siloed AI experiences that don't scale across sales, CRM, finance, and operations.
Most conversational analytics solutions stop at an NLQ interface — without the enterprise-grade routing, orchestration, state management, observability, and policy guardrails needed for production deployment.
As usage grows, organizations hit QPM bottlenecks, cross-domain query complexity, inconsistent governance, and limited reuse of prompts, tools, and evaluation pipelines.
Point solutions remain confined to chatbot interfaces — lacking the orchestration, governance, memory, evaluation, and lifecycle controls that broad business adoption demands.
→ Pilots prove possibility. Enterprises need a scalable operating model.
GenAI pilots are multiplying across every function.
Production-grade use cases remain rare.
The leap from POC to scale is operationally brutal.
Governed, enterprise-ready AI workflows are still scarce.
→ The gap isn't AI capability. It's operationalization.
AURA is MathCo's enterprise adoption framework for Databricks-native AI/BI transformation. It accelerates Databricks AI/BI enterprise adoption by 40–60% by reusing orchestration, memory, and governance patterns across domains.
A reusable supervisor for routing, planning, task execution, and response synthesis across Genie and custom agents.
Unified enterprise memory using Lakebase, Delta, and vector retrieval — for session continuity and reusable business context.
Built-in RBAC, audit trails, policy guardrails, prompt controls, and domain-level permissions via Unity Catalog.
Continuous optimization through MLflow evaluation, DB Judges, feedback capture, and Lakehouse monitoring.
Template-driven domain rollout using Genie spaces, metric views, YAML semantics, and cross-domain federation patterns.
Web, chat, or admin client hits the AI gateway — SSO authentication, rate limiting, secret management, RBAC, and caching all sit here.
Intent detection, agent routing, task planning, state & checkpoint, evaluation guardrails, and result aggregation across Genie and non-Genie agents.
Domain-specific Genie spaces handle NL→SQL on certified metric views; custom agents deliver visualization, statistics, and report-share alongside MCP tools.
Session and long-term memory in Lakebase + vector store. Unity Catalog governs every call. MLflow evaluation and Lakehouse monitoring close the feedback loop.
→ Question to insight to action — governed end-to-end, not glued together.
Stands up fully configured Genie Spaces for merchandising, trade promotions, supply chain, and finance in days — pre-loaded with domain instructions, certified datasets, and question libraries so analysts get accurate answers without waiting on IT.
A supply chain analyst asking about promo impact on inventory gets routed to the right Genie Space automatically, without knowing how domains are partitioned or which space to open.
Genie retains context across sessions and domains, enabled by Lakebase.
Shows exactly how accurate each Genie Space is by validating against the golden question set.
Every Genie Space ships with Unity Catalog policies — compliance is built into deployment, not discovered as a problem after go-live.
Use-case scoring, data-readiness audit, and a value map agreed with the business.
Stand up the Genie layer on the Lakehouse, certify the first set of metrics, ship the first conversational surface.
Wire in pipeline-building and decision agents. Add evaluation, observability, guardrails.
Expand to additional domains, geos, and lines of business — with reusable patterns and a shared semantic layer.
Move from experimentation to scalable AI-driven decisioning, with a partner who has done it before.
Download the AURA one-pager (PDF) →
From data to decisions. From AI to impact.
