Lumi AI

What Lumi AI does

Lumi AI's agentic analytics platform connects to all your data sources, automatically builds your context layer, and proactively surfaces opportunities hidden in your data. It acts as a chat-based agentic data analyst that uncovers hidden optimization opportunities across ERPs and databases, powering smarter Supply, Distribution, and Customer operations. The platform aims to accelerate time to insights and reduce the cost of data requests by enabling self-service analytics and proactive insight generation for enterprises.

Key capabilities

• Agentic analytics platform
• Automated Context Layer config
• Semantic layer building
• Chat-based agentic data analyst
• Conversational analytics
• Auto-generated charts & tables
• Query builder
• Magic Wand setup agent
• Advanced context management
• Publish & auto-refresh dashboards
• Customize visuals and drill down
• Proactive insights and alerting
• Roles-based access controls
• Lumi AI Data Gateway (on-prem)
• Custom SAML SSO
• Single tenant available
• Forward deployed engineer/customer success
• Services driven hands-on pilot
• Curated prompts
• Recommended follow-up questions
• Access logic and assumptions
• View SQL and Python used
• Summarized insights
• Download to CSV or Excel
• Tune with feedback (thumbs up/down)
• Share insights with team members
• Analyze uploaded files
• Integrate with Slack and Microsoft Teams
• Explore data
• Monitor key business metrics
• Detect anomalies
• Analyze trends over time
• Investigate root cause
• Conduct data quality checks
• Generate forecasts (in preview)
• Save chats to boards
• Drill deeper into any insight
• Re-arrange reports or charts on board
• Scheduled auto-refreshes (coming soon)
• Publish boards to your team
• View report summaries
• Connect data sources
• Build and manage semantic layers
• Create custom fields
• Add context to describe tables and fields
• Define join conditions between tables
• View data model & relationships
• Manage business context and terminology
• Define and catalog logic for key metrics
• Email alerting and notifications
• Schedule delivery of alerts
• Create threshold alerts on KPIs
• Tune responses with user feedback
• Protect memories with automatic updates
• Request verification for AI-generated insights
• Receive approvals, rejections, or feedback
• Enterprise gateway connection
• Query limiters and controls
• Users & permissions management
• Full admin usage dashboard
• Dedicated Value Engineer
• In-app chat support
• Email support
• Phone support
• Implementation services
• Access to additional professional data services
• Query limiters and controls
• Users & permissions management
• SOC2 Type ii certified (coming soon)
• Raw data stays within your network
• Shared data is not used for training
• Flexible connection modes (Direct Connection, Secure Data Gateway)
• Separation of Scope through Knowledge Bases
• Segmented Knowledge Base Architecture
• Role-Based Access Controls
• Deterministic Filtering
• Concurrency and Row Limits
• Quota and Usage Controls
• Uncover Root Causes
• Solve Complex Queries
• Explore Opportunities
• Speak Your Language (business terms)
• Easy implementation in just a few clicks
• Fully managed pre-built data models
• Access insights that drive actions
• Curated prompts & reports
• Instantly unlock actionable insights from your sales data
• Generate exception reports
• Create graphs/charts
• Generate code (SQL/Python)
• Uncover data quality issues
• Drill down into the data
• Identify trends and patterns
• Upload your file and chat
• Understand logic/assumptions
• Get alerts and notifications
• Send agents on 'data missions'
• Define KPIs & business-specific details
• View and define relationships across tables
• Select specific tables and fields you want to include in your data model
• Describe and rename vague table and columns for clarity

Use cases

• Uncovering hidden optimization opportunities across ERPs and databases
• Improving pricing strategy to increase revenues
• Identifying high-volume SKUs for price reduction to increase units sold and revenue
• Identifying SKUs for price increases and quantifying revenue impact
• Uncovering unfulfilled demand in inventory, sales, and procurement datasets
• Demand Planning
• Shopper Cohort Analysis
• Price Sensitivity analysis
• Increasing revenues
• Decreasing costs
• Freeing up working capital
• Self-service analytics
• Proactive insight creation
• Conversational analytics for the whole business
• Autonomously analyzing operational data for hidden opportunities
• Pinning, auto-refreshing, and sharing insights
• Streamlining manual analytics
• Identifying unfulfilled demand
• Uncovering revenue opportunities by targeting customers most likely to shift to higher AOV transactions
• Identifying recoverable revenue
• Accelerating report development
• Procurement at scale (analyzing materials to drive savings)
• Customer Behaviour Analysis
• Sales Performance Optimization
• Category & Assortment Planning
• Segmentation & Profiling (customers by demographics, behavior, purchasing patterns)
• Identifying high-value customers
• Detecting trends and anomalies in customer behavior
• Loyalty & Retention (measuring churn, identifying at-risk groups, tracking loyalty program impact, uncovering drivers of satisfaction/dissatisfaction)
• Cross-Sell & Up-Sell Opportunities (analyzing purchase sequences, pinpointing customers for upgrades, understanding category affinities)
• Revenue Analysis (breaking down revenue, measuring growth, forecasting sales)
• Promotion Effectiveness (analyzing impact, identifying underperforming promotions, tracking ROI)
• Channel Performance (comparing performance across channels, highlighting underperforming stores/regions, aligning sales efforts)
• Category Analysis (measuring sales/profit contributions, identifying cannibalization/synergies, optimizing growth)
• Assortment Optimization (analyzing SKU performance, detecting slow-moving items, aligning assortments with demand)
• Pricing Strategy (evaluating price elasticity, measuring impact of price changes)
• Finding outliers & anomalies
• Generating exception reports
• Uncovering data quality issues
• Drilling down into data
• Uncovering root causes
• Solving complex queries
• Identifying trends and patterns
• Automated insights generation from large datasets
• Pinpointing operational inefficiencies and untapped potential
• Redirecting technical teams from ad-hoc requests to strategic initiatives
• SKU Rationalization Workflow

AI approach

Lumi AI's agentic analytics platform connects to data sources, automatically builds a context layer, and proactively surfaces opportunities. It acts as a chat-based agentic data analyst that uncovers hidden optimization opportunities across ERPs and databases. Lumi AI converts plain language prompts into SQL/Python code, executes it within the client's network, and renders results. It uses a knowledge base to retrieve relevant context and interpret business-specific language.

Tech named: Generative AI, Large Language Models, SQL, Python

Industries served

• Software Development
• Supply Chain Analytics
• Retail Analytics
• Business Intelligence
• Agriculture
• Brand & Retail
• Grocery Retailer
• Luxury Retail Group
• Global Food Manufacturer
• Food & Beverage Service Provider
• Textile Manufacturer

What it says sets it apart

• Agentic analytics platform
• Connects to all data sources
• Automatically builds semantic layer/context layer
• Proactively surfaces opportunities
• Chat-based agentic data analyst
• Uncovers hidden optimization opportunities
• Outcome-driven analytics (configure to hunt for opportunities around metrics that matter)
• Autonomous analytics workflows
• Self-service analytics as a starting point, proactive analytics as the destination
• Builds rich context layer in minutes (Magic Wand auto-generates semantic models)
• Enables conversational analytics for the whole business with guardrails
• Reveals opportunities dashboards miss
• Pin, auto-refresh, and share insights
• Ability to de-average and re-aggregate down to store-item impacts
• Quantifiably best in class (outperforms others in direct head-to-head comparisons)
• Lumi speaks your business lingo (Knowledge Base integrates unique business terms and metrics)
• Enterprise-grade security (processes raw data within your network, user permissions, query controls, cost management)
• Raw data stays within your network (no data replication, no external storage)
• Shared data is not used for training (only abstracted metadata, user prompts, responses; never trains AI models on company info)
• Flexible connection modes (Direct Connection, Secure Data Gateway)
• Separation of Scope through Knowledge Bases (segmented architecture, granular permissions, deterministic filtering)
• Query and Usage Control Limits (admins can set cost, duration, row limits, concurrency limits)
• Lumi thinks and acts like your best data analysts (interprets business-specific language, uncovers root causes, solves complex queries, explores opportunities)
• Eliminates manual analytical processes
• Boosts productivity
• Makes better, faster data-driven decisions
• Uncovers hidden value
• Pinpoints operational inefficiencies and untapped potential within seconds
• Frees up technical teams from ad-hoc requests
• Instantly converts plain language prompts into code (SQL/Python)
• Removes delays between questions and insight
• Users interact with data in natural language, generate queries automatically, produce accurate results across complex datasets
• Removes bottlenecks and supports fast, independent decision making
• Knowledge Management features automatically understand user intent, business rules, organizational context
• Knowledge base stores field definitions, table relationships & context, business details, KPIs, Aliases
• Generates stunning visual displays of results with full customizations
• Secure Connectors (governed credentials, encrypted connections, data never leaves customer environment)
• Query Builder provides guided environment for writing and refining SQL, Python, analytical logic (Lumi pulls schema details, definitions, business rules from knowledge base)
• Reduces rework, shortens development cycles, keeps analytical logic consistent
• Accelerates time to insights (7 days to 30 seconds)
• Reduces cost of data requests (517x cheaper)
• Easily configure data model (structure/organize data, define KPIs, view/define relationships, select tables/fields, describe/rename vague elements)

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.