o9 Solutions is one of the major players in providing AI-based digital platforms that help businesses make informed decisions about their operations through integrated Business Planning, as well as being a leader in Supply Chain Management and planning since it was first established in 2009. As an organization based in Dallas, TX, o9 helps businesses around the world use their Digital Brain platform which includes an Enterprise Knowledge Graph, so that they can be making more informed, timely, and optimal decisions using the knowledge graphβs real time insights. Through the entrepreneurial spirit of co-founders Chakri Gottemukkala, Sanjiv Sidhu, and another fellow entrepreneur, o9 aims to continually innovate and bring value to businesses through improving their operational efficiency, financial performance, and environmental sustainability.
Founded by entrepreneurs Chakri Gottemukkala, Sanjiv Sidhu, and another fellow entrepreneur to address the problem of separate enterprise-wide planning and supply chain decision-making processes that are not connected.
o9 Solutions received $300M from Generation Investment Management and General Atlantic's Beyond Net Zero to fund the growth and continued expansion of o9βs Digital Brain platform.
The company has reached $150M in Annual Recurring Revenue (ARR) for their Digital Brain platform and have had an incredible amount of success with getting many large enterprises to adopt the Digital Brain platform.
Cloud-based multi-tenant enterprise platform
AI-powered Digital Brain with Enterprise Knowledge Graph enabling real-time integrated planning, demand forecasting, and decision optimization across enterprise functions
Inferred from product descriptions and enterprise positioning; specific technologies not publicly detailed
| Service | Cost | Details | Source |
|---|---|---|---|
| Enterprise Platform | Custom quote | Subscription-based SaaS pricing tailored to modules, users, and deployment size. Contact sales for proposal. | G2 and official website |
| Price Planning & Optimization | Custom quote | AI-powered pricing module with diagnostics, elasticity modeling, and competitive benchmarking. | o9solutions.com pricing solutions page |
| Digital Brain (EKG Platform) | Custom enterprise license | Patented Enterprise Knowledge Graph for integrated planning across supply chain and revenue management. | Official product documentation |
| Feature | o9 Solutions | Kyriba | SAP Treasury | Nomentia | GTreasury |
|---|---|---|---|---|---|
| Demand Forecasting & Planning | Yes (AI/ML) | Partial | Partial | No | No |
| Price Optimization | Yes (Elasticity modeling) | No | No | No | No |
| Liquidity Management | Partial | Yes | Yes | Yes | Yes |
| Enterprise Knowledge Graph | Yes (Patented) | No | No | No | No |
| FX Risk Management | No | Yes | Yes | Yes | Partial |
| Starting Price | Custom quote | Custom quote | Custom quote | Custom quote | Custom quote |
| Free Tier | No | No | No | No | No |
| API Availability | Yes | Yes | Yes | Yes | Yes |
| Integration Count | High (ERP/SCM) | High (Banks/ERP) | SAP-centric | High | High |
| Support Options | Enterprise | Enterprise | Enterprise | Enterprise | Enterprise |
| Security Certifications | Enterprise-grade | SOC2 | SAP standards | GDPR | Enterprise-grade |
o9 is focused on Integrated Supply Chain Planning and Revenue Growth Management utilizing AI-Powered Demand Forecasting while Kyriba is focused on Core Treasury Functions such as Liquidity and FX Risk. o9 supports planning intensive businesses while Kyriba supports Treasury First Organizations.
Select o9 for End-to-End Supply Chain + Demand Planning; Kyriba for Pure Treasury and Cash Management.
SAP offers deeper ERP Integration for Treasury, but lacks o9's Advanced AI-based Demand Planning and Price Optimization. o9 provides broader planning capabilities across the Supply Chain while SAP excels at Financial Compliance within S/4HANA Ecosystems.
o9 for Planning Innovation Across SAP/non-SAP; SAP for Embedded Treasury Compliance.
GTreasury provides Comprehensive Treasury Management with Strong Bank Connectivity, however o9 differentiates itself through its Enterprise Knowledge Graph which connects planning, pricing and execution β areas of capability where GTreasury has limited functionality.
For a more traditional treasury function, GTreasury is better suited than o9 for an AI-integrated planning platform.
Nomentia provides Solid Mid-Market Treasury with Good Automation, while o9 addresses enterprise-level supply chain planning complexity that Nomentia does not provide, although both offer Custom Pricing Models.
Nomentia provides simpler treasury functions than o9 does for large, complex, multi-domain enterprise planning.
Internal/External signals for a complex/volatile time series that have been created from a large SKU location hierarchy. This is best used by Consumer Goods, Electronics and Automotive companies due to their high volatility and the ability of promotional activity to influence demand. Liquidity sensitive demand streams.
Price Elasticity of Demand, Promotion Signals, and other external events are found in these feature rich datasets. This is best used for Price Change Forecasting, Demand Sensing with Market Intelligence, and Treasury Cash Flow Predictions.
Large Scale Hierarchical Time Series with Complex Patterns and Intermittencies. This is best used for New Product Introduction, Seasonal Demand with Multiple Cycles, and Financial Forecasting Stability.
Historical Shipment/Sales Data that has been corrected for Cut Quantities and Lost Sales. This is best used as Baseline Generation prior to Collaborative Layers and for Stable Demand Categories in Banking Liquidity Planning.
ERP Systems with Cut Quantity Correction; POS Transaction Data. Critical - o9 Auto-Correction for Lost Sales through Sophisticated Interpolation. Real-Time Preferred; Daily Minimum. Granular Timestamps; Automatic Correction for Order Fill Rates <100%.
Pricing Systems, Revenue Management Platforms. Critical - ML Models Learn Price Elasticity for Accurate Volume Prediction Post-Changes. Real-Time for Planned Changes; Daily Historical. Effective Date Tracking; Discount Structures; Competitive Pricing Context
Influenza Surveillance, Search Trends, Weather APIs, Glance View Data. High - Category-Specific Signals Dramatically Improve Accuracy (Cold/Flu Products, Seasonal Goods). Real-Time/Hourly for High-Impact Signals. Validated Correlation to Demand; Time-Series Alignment
Marketing Systems, Trade Promotion Management. High - Structured Demand Assumptions Prevent Override Proliferation. Weekly Planning Cadence. Hierarchy-Aware Volume Assumptions; Approval Workflow Integration
The medium-high level of frequency that will be used for competitive pricing feeds, social listening, and economic indicators is a daily update. There are real-time APIs available that can provide a history of where things have been in order to compare current prices to previous ones.
The use case for the executive team (CFO/VP Supply Chain) to review how accurately we are using forecasting to manage our working capital, as well as ROI from demand planning is via a set of KPI tiles that provide a drill down into the FVA (Forecast Value Added) of each category, as well as an overview of forecasted exceptions and their respective planning layers. The display is a real time 13-week rolling view.
The use case for the demand planners is a daily collaboration and exception resolution process. This is facilitated through a real-time collaborative platform that allows for drag-drop assumption management, as well as layer-by-layer FVA visualization and what-if override testing. The platform also provides exception alerting and demand assumption status.
The use case for the data science/analytics teams is continuous model improvement. This is supported through a multi-model ensemble scoring system, as well as signal importance ranking, stability/reasonability metrics and FVA decomposition. The platform also supports continuous learning updates, allowing for model comparison, feature importance heat maps and retraining triggers.
The use case for the revenue management teams is responding to short-term demand volatility based on real-time external signal impact, short-term accuracy lift, price elasticity curves and promotion effectiveness. The platform also supports real-time signal ingestion, live signal correlation analysis and scenario impact simulation.
| Workflow Stage | Participants | System Capabilities | Inputs | Outputs | Supported Scenarios |
|---|---|---|---|---|---|
| AI/ML Statistical Baseline | Automated + Demand Planning Analyst | Multi-model ensembles, external signal auto-integration, FVA baseline generation, history auto-correction | Corrected historical shipments, price data, external signals (weather, search trends, flu surveillance) | Explainable baseline forecast with confidence bounds; automatic algorithm selection rationale | Signal-driven baseline adjustments; price change simulations |
| Demand Assumption Collection | Sales, Marketing, Demand Planners | Structured assumption framework, hierarchy-aware volume inputs, approval workflows, mix change resilience | Promotional calendars, market intelligence, customer insights, competitive actions | Approved demand volume assumptions by hierarchy level with ownership tracking | Promotional lift scenarios; market event impacts; customer win/loss adjustments |
| Consensus Forecasting Layers | Cross-functional planning teams | Layered adjustment framework, real-time collaboration, FVA calculation per layer, conflict resolution | Statistical baseline + approved assumptions from all stakeholders | Consensus forecast with complete audit trail by layer/contributor | Override testing with instant FVA feedback; collaborative what-if analysis |
| Liquidity & Financial Review | Treasury, Finance, Supply Chain Executives | Working capital impact modeling, cash flow forecasting, scenario comparison by layer | Consensus demand plan + inventory policies + financial constraints | Financially feasible demand plan with liquidity optimization recommendations | Working capital stress tests; seasonal funding scenarios; growth vs. conservation |
| Final Approval & Execution | Executive Planning Committee | Approval workflows, final FVA validation, execution handoff automation | Final consensus forecast with complete governance documentation | Approved demand plan published to execution systems (ERP, WMS) | N/A - Single committed execution plan |
| Continuous Learning & Replanning | All stakeholders + AI/ML automation | Real-time actuals vs. forecast tracking, FVA recalibration, model retraining triggers | Rolling actual demand data + new signals + performance diagnostics | Updated forecast baseline; replanning recommendations; performance alerts | Exception-based replans; signal-driven mid-cycle adjustments |
