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53 Listings in Predictive Analytics Available
What is Seeq? Seeq is an industrial analytics software company whose Seeq AI platform combines operational data, human expertise and advanced analytics to help industrial organizations decide faster. It also offers Seeq for Pharma for GMP-compliant decisions across the drug lifecycle. Seeq says its AI is traceable and adds context, speed and precision. Key capabilities of Seeq Seeq AI: analytics across assets, plants and enterprises Seeq for Pharma: GMP-compliant decision support Subject matter expert workflows: turn expertise into repeatable outcomes Traceable AI: adds context while keeping decisions explainable Cross-system data access: connects existing data sources Ecosystem partners: AWS, Microsoft Azure, AVEVA and Databricks How Seeq works Seeq connects to existing operational data sources, lets engineers and experts analyze time series and context, and applies AI that stays traceable to its data. Results become repeatable analyses across plants. Who uses Seeq? Process engineers in oil and gas, chemicals, pharmaceuticals, mining, power and utilities, food and beverage and semiconductors use Seeq. Customers report 25% capacity increases and 75% fewer complaints. Seeq pricing Seeq does not publish pricing. Contact the vendor for a quote. Seeq alternatives Seeq is compared with Cognite, Dataiku and H2O.ai. Cognite focuses on industrial data operations, and Dataiku and H2O.ai are general data science platforms.
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What is Uptake? Uptake is a predictive maintenance AI agent offering AI-driven predictive maintenance for fleets and heavy equipment. Founded in 2014 and based in Chicago, Illinois, USA, Uptake helps fleets, rail and government agencies automate predictive maintenance work and get results faster. Key capabilities of Uptake Failure prediction Work order intelligence Fleet analytics Parts optimization Time series analytics Root cause insights How Uptake works Uptake takes sensor data and work orders as input and produces predictions and alerts. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as OSIsoft PI, Siemens, Rockwell Automation and SAP, so the agent works inside existing workflows. Who uses Uptake? Uptake is built for fleets, rail and government agencies. It suits teams that want failure prediction and work order intelligence without adding headcount, while keeping people in control of review and final decisions. Uptake vs Augury Uptake is often compared with Augury. Uptake stands out for failure prediction and fleet analytics. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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Saaskart Market Grid™
Explore how leading Predictive Analytics solutions compare based on customer satisfaction, market presence, adoption, and buyer feedback. The Market Grid helps you identify category leaders, high-performing solutions, and emerging products within the Predictive Analytics ecosystem.
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Pecan AI
#1 in Predictive Analytics
Best Value Predictive Analytics
Akkio
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See where predictive analytics fits in a complete stack, with the other software, AI agents and services each business needs.
What is Sylvera? Sylvera is a carbon credit ratings AI agent offering ratings and data on carbon credit projects using machine learning and satellite analysis. Founded in 2020 and based in London, United Kingdom, Sylvera helps corporate buyers and investors automate carbon credit ratings work and get results faster. Key capabilities of Sylvera Carbon credit ratings Project monitoring Market intelligence Portfolio tools Satellite data analysis Audit-ready methodologies How Sylvera works Sylvera takes satellite imagery and project data as input and produces ratings and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, Salesforce, SAP and REST APIs, so the agent works inside existing workflows. Who uses Sylvera? Sylvera is built for corporate buyers and investors. It suits teams that want carbon credit ratings and project monitoring without adding headcount, while keeping people in control of review and final decisions. Sylvera vs Pachama Sylvera is often compared with Pachama. Sylvera stands out for carbon credit ratings and market intelligence. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Avathon? Avathon is an industrial AI company focused on autonomy for operations. Its platform has three pillars: Knowledge, Decision Intelligence and Governance. Key capabilities of Avathon Knowledge: Ontology and context for multi-tier physical operations Decision Intelligence: Agentic AI for faster, adaptive operations Governance: Autonomy with guardrails and accountability Manufacturing: Planning, building and maintenance Supply chain: Optimization across rail, maritime, road and warehouse logistics Asset management: Predictive and preventive maintenance How Avathon works Avathon models an operation's assets and processes as an ontology, applies agentic AI to recommend or take decisions, and enforces governance guardrails. Reported outcomes include cost reductions of 20-30% and analysis time reductions of 30-75%. Who uses Avathon? Avathon serves aerospace, energy, government and defense, manufacturing and automotive, mining and supply chain. Clients include National Grid, Aramco Trading Company, Orsted and Airbus. Avathon pricing Avathon does not disclose pricing. Interested parties book a demo. Avathon alternatives Alternatives include C3 AI and SAS Viya for enterprise AI, Tomorrow.io for weather intelligence and Subex. Avathon focuses on physical operations.
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What is FLYR? FLYR is an Offer and Order platform for airline retailing that operates on top of legacy passenger service systems without a full replacement. It covers offer management, order management and delivery, with AI-driven dynamic pricing of offers. Key capabilities of FLYR Offer Suite: Offer management and optimization for airline products. Order Suite: End-to-end order management. Deliver Suite: Order-based delivery management. Dynamic Offers: AI-driven real-time pricing of offers. Shopping Cart: Omnichannel multi-product trip planning in one cart. Marketplace: Sells non-air items such as hotels and ground transport. Legacy Translator: Connects orders to legacy systems. How FLYR works FLYR uses a layered architecture that sits between an airline legacy PSS and modern retail front ends. Orders created in the FLYR layer are translated into the formats legacy systems expect, so airlines can adopt modules such as dynamic offers or a shopping cart one at a time. The vendor describes this as modular retailing with value in quarters, not years. Who uses FLYR? Airlines moving from ticket-based to offer and order retailing. The vendor names Virgin Atlantic, Azul, Air New Zealand, Avianca and Riyadh Air as customers. FLYR pricing FLYR does not publish pricing. Contracts for airlines are enterprise agreements negotiated with the vendor. FLYR alternatives Seeq, Nixtla and Unit8 are analytics and forecasting tools, not airline retailing platforms. FLYR is purpose-built for airline offer and order management.
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What is Intelmatix? Intelmatix is a decision intelligence AI agent offering a Saudi deep tech company whose EDGE platform delivers AI decision intelligence for enterprise planning. Founded in 2021 and based in Riyadh, Saudi Arabia, Intelmatix helps enterprises in the Middle East automate decision intelligence work and get results faster. Key capabilities of Intelmatix Demand forecasting Pricing optimization Scenario planning Executive insights Predictive scenarios Decision recommendations How Intelmatix works Intelmatix takes data as input and produces predictions and recommendations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as SAP, Oracle, Excel and Snowflake, so the agent works inside existing workflows. Who uses Intelmatix? Intelmatix is built for enterprises in the Middle East. It suits teams that want demand forecasting and pricing optimization without adding headcount, while keeping people in control of review and final decisions. Intelmatix vs o9 Solutions Intelmatix is often compared with o9 Solutions. Intelmatix stands out for demand forecasting and scenario planning. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Pachama? Pachama is a carbon credit verification AI agent offering AI and satellite data that measure forest carbon and verify the quality of carbon credit projects. Founded in 2018 and based in San Francisco, California, USA, Pachama helps carbon buyers and project developers automate carbon credit verification work and get results faster. Key capabilities of Pachama Satellite forest measurement Carbon project ratings Dynamic baselines Project origination Satellite data analysis Audit-ready methodologies How Pachama works Pachama takes satellite imagery as input and produces insights and ratings. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Snowflake, Salesforce, SAP and REST APIs, so the agent works inside existing workflows. Who uses Pachama? Pachama is built for carbon buyers and project developers. It suits teams that want satellite forest measurement and carbon project ratings without adding headcount, while keeping people in control of review and final decisions. Pachama vs Sylvera Pachama is often compared with Sylvera. Pachama stands out for satellite forest measurement and dynamic baselines. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is SAS Viya? SAS Viya is a unified data and AI platform from SAS that integrates data management, analytics and governance. Teams use it to build, validate and deploy AI models with fairness and compliance controls. Key capabilities of SAS Viya Data management: Connect to data sources with governance, lineage and auditability. Explore and model: Machine learning, statistical modeling and automated AI. Visual and code interfaces: Work in drag-and-drop tools or code. Deploy insights: Embed models in workflows with business rules and decision governance. SAS Viya Copilot: AI assistant for data tasks, coding and analytics. Flexible deployment: AWS, Azure, GCP, hybrid or on premises. How SAS Viya works Teams connect data sources, prepare and explore data, build models with visual or code tools and then deploy them with decision governance. Copilot assists with coding and analytics tasks. Models include fairness and transparency controls for compliance review. Who uses SAS Viya? SAS Viya is used by large enterprises, banks, insurers and public agencies. The vendor says 90% of the Fortune 100 trust SAS and reports eight consecutive years as a Leader in the Gartner Magic Quadrant for Data Science. SAS Viya pricing SAS does not publish Viya prices and offers flexible licensing through a quote. A 14-day free trial is available. SAS Viya alternatives Alternatives include Databricks, which offers a lakehouse and ML platform, Dataiku, which provides collaborative data science and AI, and Alteryx, which focuses on analytics automation.
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What is Unit8? Unit8 is a Swiss data and AI services company headquartered in Lausanne that helps traditional enterprises turn data into value. It maintains the open-source Darts forecasting library. Key capabilities of Unit8 Consulting: Data and AI strategy Solutions: Custom implementations Data platforms: Infrastructure builds MLOps: Platform operations Generative AI services: Application development Darts: Open-source forecasting library How Unit8 works Unit8 teams work with clients on strategy, build solutions and platforms and operate them, using tools such as Darts for time-series forecasting. Who uses Unit8? Enterprises such as Merck, Swiss Re, Daimler, Firmenich, Helvetia and Julius Baer, with offices in Switzerland, Germany, Poland and the USA. Unit8 pricing Unit8 does not publish prices. Darts is open source and free. Unit8 alternatives Alternatives include Nixtla for forecasting and Akkio and Pecan AI for predictive analytics.
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What is H2O.ai? H2O.ai is an AI company whose products span open-source machine learning (H2O-3), automated machine learning (Driverless AI) and enterprise generative AI (h2oGPTe and the H2O AI Super Agent). Its platform targets regulated industries that need on-premises or air-gapped deployment. Key capabilities of H2O.ai H2O-3: Apache-licensed open-source ML framework for Python and R Driverless AI: AutoML with automatic feature engineering and explainability H2O LLM Studio: no-code fine-tuning of language models Enterprise h2oGPTe: secure generative AI for regulated industries Hydrogen Torch: template-based deep learning for images, text and time series Feature Store: shared repository of reusable features How H2O.ai works Data teams use H2O-3 or Driverless AI to build and explain predictive models, and LLM Studio to fine-tune language models without code. h2oGPTe and the H2O AI Super Agent add multi-model generative AI with cost controls and app integrations. H2O.ai states deployments carry no data sharing and no model exfiltration. Who uses H2O.ai? Data scientists, banks, telecoms and other regulated enterprises use it. H2O.ai cites Commonwealth Bank of Australia cutting fraud losses by 70% and AT&T reporting 2X ROI in free cash flow from h2oGPTe. H2O.ai pricing H2O-3 is free and open source under the Apache license. Driverless AI, h2oGPTe and the Super Agent are enterprise products, and the vendor site lists no prices, so cost is set by quote. H2O.ai alternatives Alternatives include DataRobot for enterprise AutoML, Dataiku for collaborative data science, Alteryx for analytics workflows, Altair RapidMiner for visual ML, and Tellius for conversational analytics.
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What is Camus Energy? Camus Energy provides grid orchestration software for utilities. Its FlexConnect platform analyzes hourly grid capacity and sets firm versus conditional service levels so data centers and renewable energy sources can interconnect years sooner. Key capabilities of Camus Energy Capacity mapping: hour-by-hour grid capacity from utility data Service tiers: configure firm and conditional loads Day-ahead forecasts: operating forecasts and automated alerts Real-time controls: closed-loop controls Shared dashboard: visibility for utilities and developers ODMS: Operational Data Management System How Camus Energy works FlexConnect maps available capacity hour by hour, defines which load is firm and which is conditional, forecasts operating conditions a day ahead and applies real-time controls so on-site resources cover constrained hours. It bridges planning studies and operations. Who uses Camus Energy? Utilities, data center operators and distributed generation providers. Customers named on its site include PPL, AES, Vermont Electric, DLC, Google and LGEKU. The vendor claims sites achieve over 99% grid availability. Camus Energy pricing Camus Energy does not publish prices. Projects are scoped with utilities and developers. Camus Energy alternatives Kevala applies analytics to grid planning, while Sylvera and Pachama deal with carbon credits, and H2O.ai and Altair RapidMiner are general machine learning platforms.
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What is Lyric? Lyric is an AI-native decision platform for supply chain operations. Its main product, Lyric Studio, replaces fragmented spreadsheets and black-box tools with connected decision sequences. Key capabilities of Lyric Network design: Models supply chain network choices. Demand planning: Plans demand within connected decisions. Supply planning: Plans supply with linked scenarios. Inventory management: Optimizes inventory decisions. Transportation: Optimizes transportation decisions. Capacity and labor: Optimizes capacity and workforce choices. Decision Journeys: Connected sequences of decisions. How Lyric works Lyric Studio has a Creator Zone for designing decision logic and a Consumer Zone for running and monitoring decisions. The vendor describes composable, math-first decision making, where outputs from one decision feed the next in a Decision Journey. Who uses Lyric? Enterprise supply chain teams in consumer goods, logistics and retail. The vendor lists Coca-Cola, Krispy Kreme, Estee Lauder, WESCO, Wayfair, Petco and Suntory Global Spirits, and cites $5.9M in savings at Coca-Cola Consolidated. Lyric pricing Lyric does not publish pricing on its homepage. Engagements are quoted by the vendor. Lyric alternatives Altair RapidMiner, Alteryx and SAS Viya are general analytics and AutoML platforms. Lyric is specific to supply chain decisions.
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Predictive analytics software uses machine learning and statistics to forecast outcomes and behavior from data, predicting churn, demand, risk, and revenue so teams act proactively. This guide explains what predictive analytics is, how it works, what matters, and how to choose one.
Predictive analytics software uses machine learning and statistics to forecast outcomes and behavior from data, predicting churn, demand, risk, and revenue so teams act proactively. This guide explains what predictive analytics is, how it works, what matters, and how to choose one.
Predictive analytics software analyzes historical and current data to forecast future outcomes, customer churn, demand, sales, risk, equipment failure, and more, using machine learning and statistical models.
It spans no-code/automated predictive platforms, data-science tools for building custom models, and predictive features embedded in CRM, marketing, finance, and operations software.
The category turns data into foresight for proactive decisions. Buyers weigh prediction accuracy and explainability, data requirements and integration, ease of use (for business vs. data-science users), and how predictions are operationalized into action.
Predictive analytics ingests historical data, identifies patterns, trains models to predict a target outcome, and applies them to new data to produce forecasts and scores, which feed dashboards, alerts, or automated actions.
Platforms combine data integration, automated or custom model building, evaluation, and deployment, with explainability and integration into business systems.
Teams connect data and define the outcome to predict, build or auto-generate models, validate accuracy, and operationalize predictions into workflows, monitoring and retraining as data changes.
AutoML and no-code tools build predictive models without deep data-science expertise.
Predict outcomes (churn, demand, risk, LTV) and score records for prioritization.
Explain which factors drive predictions for trust and actionability.
Connect to your data sources and warehouse for training and scoring.
Deliver predictions via dashboards, alerts, APIs, or automated actions.
Track accuracy and retrain as data and conditions change.
Anticipate churn, demand, and risk and act before outcomes happen.
Scores prioritize where to focus effort for the most impact.
Data-driven forecasts improve planning and reduce surprises.
Predict failures, fraud, and risk to prevent costly events.
No-code tools bring prediction to teams without data scientists.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| No-code/AutoML platforms | Predictions without data science | SMB to enterprise | Accessible, fast | Less control than custom |
| Data-science platforms | Custom predictive models | Teams with data scientists | Full control and accuracy | Expertise required |
| Embedded predictive features | Predictions inside business apps | Any | In-context, easy | Limited to that app's data |
| Domain predictive solutions | Churn, demand, risk, maintenance | Any | Purpose-built | Narrower scope |
Technology: Technology teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Healthcare: Healthcare teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Financial Services: Financial Services teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Retail & E-commerce: Retail & E-commerce teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Education: Education teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Professional Services: Professional Services teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Manufacturing: Manufacturing teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Media: Media teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively, turning historical data into foresight for better decisions.
Test prediction accuracy on your data with proper validation, not just training performance.
Confirm the tool explains prediction drivers so teams trust and act on results.
Assess how much clean historical data is needed and whether yours suffices.
Match the tool to your users, business teams need no-code; data scientists want control.
Verify predictions flow into dashboards, alerts, or actions in your systems.
Understand seat, usage, or platform pricing and how it scales.
AutoML and generative interfaces are making predictive analytics accessible to more business users.
Predictions are increasingly operationalized into automated, agentic actions.
Explainability and uncertainty quantification are improving trust and decisions.
Buyers should prioritize accuracy and validation, explainability, data fit, and operationalization into action.
Predictive analytics uses machine learning and statistical models to forecast future outcomes from historical and current data, predicting customer churn, demand, sales, risk, equipment failure, and more. The software spans no-code/AutoML platforms, data-science tools for custom models, and predictive features embedded in CRM, marketing, finance, and operations software, turning data into foresight for proactive decisions.
Accuracy depends heavily on data quality and quantity, the predictability of the outcome, and proper model validation. Good models on solid data can be highly useful, but predictions carry uncertainty and can degrade as conditions change. Validate accuracy on held-out data, monitor over time, and treat forecasts as informed probabilities, not certainties.
Not necessarily. No-code and AutoML platforms let business users build and use predictive models without deep expertise, which is enough for many use cases. Data-science platforms offer more control and accuracy for complex problems but require expertise. Choose based on your team's skills and the complexity of what you're predicting.
Common applications include customer churn, lifetime value, and conversion; demand and sales forecasting; credit and fraud risk; predictive maintenance (equipment failure); and inventory and staffing needs. Essentially, any outcome with enough relevant historical data to learn from can be a candidate, feasibility depends on data quality and signal.
Teams need to understand why a model predicts an outcome to trust it and act appropriately, for example, which factors drive churn so you can intervene. Explainability also helps detect bias and errors. Black-box predictions are harder to act on and riskier, so favor tools that explain prediction drivers.
Predictions are operationalized by delivering scores and forecasts into dashboards, alerts, CRM/marketing systems, or automated workflows so teams or systems act on them. Operationalization is often harder than model-building, so confirm how a tool integrates predictions into your systems and processes, not just how it generates them.
Reputable vendors offer encryption, access controls, and compliance; confirm whether your data is used to train shared models and how it's processed. Since predictive analytics uses potentially sensitive historical and customer data, review data handling, residency, and governance before adopting.
Prioritize prediction accuracy with proper validation on your data, explainability, fit with your data requirements and quality, ease of use for your users (no-code vs. data science), operationalization into your workflows, integration, and pricing. Pilot on a real prediction problem and validate accuracy and actionability before scaling.
