PI.EXCHANGE
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About
PI.EXCHANGE is an advanced artificial intelligence platform designed to streamline and enhance business forecasting through machine learning. At its core, the platform addresses the limitations of traditional spreadsheet-based forecasting and rigid ERP systems by offering customized models that adapt to specific business environments. The suite consists of three specialized studios: Demand Forecasting Studio for inventory and sales optimization, Customer Insights Studio for behavioral predictions like churn and lifetime value, and FlexiBuild Studio, a no-code environment for general machine learning tasks such as regression and clustering. The platform operates by creating an automatic forecast pipeline that retrieves data, trains models, and generates predictions on a pre-defined schedule. A standout feature is the "Golden Record" of demand drivers, which allows users to enrich their internal data with curated external variables such as weather patterns, public holidays, and macro-economic indicators like inflation or exchange rates. Users can also perform collaborative scenario planning, simulating various 'what-if' situations to understand the potential impact of promotions or external shifts on their revenue targets and stock levels. This tool is primarily tailored for professionals in manufacturing, wholesale, retail, and finance who manage complex supply chains and customer data. It serves supply chain managers looking to reduce excess stock, sales directors aiming to prevent product stockouts, and data scientists seeking to automate repetitive data wrangling tasks. Because it offers a no-code interface alongside API and SDK access, it bridges the gap between business users and technical teams, enabling organization-wide collaboration on predictive analytics without requiring deep coding expertise for every task. What distinguishes PI.EXCHANGE from standard forecasting tools is its focus on customization and external context. Unlike fixed ERP modules, these models learn continuously from new data and backtest against historical performance to ensure reliability. The platform provides explainability insights, allowing users to see exactly which drivers are influencing their forecasts. Additionally, the ability to deploy the solution on-premise or in a private cloud provides a level of security and architectural flexibility often missing from purely SaaS-based AI offerings, making it suitable for enterprises with strict data governance requirements.
Pros & Cons
Achieves 20-40% higher accuracy than standard spreadsheet-based forecasting.
Automates manual data retrieval and model training on a pre-defined schedule.
Includes built-in access to external data like weather and economic indicators.
Allows for unlimited users across all Demand Forecasting Studio pricing tiers.
Provides model explainability to help users understand exactly which drivers influence demand.
Usage limits on forecast items and model trainings apply to non-enterprise tiers.
Additional fees are required for dedicated compute, SLA-based support, and on-premise deployment.
The standard implementation process requires a four-week setup and workshop period.
Specific pricing is not listed publicly and requires booking a demo for a quote.
Use Cases
Supply chain managers can reduce excess inventory and capital tie-up by using ML-powered forecasts that adapt to changing market conditions.
Retail planners can prevent stockouts of new product lines by simulating 'what-if' scenarios to understand the impact of promotions and seasonal trends.
Data scientists can use the API and UI to automate labor-intensive data prep and model-building tasks, speeding up the production cycle.
Financial analysts in banking can predict customer churn and lifetime value to optimize marketing spend and retention strategies.
Wholesale distributors can integrate external economic indicators like tariffs and inflation into their models to prepare for macro-economic shifts.
Platform
Features
• on-premise deployment options
• custom machine learning models
• model explainability insights
• smart data wrangling
• what-if analysis
• collaborative scenario planning
• external data driver library
• automatic forecast pipeline
FAQs
How accurate is the forecasting compared to traditional methods?
Users typically see a 20-40% improvement in accuracy compared to using spreadsheets or standard ERP software. The platform achieves this by building custom machine learning models that adapt to unique business contexts rather than relying on generic algorithms.
Can I integrate external data like weather or economic trends?
Yes, the platform includes a curated library of external drivers. You can easily incorporate weather, holidays, and economic indicators such as exchange rates or inflation into your models to better understand their impact on demand.
Does PI.EXCHANGE support no-code model building?
The FlexiBuild Studio is specifically designed as an all-in-one no-code machine learning platform. It allows users to perform regression, classification, and clustering tasks without writing code, featuring AI-guided model recommendations.
What deployment options are available for enterprise customers?
Enterprise users have the option for on-premise or private cloud deployments, though additional fees apply. This tier also supports dedicated compute instances for businesses requiring high-performance processing and strict data control.
How long does it take to implement the solution?
The initial implementation process typically spans four weeks. This includes solution workshops in weeks 1-3 to define requirements and scope, followed by deployment integrated into your systems by week 4.
Pricing Plans
Essential
Unknown Price• Unlimited users
• 3,000 Forecast Items per Month
• 3 Model Trainings per Month
• Golden Record of Demand Drivers
• Online Support (Email, Slack, Hub)
• SLA-based Support (Additional fee)
Business
Unknown Price• Unlimited users
• 10,000 Forecast Items per Month
• 10 Model Trainings per Month
• Scenario Planning
• Just-in-Time Actions
• Dedicated Customer Success Manager
Enterprise
Unknown Price• Unlimited users
• Custom Forecast Items
• Custom Model Trainings
• Dedicated Compute Instance (Additional fee)
• On-Prem & Private Cloud Deployment
• Dedicated Technical Resources
Job Opportunities
Product Owner
Achieve 20-40% higher accuracy in demand forecasting with machine learning models that adapt to your business context and automate manual spreadsheet tasks.
Benefits:
A competitive compensation package including opportunities for employee options awards
Opportunities to enjoy meaningful and disruptive works on game-changing products
A flexible, supportive and productive working environment
Passionate colleagues and friends
A strong commitment to your personal and professional growth
Experience Requirements:
Track record of 3+ years of leading and solving customer problems for B2B SaaS enterprise software products as a Product Owner or a Business Analyst
Other Requirements:
Highly analytical mind
Well developed communication skills
Effective organization and management skills
Strong UX understanding
A “can-do” attitude
Responsibilities:
Work closely with cross-functional teams to conceptualize, analyze, define, design, validate, implement and release new features
Analyze product requirements, develop appropriate user stories and manage delivery
Collaborate with and support stakeholders to develop and execute product roadmaps & planning
Collaborate to conduct user research/interview, design sprints and others product development & planning activities
Develop and drive best practices in product development & process management
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Product Designer
Achieve 20-40% higher accuracy in demand forecasting with machine learning models that adapt to your business context and automate manual spreadsheet tasks.
Benefits:
A competitive compensation package including opportunities for employee options awards
Opportunities to enjoy meaningful and disruptive works on game-changing products
A flexible, supportive and productive working environment
Passionate colleagues and friends
A strong commitment to your personal and professional growth
Experience Requirements:
Track record of 5+ years of leading and solving customer problems through production-ready design for B2B SaaS enterprise software products
Other Requirements:
Stella design thinking and design craft
Strong UX skills
Passion for excellence
Deep curiosity about how things work
A “can-do” attitude
Responsibilities:
Drive the design process and iterate designs efficiently to synthesize abstract ideas into concrete artifacts
Conduct regular audits of the product to identify areas for improvement
Engage in various forms of communication (brainstorm and design review sessions) to update teams
Effectively employ and further improve existing design systems
Assist with user researching, testing and validating designs post implementation
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Product Data Scientist
Achieve 20-40% higher accuracy in demand forecasting with machine learning models that adapt to your business context and automate manual spreadsheet tasks.
Benefits:
A competitive compensation package including opportunities for employee options awards
Opportunities to enjoy meaningful and disruptive works on game-changing products
A flexible, supportive and productive working environment
Passionate colleagues and friends
A strong commitment to your personal and professional growth
Experience Requirements:
2+ years as a Data Scientist or ML Engineer in a strong data-driven SaaS environment/product
Real-world experience in working with customers to develop and deploy Data Science and ML solutions
Other Requirements:
Solid analytical thinking and problem-solving skills
Hands-on experience and demonstrable ability to code fluently in Python
Strong working knowledge of efficient data structures, computational complexity and algorithms
Solid theoretical understanding in probability, statistics, supervised and unsupervised learning, time series
Strong experience and demonstrable skills in Data Science stack (scikit-learn, pandas, numpy, scipy)
Responsibilities:
Applying Data Science and Machine Learning to solve real-world business problems
Developing new data-science / ML functions across products
Taking charge of existing code base for continuous maintenance, bug fixing, and improvement
Working with the Software/DevOps Engineering team to brainstorm ideas and develop new features
Participate the design process for new features as a representative from the Data Science team
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Ratings & Reviews
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