Forecasting
and planning with AI
Real-time analysis, processing of millions of variables, and scenario building with an accuracy of 99%. Automated adaptation instead of retrospective tables.
2 min ago
Transformation
Traditional planning vs. AI
Traditional planning
- Based on retrospective data and subjective assumptions
- High probability of human error
- Low speed of response to market changes
- The accuracy of forecasts is usually 60-75%
AI transformation
- Real-time analysis without subjective factors
- Processing millions of variables simultaneously
- Automatic adaptation to changing market conditions
- Forecast accuracy reaches 90-98%
Efficiency
Why businesses choose AI
Annual growth of the AI planning market (SaaS)
Accuracy of AI predictions vs. 70% in manual planning
Factors that AI analyzes simultaneously for scenarios
WHY WE
Why choose Applic
We are a team of developers with 23 years of experience at IT Artel Group. We build complex business systems where AI is part of the solution, not the entire product.
- Full development cycleFrom idea and MVP to production support — all in one place
- Complex business logicNo templates, no CMS — custom architecture for your business needs
- AI as part of a systemIntegrating LLMs deeply into business processes, not superficially
23+
Years of experience
50+
Projects
BEHIND THE TECH
In-house development infrastructure and deep expertise in mathematical modeling.
Data.
A foundation for accurate forecasts
Internal data
- History of sales and balances
- Marketing activity (promo)
- Production and delivery schedules
- CRM data on pipeline transactions
External factors
- Weather conditions and seasonality
- Currency exchange rates and energy prices
- Logistical delays (ports, borders)
- Social and geopolitical events
Market trends
- Competitors' activity and prices
- Macroeconomic indicators
- Changes in consumer behavior
- Search queries (Google Trends)
Directions
Areas of application in the company
- Forecast of sales volumes (Revenue)
- Probability of closing deals in CRM
- Plan for the implementation of KPIs by managers
- Effectiveness of advertising channels
- Optimal residue levels
- Safety Stock replenishment schedules
- Forecasting peak loads
- Transportation capacity of warehouses
- Production cycles and queues
- Operating equipment utilization (OEE)
- Demand for raw materials
- Schedule of preventive maintenance
- The need for staff for projects
- Staff turnover forecast
- Budget for training
- Replacement and vacation schedules
- Release dates (Milestones)
- Allocation of resources between teams
- Risks of going over budget
- Critical paths (CPM)
Comparison.
AI vs. traditional methods
TECHNOLOGIES
Tech stack
We combine the power of LLM with classical forecasting algorithms to achieve the most accurate results.
Application.
Industry-specific solutions
Logistics
- Optimization of loading routes
- Forecast of queues at the borders
- Fleet maintenance planning
Retail
- Forecasting demand at the SKU level
- Optimization of inventory (Stock-out prevention)
- Dynamic pricing
Finance.
- Cash flow forecasting (Cash Flow)
- Identify risks and anomalies
- Budget planning for "What-if" scenarios"
Analytics
Dynamics of the global AI market
Annual market growth (CAGR)
According to Precedence Research, the market of AI forecasting solutions shows steady growth, which confirms the transition of global businesses to an autonomous management model.
Accuracy
To. 99% prediction accuracy vs. 70% manual method
Savings.
Reduction in operating expenses by 25% by optimizing
Speed
В 10 times faster processing of "what if" scenarios"
Evolution
From a tool to an autonomous enterprise
AI as an analyst's assistant
AI prepares data, visualizes trends, and offers options. A human checks and makes the final decision in Excel or ERP.
Autonomous Enterprise
AI independently manages supply chains, prices, and procurement. Humans only set global strategies and goals.
Process
Our approach: Pilot-First
Quick start, hypothesis testing, scaling.
Pilot Launch
Quickly deploy a prototype on limited data to test accuracy and ROI.
Deep Discovery
A detailed audit of all knowledge sources, building architecture and mapping processes.
Engineering
Development of a full-scale system with support for Multi-agent workflows and APIs.
Deployment
Industrial launch, staff training, and full migration of corporate knowledge.
Growth
Support, model training, and expansion of the system to new business verticals.
Ukrainian
Russian
English