Catnip Infotech
RPA Modelling & Bot Sizing
In today’s automation-driven business landscape, success depends not just on deploying bots but on designing the right automation foundation. As organizations scale their digital transformation initiatives, Robotic Process Automation (RPA) has become a catalyst for driving efficiency, precision, and growth. At Catnip Infotech, we help enterprises move beyond basic automation by delivering strategic RPA Modelling and Bot Sizing services that ensure your automation ecosystem is optimized, scalable, and built for long-term value.
Our approach focuses on building intelligent, performance-driven RPA frameworks that deliver measurable ROI and operational agility. Whether your goal is to automate a single process or scale automation across multiple business units, Catnip Infotech helps you determine the ideal bot mix, infrastructure setup, and orchestration strategy for maximum impact.
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RPA Modelling — Building the Automation Blueprint
RPA Modelling serves as the foundation for successful automation. It involves designing an end-to-end automation architecture identifying processes suitable for RPA, defining workflows, and mapping how bots interact with data, systems, and users. At Catnip, our modelling process emphasizes clarity, scalability, and compliance, ensuring every automation initiative is built on a strong, future-ready foundation.
Our comprehensive modelling approach includes process discovery, workflow mapping, bot categorization (attended, unattended, or hybrid), exception handling, integration design, and scalability modelling all tailored to your organization’s needs. This ensures a seamless, high-performance automation journey that scales with your business.
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Bot Sizing — Optimizing Performance and Cost
Effective Bot Sizing ensures that your automation infrastructure is right-sized for performance and efficiency. We assess the number, type, and capacity of bots required based on workload patterns, processing demands, and runtime utilization.
By carefully balancing automation capacity with business requirements, Catnip Infotech helps organizations optimize costs, eliminate inefficiencies, and ensure consistent performance. Whether your environment is on-premise, cloud, or hybrid, our bot sizing strategy guarantees scalability and stability without unnecessary overhead.
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Empowering Scalable Automation with Catnip Infotech
With Catnip Infotech’s expertise in RPA Modelling and Bot Sizing, enterprises can scale automation confidently with the assurance of performance, flexibility, and measurable results. Our structured approach transforms automation from a tactical initiative into a strategic enabler of business excellence.
Key Factors in Bot Sizing
Factor | Why It Matters |
|---|---|
Volume of Transactions | Determines how many instances of a bot are required to process data within SLA. |
Average Handling Time (AHT) | Impacts how long a bot takes to complete one transaction, which affects capacity planning. |
Process Complexity | Higher complexity may require more bot orchestration, integration, and exception management capabilities. |
Bot Type (Attended/Unattended) | Influences infrastructure needs, human involvement, licensing, and support. |
Business Hours & SLA | Helps model bot workload distribution 24x7 or business hours only and ensures SLA compliance. |
Peak vs Normal Loads | Critical for sizing infrastructure and scheduling bots efficiently to handle volume spikes. |
Infrastructure (Cloud / On-Prem) | Impacts elasticity and resource scaling cloud allows flexible sizing; on-prem needs strict capacity planning. |
Integration Complexity | APIs, UI-based automation, legacy system interactions affect bot performance and stability. |
Error Rates & Exception Handling | More errors = more re-runs or manual handling, affecting bot load and sizing. |
Data Handling Requirements | Large file processing or real-time data ingestion impacts bot memory and speed requirements. |
Catnip’s RPA Modelling and Sizing Framework
At Catnip Infotech, we follow a structured 4-Step Framework to ensure precise and efficient RPA planning helping enterprises build scalable, cost-effective, and performance-driven automation ecosystems.
1. Process Discovery & Segmentation
- Identify automation-ready processes and tasks
- Categorize based on frequency, volume, and complexity
2. Workload Analysis & Simulation
- Analyze historical transaction volumes and patterns
- Simulate workload performance and Average Handling Time (AHT) to estimate capacity needs
3. Bot Type Allocation & Cost Modelling
- Determine the right mix of Attended, Unattended, and Hybrid Bots
- Define bot run-times, licensing models (concurrent or named), and infrastructure costs for accurate ROI estimation
4. Infrastructure Planning
- Select deployment mode: On-Premise, Cloud, or Hybrid
- Size and plan for compute, storage, and orchestration resources to ensure seamless scalability
Note: Sizing depends on bot concurrency, scheduling, and infrastructure performance.
Why Catnip for RPA Modelling & Sizing?
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Tool Agnostic Expertise – Proficiency in UiPath, Automation Anywhere, Blue Prism, Microsoft Power Automate, and more.
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End-to-End Capability – From discovery to governance, and scaling to COE (Center of Excellence) enablement.
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Business-Aligned Models – Sizing based on actual business demand, growth, SLA, and cost objectives.
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Cloud-Ready Automation – Design bots for hybrid or cloud-native deployments.
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Performance Optimized – Our models include error-handling, reusability, and real-time monitoring.
Sample Sizing Estimation Table
Process Name | Volume/day | AHT (min) | Total Bot Hours Needed/day | Bot Type | Bots Required (8hr/day) |
|---|---|---|---|---|---|
Invoice Processing | 1200 | 2 | 2,400 min = 40 hrs | Unattended | 5 |
Employee Onboarding | 100 | 10 | 1,000 min = 16.6 hrs | Attended | 3 |
Report Generation | 60 | 5 | 300 min = 5 hrs | Scheduled Bot | 1 |
Fitment Based on Use Case
Use Case | What to Prioritize | RPA Fit |
|---|---|---|
High-volume data entry | Speed, error-free processing | Unattended bots with parallel execution |
Front-office / Agent Assist | Real-time responsiveness | Attended bots with smart triggers |
Night-time batch jobs | Automation orchestration, scheduling | Scheduler + unattended bots |
Exception-heavy workflows | Dynamic decision-making, alerts | Hybrid bots + AI-based decision layers |
AI/ML model integration | API connectivity, unstructured data | RPA + AI integration frameworks |
Regulatory documentation & audit | Logging, compliance, traceability | Unattended bots with audit trails and reports |