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Your nearest office- Sri Lanka
Fortude (Pvt) Ltd
146 Kynsey Road, Colombo 7, Sri Lanka
Email – talk-to-us@fortude.co
Phone – +94 11 453 1531
Every day, we bring together diverse perspectives, strong leadership and responsible thinking to build a business that creates lasting value for our clients, people and communities.
Your nearest office- Sri Lanka
Fortude (Pvt) Ltd
146 Kynsey Road, Colombo 7, Sri Lanka
Email – talk-to-us@fortude.co
Phone – +94 11 453 1531
As businesses navigate the complexities of modern operations, the demand for intelligent tools that don’t just answer questions but actively solve problems has never been higher. Charlie, Fortude’s AI-powered virtual assistant, drives productivity by simplifying tasks and accelerating processes. Built on the Retrieval-Augmented Generation (RAG) framework, Charlie excels at delivering precise and context-aware responses by blending retrieval and generation models.
This blog explores how we are extending Charlie’s core strengths to incorporate agentic capabilities, transforming it from a powerful knowledge assistant into a proactive, solution engine. We will detail four groundbreaking use cases that leverage this new architecture to drive efficiency and foresight in enterprise environments.
Agentic AI represents the evolution of artificial intelligence towards autonomous agents that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI tools that are purely reactive, agentic systems use decision-making frameworks to choose the best actions and can adapt to changing environments, often learning from experience using techniques like reinforcement learning.
Furthermore, in complex enterprise settings, these agents often operate in multi-agent systems, communicating and cooperating to achieve common goals. This capability is proving vital across various industries, including finance, healthcare, and supply chain management.
Charlie is a central piece of Fortude’s Automation product portfolio, which also includes the dedicated HR knowledge assistant, Ellie. Charlie already uses a skills Manager to execute tasks like creating tickets and automating routine actions, making it a proactive tool for efficiency, notably supporting functions like HR self-service and currently piloting an internal HR use case by integrating the employee handbook.
Expanding Charlie’s capabilities with CharlieX
While Charlie specializes in knowledge retrieval and task automation, CharlieX complements it by focusing on enterprise intelligence. It connects directly to ERP and CRM systems, allowing anyone, regardless of technical skill, to ask business questions in natural language, uncover hidden patterns, and detect anomalies before they become business issues. By supporting advanced querying and rapid context parsing, CharlieX works to democratize access to Business Intelligence (BI) across the enterprise, regardless of technical skill. Its primary benefits include drastically reducing insight generation time and automating the detection of patterns and anomalies within data.
These capabilities are being extended in our roadmap, with the aim of making Charlie more agentic in the future. Let’s take a quick look at the new features that will support this goal.
Designed for supply chain managers, planners, and retail operations teams, this capability tackles the common problem of imbalanced inventory across multiple stores or warehouses.
The challenge: Retailers frequently experience stock-outs in some locations, leading to lost sales, while simultaneously accumulating excess stock elsewhere, which inflates carrying costs and ties up working capital. Manual redistribution decisions are often slow, reactive, and lack a proactive mechanism to predict demand variations.
The agentic solution: This AI-powered inventory levelling agent uses machine learning to forecast demand based on historical sales and demand signals (like promotions and seasonality). It then assesses inventory risks and proactively recommends balancing actions across the network.
Key outcomes: The agent delivers actionable guidance through:
This capability facilitates secure and contextual interaction between AI agents and the Infor M3 ERP system.
The challenge: Infor M3 ERP currently lacks a unified, AI-accessible interface layer. This means that every new automation or AI use case requires redundant API and logic design, and there is no standardized protocol to define intent, context, and response formats.
The agentic solution: We introduced an MCP server for Infor. This reusable, structured interface allows AI agents (like Charlie) to interact with Infor APIs using context-aware prompts. The MCP defines the exposed Infor APIs as tools for AI agents and establishes a secure authentication and permission model.
Key outcomes: This solution provides a centralized control and governance layer, enabling:
This agent is built to help retail planners, inventory managers, and supply chain analysts anticipate demand changes by leveraging external market intelligence.
The challenge: Retail and fashion businesses struggle with stockouts or overstocking due to inaccurate forecasts. Traditional planning processes are often manual and siloed, failing to incorporate external factors such as promotions, weather, or customer sentiment that heavily influence demand.
The agentic solution: The Signal-Based Forecasting Agent integrates internal business data (including inventory, sales tickets, and purchase orders) with a wide array of external market signals. These signals are categorized into short-term (e.g., weather, holidays), medium-term (e.g., emerging styles, campaigns), and long-term (e.g., historical sales trends) influencers.
Charlie uses AI/ML models to combine this internal data with weighted external signals. The agent then forecasts SKU level demand and suggests future purchase order (PO) quantities and required in-house dates across various time windows.
Key outcomes: This automation:
This use case is designed for ERP consultants and release managers supporting Infor M3 clients, helping them identify which feature releases will impact their business and plan accordingly.
The challenge: Infor M3 publishes multiple release updates annually, requiring consultants to manually read detailed release notes, identify affected modules, and cross-check changes against each client’s custom configurations and source code. This process is error-prone, highly repetitive, and risks missing critical compliance tasks.
The agentic solution: Charlie’s multi-agent architecture completely automates the release analysis process. When a new M3 release report is published, a coordinated team of agents goes into action:
Key outcomes: This agentic workflow provides consultants with a complete, pre-assessed set of tasks before they even open the release report. It saves 80–90% of the time spent on manual analysis, prevents missed critical changes, and builds client trust through consistent, intelligent foresight
These innovations aren’t theoretical, they’re already being piloted across supply chain, retail, and ERP operations. At Fortude, we believe the pathway from intelligence to agency is not just the future, it’s here. If you’re looking to streamline complex processes, gain predictive foresight, and empower your teams with proactive AI, now is the time to explore how Charlie and CharlieX can transform your enterprise operations.