Forevate is a new market intelligence service from Fcode Labs that helps businesses make faster, smarter decisions. It rapidly tracks market shifts, competitor moves, pricing changes, social trends, and other signals, allowing companies to respond in real time.
Initially focusing on manufacturing, logistics, supply chain, retail, e-commerce, and fast-moving consumer goods (FMCG), Forevate combines internal data with external market intelligence for actionable insights. Echelon spoke to Buddhishan Manamperi, Co-CEO; Pamaljith Harshapriya, COO – Delivery; Tharindu Malawaraarachchi, Co-CEO – Execution & Growth Lead; and Ramesh Rathnayake, CTO – Technology & Governance Lead about Forevate and its role within a company that grew from a three-person startup into a 100-member global software development team.
Forevate also follows a history of working with global clients like the Singapore government and Mercedes-Benz, serving markets across more than 10 countries, and partnering with multiple international collaborators.
What market shifts or client frustrations made it clear that traditional planning cycles are no longer sufficient?
Buddhishan Manamperi: Traditional planning cycles just don’t cut it anymore. Businesses used to be able to map out quarters or even years in advance, factoring in competitor moves, pricing, and market conditions. The pace of change is relentless today, because overnight tariffs, new global competitors, or sudden shifts in social trends can completely upend those plans.
Customers are now influenced by social media alongside ads and billboards, and new trends emerge hourly. Companies that aren’t aware of these shifts risk missing opportunities or facing critical disruptions. Forevate addresses this by bringing all the relevant signals, like market trends, competitor activity, social insights, weather patterns, stock movements, and internal company data, into one real-time view. This allows businesses to act fast, adapt quickly, and make smarter decisions every day, rather than relying on outdated forecasts.
Why must enterprises shift from treating AI vendors transactionally to a Strategic Partnership Model to build real competitive advantage?
Tharindu Malawaraarachchi: Some companies think AI is transactional, that they can just buy it and use it. But for those aiming for a competitive advantage, it has to be built for their organisation. That is what we do.
We partner with companies to deeply understand their structure, map decision-making units, and convert internal data into actionable signals. We then combine this with relevant external market intelligence.
A plug-and-play tool would not be enough, so we tailor Forevate using our pre-built IPs, bridging gaps and creating a system that learns and improves decisions continuously. We plan with a five-year horizon but focus on actionable 12-month roadmaps, ensuring impact immediately. By integrating AI into the daily decision-making process, companies make better choices every day, week, and month, building a compounding competitive advantage over time. We work with them throughout the process, and we provide a set of tools designed to work specifically for each organisation.
How does the Pods model convert strategy into tangible, repeatable outcomes for clients?
Pamaljith Harshapriya: We aim to work with companies over the long term because technology, AI, and their business will all evolve. Building a true competitive advantage takes time, but companies need results quickly. That’s why we structure our work in two phases: DesignPod and DeliveryPod.
DesignPod lasts six to eight weeks, during which we deeply understand the business, map decision-making units, and identify where AI can deliver the fastest impact. This is the basis for our medium-term roadmap.
DeliveryPod is a subscription-based implementation phase. Every month, we run experiments with the client, measure results, and iterate. If something works, we double down; if not, we pivot. The process ensures clients see ROI from the third month and make informed decisions about continuing. Most continue for multiple years because the results speak for themselves.
Our approach is simple: build, ship, measure, and iterate. We focus on delivering features not for their own sake, but for measurable value. Fast validation and continuous adaptation ensure the AI is actually solving real business problems instead of producing unrelated outputs.
How does Forevate ensure AI outputs are explainable, auditable, and aligned with governance requirements?
Ramesh Rathnayake: AI is central to Forevate, but it works alongside our internal processes and infrastructure. From day one, governance and auditability were built into our development workflow. We combine AI agents with human experts, collaborating to produce tailored insights and tools for clients.
We don’t rely solely on large language models (LLMs). Core analyses use established methods like classification, regression, and decision trees, ensuring explainability and auditability. LLM agents augment this by integrating internal company data, external market data, and features generated by our algorithms.
Explainability is enhanced through retrieval-augmented generation (RAG) and chain-of-thought (COT), which link insights to their source data and map the reasoning behind each decision.
Governance is handled via dedicated hubs where the RiskHub ensures a risk-based approach to applying insights, the PolicyHub checks compliance with internal and external policies, and the audit hub logs all decisions and actions. Together, these layers ensure that Forevate’s AI outputs are explainable, auditable, and aligned with governance requirements.