Every enterprise sits on mountains of data. Transactions, inventory positions, purchase orders, shipment logs, all stored. But when a planner needs to know what to do next, the stack goes silent. They export spreadsheets, stitch SQL scripts while decision-makers often wait days, or even weeks, for answers that should take minutes.

Most legacy platforms are systems of record. They excel at capturing what happened in the past but were never designed to answer the questions that drive a business forward: What should we do next? What happens if a supplier fails? Where is our working capital trapped? Which plan survives a demand shock?...

These platforms were designed in a different era. Customizations cost a fortune, business rules are baked in and rarely revisited, and data sits siloed across departments. Rip-and-replace migrations take 12 to 18 months, and by the time they're done, the business has moved on.

Five years ago, we worked on a project that changed how we thought about the scale of this problem. It was a system that powered over 20 optimization solutions for a Fortune 50 client, from demand-supply matching across a 40,000-node European network to safety-stock strategy in the US. The client attributed roughly $200–300 million in savings to that system. We started Effimal because we believe every company deserves that level of capability.

Efficient + Optimal

Effimal is a decision intelligence platform that helps businesses become efficient through optimal decision-making. Powered by scenarios and AI agents, it acts as a proactive layer that sits on top of your existing stack, turning static data into continuous action.

The platform is built around four core primitives:

• Datasets: This is how data enters the platform. Whether you are uploading Excel, taking a snapshot from an existing system, or streaming from SAP and data warehouses, we’ve removed the friction. Every decision engine ships with pre-built templates so teams can start without a massive data engineering project.

• Decision Engines: These are the mathematical core of the platform. Each engine is purpose-built for a specific challenge: demand forecasting, inventory optimization, procurement intelligence, logistics, and production planning. They encapsulate complex operations research that typically requires a team of specialists.

• Workflows and Scenarios: This is how decisions get tested. Every engine runs as a transparent, auditable workflow. Scenarios allow planners to run these workflows against real-time data, compare potential outcomes, and stress-test a plan before committing to it.

• AI Agents: Our agents sit across all three layers with full context of business logic. They answer questions in natural language, surface critical anomalies that require attention, and run autonomous simulations to stay ahead of market shifts.

Built for the AI era

Effimal wasn't retrofitted for AI; It was built for it.

• Modern architecture: High-performance microservices on Kubernetes, built to scale to global node networks without the fragility of legacy monoliths.

• Fully extensible: A configurable core that lets enterprises add new engines, workflows, and agents without rebuilding the stack.

• Agentic by default: Agents sense market shifts (like a geopolitical event, a demand change, a pricing signal) and translate them into actionable scenarios.

• No rip-and-replace: Start with Excel in, Excel out, in days. Graduate to SAP, data warehouses, and full system integrations at your own pace.

• Deploy anywhere: On-prem, any major cloud, or hosted by us with private VPN peering. IT integration in weeks, not years.

• Enterprise-grade trust: SOC 2 certification in progress, with HIPAA and GDPR compliance on our immediate roadmap.

Our path so far

Effimal is now live. Companies are now using the platform today, tuning engines for their use cases, extending agents to fit their specific workflows, and working alongside our team. What we hear consistently: the hardest part of decision intelligence isn’t the math. It's getting the right data to the right engine fast enough for teams to act on it.

Today, we’re a team of 8 across engineering, design, operations research, data science, and DevOps. We have demand forecasting, inventory optimisation, procurement intelligence, logistics orchestration, and production planning ready and working on more. We expect the same primitives will reach into finance, workforce planning, and anywhere the question is "what next?" rather than "what happened?"

Where we're headed

Our immediate focus is helping pilot customers achieve optimal outcomes and turning the use cases they uncover into core platform capabilities. We’re also onboarding design partners to explore how AI agents can further support complex planning workflows.

This is the first in an ongoing series. We'll go deep on each pilot, the problems, the outcomes, and the thinking behind the platform as we make progress.

If you're a business leader staring at dashboards wondering why your best decisions still take weeks, we’d love to talk.