Shehan Makani

IntelliForm Workflow Showcase - 2026-05-03 Full workflow showcase IntelliForm connects prompt-to-formulation with the downstream proof stack. This dashboard covers the 10 public starter prompts plus the tabs that make IntelliForm more than a formula generator: optimization, sustainability, regulatory posture, carbon, stability, certification screening, QSAR, reformulation, and memory. 10 Starter prompts rerun 9 Product tabs represented 25 Regression tests passing 0 Reports generated for failed runs Actual Live App Results These are from the running FastAPI app, not the static HTML: one real formulation run, downstream saved-run payloads, and the active tool endpoints used by the React tabs. API Health OK Version 2.1.0 responded with 12 modules loaded: optimizer, Pareto, Bayesian, EcoMetrics, QSAR, regulatory, ...

  IntelliForm in Practice: Three Real Runs, Unedited IntelliForm v0.6 · Showcase Run · April 2026 Three real runs. Unedited. After the parser and controller reliability upgrade, we captured three IntelliForm runs exactly as they came out — two successes and one honest infeasibility. Here they are, with full blend compositions, costs, eco scores, and regulatory output. Platform  intelliform.streamlit.app Repo  github.com/Cheme-Nova/IntelliForm Captured  2026-04-24 v0.6 · Post-Upgrade Demos are easy to cherry-pick. What I want to show here is the actual output of the upgraded IntelliForm controller — three runs chosen to represent the range of what the platform does today: a clean agricultural success, a food-vertical success with real regulatory flags, and a case where the optimizer correctly refused to produce a result rather than hallucinate one. All three were captured locally on Ap...

Most personal AI assistants have one thing in common: your data leaves your device. Every message you send to ChatGPT, Claude, or Gemini travels to a server you don't control, gets processed by a model you don't own, and is stored under a terms of service you probably haven't read. For casual conversation, that's fine. For anything involving business data, client information, proprietary research, or just plain privacy - it's a problem. I'm a chemical engineer and founder running two companies simultaneously. When I started feeding supplier pricing, SDS sheets, and customer databases into AI tools to speed up my workflow, I realized I needed a different approach. So I built one. Maximus-X Sentinel is a fully private, GPU-accelerated multi-agent AI assistant. It runs on hardware I own, on my local network, with zero cloud inference. The full codebase is open source: github.com/shehanmakani/MaximusX Here's exactly how it works and how you can build it to...

The Agentic Shift in Chemical Manufacturing: Scaling Sustainable Innovation via the IntelliForm Framework Abstract The global chemical manufacturing sector in 2026 has entered a definitive phase of transition, characterized by the move from static, human-directed digital workflows toward autonomous agentic systems. This report analyzes the IntelliForm framework, a proprietary architecture designed to bridge the gap between digital intelligence and physical industrial rigor. By integrating a four-layered pipeline—Data, Inference, Sourcing, and Sustainability—this framework facilitates a "Software-to-Steel" transition that addresses the pervasive "pilot purgatory" affecting 85% to 95% of industrial artificial intelligence projects. Central to this shift is the deployment of multi-agent systems (MAS) that move beyond predictive modeling to autonomous orchestration, reasoning, and execution. Technical validation through YOLOv8-based computer vision for quality assurance...