FlowOps AI

Inspiration

When end-users interact with traditional Enterprise Resource Planning (ERP) systems, they typically encounter massive friction. We were inspired to solve five critical problems plaguing legacy ERPs: information overload, manual data entry fatigue, rigid workflows, limited mobile accessibility, and complex reporting. We wanted to build an agentic ERP middleware that eliminates this bloat using dynamic role-adaptation, multimodal AI intake, and real-time process mining.

What it does

FlowOps AI is a Zero-Friction Agentic ERP Copilot and Role-Adaptive PWA.

Role-Adaptive Chameleon Workspace: It dynamically morphs its entire UI based on the active persona (e.g., Warehouse Deskless Worker, Procurement Officer, or CFO Executive) instead of forcing every employee through dense menu tabs.

Zero-Entry Multimodal Intake: Users can speak natural voice commands or drop raw vendor invoices, and the AI automatically extracts structured entities and stages the ERP transaction.

Process-Mining Radar: It analyzes event logs to highlight hidden dwell-time bottlenecks and executes 1-click AI workflow remediations.

Natural Language BI: Non-technical users can ask plain-English questions to generate instant visual KPI breakdowns.

How we built it

We architected FlowOps AI as a mobile-first Progressive Web App (PWA) paired with an agentic Node.js backend.

Frontend: We built a zero-install, offline-ready mobile-first PWA featuring touch-first cards for floor staff and barcode/QR scanner simulations.

AI Intake & Processing: We implemented OCR and voice multimodal intake to eliminate manual typing.

Guardrails: To ensure the AI never hallucinated financial data, we implemented a Deterministic Pre-Commit Guardrail. Before executing any mutating ERP tool call, the guardrail evaluates the grounding confidence against the raw user transcript (enforcing a calibrated safety threshold of 0.85).

Challenges we ran into

Preventing AI Hallucinations: Generative AI models are eager to guess missing variables. If a user didn't state a unit price, the LLM would occasionally hallucinate one to complete the function call. We solved this by building a deterministic guardrail that successfully intercepts ungrounded tool arguments in roughly 112ms and prompts the user for confirmation.

Zero-Install Mobile Friction: Ensuring deskless workers could use the app seamlessly on the floor was tough. We opted for a PWA architecture so it could be installed directly from the browser with zero app-store friction.

Accomplishments that we're proud of

We are incredibly proud of eliminating manual data entry fatigue by creating a system where users can simply speak or scan documents to automatically validate SKUs and stage transactions. Additionally, building the sub-115ms Deterministic Pre-Commit Guardrail ensures that every single ERP write-action is 100% grounded in user input before touching the enterprise ledger, bringing total trust to the AI automation.

What we learned

Process Mining is Eye-Opening: By dynamically tracking dwell times, we realized that manual PDF invoice-to-PO line item verification across legacy screens was causing massive delays (e.g., 31.5 hours). Executing automated AI 3-way matching drastically optimized these bottlenecks.

Deterministic Guardrails Build Trust: Showing the user exactly why an action was blocked (because a specific parameter wasn't grounded in their voice command) turns a frustrating AI error into a trust-building safety feature.

What's next for FlowOps AI

Moving forward, we plan to expand our automated AI 3-way matching capabilities to resolve more complex supply chain and quality assurance (QA) bottlenecks. We also aim to scale the Role-Adaptive PWA to support additional enterprise personas and seamlessly integrate with more legacy enterprise ledgers.