RealityOS

RealityOS is an AI Decision Intelligence Platform that learns from every decision, simulation, outcome, and user interaction.

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RealityOS

Tagline

Before you act, simulate the future.

Inspiration

Most AI tools answer questions. But real-world decisions require more than answers. Small businesses, founders, and operators make expensive decisions every day with incomplete information: whether to hire, buy inventory, launch a campaign, expand locations, or prepare for a major event.

RealityOS was inspired by a simple question:

What if an AI agent could help people simulate the future before committing money, time, and resources?

What it does

RealityOS is an AI Decision Intelligence Platform powered by Gemini agents and MongoDB Decision Memory.

The MVP focuses on small businesses preparing for major event surges, such as the 2026 World Cup. A business owner can ask:

“I own a pizza shop near BC Place in Vancouver. Should I hire 3 temporary workers and increase inventory by 40% during World Cup weekend?”

RealityOS then:

  1. Breaks the decision into assumptions.
  2. Searches MongoDB Decision Memory for similar past decisions.
  3. Runs conservative, expected, and aggressive future scenarios.
  4. Calculates projected revenue, costs, profit, risk, and confidence.
  5. Generates a recommendation and 7-day action plan.
  6. Stores the simulation, agent trace, recommendation, and patterns back into MongoDB.

How we built it

RealityOS is built as a multi-agent web application.

Core stack:

  • Next.js
  • TypeScript
  • Tailwind CSS
  • Gemini reasoning layer
  • Google Cloud-ready architecture
  • MongoDB Atlas
  • MongoDB MCP-ready integration
  • MongoDB Decision Memory
  • Cloud Run deployment path

The agent system includes:

  • Supervisor Agent
  • Memory Agent
  • Research Agent
  • Finance Agent
  • Risk Agent
  • Scenario Agent
  • Action Agent
  • Learning Agent

MongoDB is not used only as storage. It acts as the memory layer of the agent system. Every decision, scenario, outcome, recommendation, and learned pattern becomes reusable intelligence for future simulations.

Best use of MongoDB

MongoDB powers RealityOS as the Decision Memory Engine.

It stores:

  • Decisions
  • Simulations
  • Scenarios
  • Agent traces
  • Recommendations
  • Outcomes
  • Pattern insights
  • Future vector-search-ready memory records

The Memory Agent uses MongoDB to retrieve similar decisions and patterns. The Learning Agent writes new insights back into MongoDB after each simulation. This creates a growing memory graph of business decisions over time.

Challenges we ran into

The biggest challenge was designing RealityOS to feel like more than a chatbot. We wanted the experience to show real agentic behavior: planning, memory retrieval, scenario generation, risk evaluation, and human approval.

Another challenge was making MongoDB central to the product rather than treating it as a normal database. We solved this by designing MongoDB as the intelligence layer where every decision becomes reusable memory.

Accomplishments that we're proud of

We are proud that RealityOS turns a simple business question into a structured decision simulation.

Instead of giving generic advice, it produces:

  • Similar decision memories
  • Three possible futures
  • Risk analysis
  • Financial projections
  • Pattern insights
  • A concrete 7-day action plan

We are also proud of the product concept: “Before you act, simulate the future.” It creates a new mental model for human-agent collaboration.

What we learned

We learned that the next generation of AI agents should not only automate tasks. They should help humans make better decisions before action is taken.

We also learned that memory is one of the most important missing pieces in agent systems. Without memory, agents repeat themselves. With MongoDB Decision Memory, every simulation can improve the next one.

What's next for RealityOS

Next, RealityOS can expand beyond World Cup business planning into:

  • Retail expansion decisions
  • Hiring and workforce planning
  • Marketing budget simulations
  • Supply chain risk planning
  • Real estate location decisions
  • Startup strategy simulations
  • City and event operations planning

Future roadmap:

  • MongoDB Atlas Vector Search for similarity-based decision retrieval
  • Real outcome tracking
  • Deeper Gemini Agent Builder orchestration
  • More MCP tool integrations
  • Collaborative team decision rooms
  • Decision audit trails
  • Industry-specific simulation templates

Built with

  • Gemini
  • Google Cloud
  • MongoDB Atlas
  • MongoDB MCP-ready architecture
  • Next.js
  • TypeScript
  • Tailwind CSS
  • Cloud Run-ready deployment

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