Building ZeroCo: An AI-Native Operating System for Generating Digital FTE Teams with Amazon Nova
Meet Zero.co: The world's first autonomous CRM. While you sleep, our AI FTEs research leads, build pipelines, and close deals end-to-end on autopilot. Stop managing software. Let it manage itself. Meet Zero.co: The world's first autonomous CRM. While you sleep, our AI FTEs research leads, build pipelines, and close deals end-to-end on autopilot. Stop managing software. Let it manage itself.
Most businesses do not fail because they lack tools.
They fail because their operations are messy.
A customer sends a message. Nobody replies fast enough.
A lead comes in. Nobody follows up.
A task is repeated every day. Nobody documents it.
A team wants to use AI. Nobody defines what AI is allowed to do.
A lead comes in. Nobody follows up.
A task is repeated every day. Nobody documents it.
A team wants to use AI. Nobody defines what AI is allowed to do.
This is the problem I wanted to solve with ZeroCo.
ZeroCo is an AI-native operating system that helps a business turn its desired result into a structured team of Digital FTEs: AI workers with clear roles, measurable outcomes, tools, approval rules, and operating guardrails.
This project was built as part of the Amazon Nova hackathon to explore how generative AI can help businesses move beyond generic chatbots and toward controlled, outcome-driven AI operations.
Instead of starting with the question, “Which chatbot should we build?”, ZeroCo starts with a more important question:
What business result should this company produce, and what digital workers are needed to produce it safely?
The Problem
Most AI tools are too generic.
A company signs up for an AI assistant, connects a few tools, and then expects it to magically improve the business. But real operations are more complicated than that.
A useful AI system needs to understand:
- What result the business wants
- Who the customer is
- What bottleneck blocks the result today
- Which work is repetitive
- Which work is high-risk
- Which actions need human approval
- Which actions must never happen
- Which tools the team already uses
- How success will be measured
Without these answers, AI becomes risky.
It may respond to customers incorrectly, make promises the business cannot keep, send the wrong information, or automate work that should require human approval.
That is why ZeroCo is designed around operating structure first, automation second.
Why I Built This for the Hackathon
For this hackathon, I wanted to build something that does not simply demonstrate AI generation, but demonstrates how AI can help design a real operating system for a business.
Many AI demos generate text, summaries, or chat responses. Those are useful, but businesses need more than content generation.
They need systems that can answer questions like:
- What should this AI worker be responsible for?
- What result should it improve?
- What tools should it use?
- What actions should it avoid?
- When should it pause for human approval?
- How do we know if it succeeded?
ZeroCo is my attempt to turn those questions into a repeatable product experience.
The goal is to show how Amazon Nova can support an AI-native workflow where business inputs are transformed into structured operating models, Digital FTE recommendations, guardrails, and success criteria.
What ZeroCo Does
ZeroCo helps founders, operators, and small teams generate an AI-native operating system for their company.
The system works through two focused forms.
Form 1: Define the Business Result
The first form captures the foundation of the business operation:
- What company or operation are you building?
- Who are your customers?
- What painful problem blocks the result today?
- What measurable result do customers pay for?
- What departments or functions exist or are needed?
- What human roles exist today?
- What tools does the team already use?
This step prevents the AI system from becoming vague. It forces the business to define the result before selecting agents or automations.
For example, for a home-service business, the measurable result might be:
Increase booked jobs from existing inbound leads while reducing manual admin work.
That is much more useful than saying:
Use AI for customer support.
The first version is measurable.
The second version is vague.
The second version is vague.
ZeroCo is built around the first version.
Form 2: Select Digital FTEs and Guardrails
The second form focuses on work design and safety:
- What work is repetitive?
- What work is high-risk?
- What results should Digital FTEs produce or improve?
- What actions always need human approval?
- What actions should be blocked?
- What can be auto-approved?
- What is the monthly AI budget?
- What is the risk tolerance?
Then the user selects the Digital FTEs the business needs.
Examples include:
- Support Agent
- Sales Agent
- Research Agent
- Finance Agent
- Agent Developer Agent
- Tool Connector Agent
- Result QA Agent
Each Digital FTE is not just a chatbot. It has a job, an expected result, and operating limits.
Why Digital FTEs Need Guardrails
One of the biggest mistakes in AI automation is giving agents too much freedom too early.
In the real world, some actions are safe to automate:
- Collecting customer details
- Asking follow-up questions
- Tagging leads
- Updating CRM fields
- Sending appointment reminders
- Generating daily summaries
But other actions should require human approval:
- Final quotes
- Refunds
- Discounts
- Cancellations
- Emergency dispatch
- Payment disputes
- Legal or safety-related advice
- Any promise that creates financial responsibility
ZeroCo separates these categories before the AI system starts operating.
This makes the system safer, more useful, and easier for a business owner to trust.
How Amazon Nova Fits
ZeroCo is designed as an AI-native operating layer where Amazon Nova can support the intelligence behind the workflow.
In this type of system, Amazon Nova can help with:
- Understanding business descriptions
- Extracting operating requirements
- Classifying repetitive and high-risk work
- Generating Digital FTE recommendations
- Drafting operating guardrails
- Turning messy business inputs into structured workflows
- Evaluating whether the generated operating system matches the business result
For example, if a user describes a business like:
“We run a plumbing company and lose leads because we miss calls and reply late to WhatsApp messages.”
The AI system can understand that the business needs:
- Lead intake
- Customer support
- Job qualification
- Scheduling
- Follow-up
- CRM updates
- Owner reporting
Then ZeroCo can recommend a Digital FTE team such as:
- Support Agent
- Sales Agent
- Tool Connector Agent
- Result QA Agent
The important part is that the system does not only generate text. It generates an operating model.
That operating model can become the foundation for future AI execution.
Example Use Case: Home-Service Businesses
A strong use case for ZeroCo is small home-service companies such as plumbers, electricians, HVAC businesses, cleaning companies, pest control companies, and appliance repair teams.
These businesses often live or die by inbound leads.
If they miss one call or reply late to a message, they may lose the job to a competitor.
For this segment, ZeroCo can generate an AI-native operating system focused on:
- Capturing every inbound lead
- Asking the right qualification questions
- Checking urgency
- Creating draft appointments
- Following up with customers
- Sending reminders
- Collecting reviews
- Giving the owner a daily summary
The goal is not to replace the business owner.
The goal is to remove repetitive admin work so the owner can focus on service delivery, customer trust, and business growth.
Community Impact
The target community for ZeroCo is small business owners and operators who do not have large teams, expensive consultants, or dedicated automation departments.
Many small businesses already use tools like WhatsApp, Gmail, Google Calendar, spreadsheets, CRMs, and website forms. But their daily work is still manual, scattered, and inconsistent.
ZeroCo can help this community by giving them a structured way to adopt AI safely.
Instead of asking a small business owner to understand prompts, agents, workflows, and integrations from scratch, ZeroCo asks simple operating questions and converts the answers into a Digital FTE structure.
The real-world benefits could include:
- Fewer missed leads
- Faster response times
- Better follow-up
- Cleaner customer records
- Less repetitive admin work
- More booked jobs
- Better owner visibility
- Safer AI adoption through approvals and blocked actions
For small businesses, even a small improvement in response time or lead follow-up can create meaningful revenue impact.
Adoption Plan After the Hackathon
After the hackathon, I would encourage adoption by starting with one narrow vertical: home-service businesses.
Instead of trying to serve every company immediately, ZeroCo would focus on a repeatable use case:
Inbound lead to booked job.
The first adoption path would be:
- Interview small business owners about missed leads and follow-up problems.
- Build a focused version for one vertical such as plumbing, cleaning, or appliance repair.
- Connect common tools like WhatsApp, Gmail, Google Calendar, Google Sheets, and a simple CRM.
- Let the owner generate a Digital FTE operating system from their own workflow.
- Start with safe automation only: lead intake, qualification questions, reminders, summaries, and CRM updates.
- Keep high-risk actions behind human approval.
- Track measurable results such as response time, booked jobs, and missed lead reduction.
This adoption path keeps the product practical, measurable, and safer for real businesses.
What I Learned During the Hackathon
While building ZeroCo, I learned that the hardest part of AI automation is not only the model.
The harder part is defining the work.
Before an AI agent can help a business, the system needs to know:
- What outcome matters
- What the agent is allowed to do
- What it must never do
- When it should escalate
- How success will be checked
This changed how I think about AI agents.
A useful agent is not just intelligent.
A useful agent is accountable.
A useful agent is accountable.
That is why ZeroCo includes Result QA as a core Digital FTE. The system should not only do work. It should check whether the work actually improved the result.
What I Would Build Next
The next version of ZeroCo would move from generated operating design to live execution.
The roadmap includes:
- Connecting WhatsApp, Gmail, Google Calendar, and CRM tools
- Generating agent workflows automatically
- Adding a live approval queue for risky actions
- Creating daily owner summaries
- Tracking measurable business outcomes
- Adding Result QA checks for every Digital FTE
- Supporting multiple industries beyond home services
- Creating templates for common operations like sales, support, onboarding, recruiting, and internal operations
The long-term goal is to make it possible for a founder or operator to generate a controlled AI-native company operating system from a business result.
Conclusion
ZeroCo is an early step toward a bigger idea:
Businesses should not just adopt AI tools.
They should be able to generate AI-native operations.
That means defining the result, selecting the right Digital FTEs, setting approval rules, blocking risky actions, connecting tools, and measuring outcomes.
With Amazon Nova and AWS, systems like ZeroCo can move from simple AI demos toward real operating infrastructure for businesses.
The future of AI work is not only about replacing tasks.
It is about designing better companies.
Built by:
Muhammad Hassaan Shaukat
Muhammad Hassaan Shaukat
Mohammed Ayaan Adil Ahmed
#H0Hackathon
#H0Hackathon
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