FRONTEND · BACKEND · DB · AUTH
Complete Codebase
Frontend, backend, database layer, API routes, auth — all wired together and working
8080-ai-platform
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Guneet Bajaj
Guneet BajajCopy and clone 8080.ai platform and provide system design and development and same output.
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F(X) DATA LABS · AGENTIC AI FACTORY
How the factory works
This page walks through the agentic workflow — how a described idea moves through planning, agent execution, human approval gates, architecture, quality, and deployment until it is a shipped product.
Platform scaleLive
- SCREENS GENERATED
- 0
- TOKENS PROCESSED
- 0.000T
- COUNTRIES SERVED
- 0
Workflow / four steps
Describe Plan Build Ship
This is the workflow the factory runs on every project, from the first description to a production deployment.
Step 01: Describe
You bring the idea, the constraints and the scope. No spec writing required.
Step 02: Plan
AI agents decompose the idea into architecture, tasks and milestones — approved before code is written.
Step 03: Build
Parallel multi-agent execution across frontend, backend, infrastructure and tests, one PR per task.
Step 04: Ship
Dockerized, tested, documented and deployed to Kubernetes — production-ready output.
Multi-agent system
Three agents, three branches — merged by pull request
The multi-agent system executes in parallel across three branches, carrying each task from architecture to code to deployment. Work converges as a reviewed pull request rather than a single opaque output.
Each task runs on its own branch. Parallel lanes share one merge point.
- Tech LeadSequences work, opens the branch, reviews the merge
- ArchitectRequirements, microservices, schemas, API contracts
- QAVisual tests and continuous checks before staging
Approval gates · task flow
Human control at every gate
Tasks move through five stages, from To Do to Done. Your decisions sit at the gates along the way, and rejecting a task returns it to its prior state with your feedback.
- 01To Do
To Do
A task is queued with its requirements and acceptance criteria before any agent picks it up.
Gate: human scopes - 02AI In Progress
AI In Progress
Agents implement the task on their own branches, opened as pull requests against the project.
Gate: agent execution - 03Human In Progress
Human In Progress
A person reviews the work directly, requests changes, or hands the task back into execution.
Gate: human intervention - 04Review
Review
Approval gates and security scans run before anything can reach staging deployment.
Gate: approval + scan - 05Done
Done
The pull request merges and the task closes with its documentation and tests in place.
Gate: merged
Rejection loop
Rejection returns a task to its prior state with feedback, so the next agent pass works from your review notes — nothing advances past a gate without a human decision.
Architecture-first
Architecture
before code
The System Architect Agent designs scalable architecture automatically before code is written — the system is drawn, named and agreed while it is still a plan.
- Artifact 01: System requirements documentScope, constraints and acceptance criteria agreed before a line of code exists.
- Artifact 02: Microservice designService boundaries and responsibilities mapped as separately deployable units.
- Artifact 03: Database schemasTables, relations and indexes specified ahead of the first migration.
- Artifact 04: API contractsRequest and response shapes that frontend, backend and agents build against.
- Artifact 05: Evolving component diagramsThe system map redrawn as services and dependencies change through the build.
Approval gateThe architecture set is reviewed as one unit before agents begin to build.Waiting on human review
Quality & delivery
Test, scan, and ship
Visual testing, continuous QA, security scans, and Kubernetes deployment all run against the project itself — every stage of delivery stays inside the same workspace.
Test
Visual testing, continuous QA, plus unit and integration tests run against the project.
- Visual testing
- Continuous QA
- Unit tests
- Integration tests
Scan
A security scan runs per task and again before the staging deployment.
- Per task
- Pre-staging
Stage
The build deploys to Kubernetes staging and is verified before it moves on.
- Kubernetes staging
- Verify
Ship
Approved work deploys to production on the platform-managed Kubernetes backend.
- Production deploy
Delivery facts
- Runtime Managed backend on Kubernetes, auto-scaling
- Namespace Own Kubernetes namespace per project
- Isolation Isolated workspaces
- Artifacts Dockerfiles, docker-compose, Helm charts, health checks
- CI/CD GitHub Actions: build, test, lint, deploy
Fail loudly: scan or deployment failures are surfaced with the failing step and are retryable — nothing is reported as shipped until it is.
Continue
Start building
The factory is reachable through a free account. Create one to open your workspace, or log in if you already have access.
F(X) DATA LABS · AGENTIC AI FACTORY
A team of AI engineers that build, test, document, and debug production-grade software — so you ship what matters.
Platform scale capabilities
Three agents · three branches
Three agents, three branches — merged by pull request
Each task fans out across parallel agent branches. Every branch lands as a pull request, so the merge point stays a human-readable gate rather than a hidden hand-off.
Agentic flow · approval gates
Human control at every gate
Work moves through five observable stages — from a queued task to a merged pull request — and nothing crosses a gate without a human decision.
To Do
A task is scoped and queued before any agent branch opens.
AI In Progress
An agent works its own branch — architecture, code or QA in parallel.
Human In Progress
You step in at the approval gate to direct, adjust or take over.
Review
The branch is opened as a pull request, scanned, and readied for merge.
Done
Merged to the project and carried forward in persistent project memory.
- To Do
- AI In Progress
- Human In Progress
- Review
- Done
Swipe to trace the flow →
Branch per task · Security scan per task · Merged by pull request
Output · Every task
What ships.
05 deliverables
UNIT + INTEGRATION
Test Suite
Unit and integration tests with 80%+ coverage
CONTAINERS · HELM
Docker & Kubernetes artifacts
Dockerfiles, docker-compose, Helm charts, health checks
API DOCS · README · ARCHITECTURE
Documentation
API docs, README, architecture overview
GITHUB ACTIONS
CI/CD Pipeline
GitHub Actions workflows for build, test, lint, deploy
Trust
Trust posture
Certifications & compliance05
- ISO 9001
- ISO 27001
- SOC 2
- GDPR-ready
- HIPAA-ready
Platform scale · recorded by 8080-ai-platform
Screens generated
Tokens processed
Countries served
Values as published by the platform
Freemium.Flat-cost models.
One price statement. No credit tables, no per-seat arithmetic.
PRICE / MONTHSTART
starting at$4.99 /month(flat-cost models)
Sign up for freeFlat-cost models · Freemium
COMPANY
Built by F(x) Data Labs, Inc, founded in 2016; 10+ years in AI.
Field notes
Reviews
Unavailable in source
Reviews
Reviews are not available yet.
Section · Contact
Contact
This page carries no form, inbox or phone line. Every conversation with 8080-ai-platform begins at one of the two account entry points opposite.
F(X) DATA LABS · AGENTIC AI FACTORY
Signing up is the only step required before your first build.
Audience index
Who it’s for
Built for the people shipping software, not watching a demo — three audiences, one workflow of branches, review and approval.
- Developers
Teams who want the scaffolding, wiring and tests written for them — then review each branch as a pull request and keep control of the codebase.
- Founders
Founders who need production-grade applications without assembling an engineering department first, and who want to watch the work take shape.
- Operations leads
Leads who need the build observable end to end — approving at human checkpoints, reviewing before staging, and seeing what the agents are doing right now.
Agentic AI factory · Capabilities
Features
Agentic AI factory to build a Website, SaaS App, Chat, or Loop.
Full VS Code in the browser, or the 8080 desktop extension.
Three agents, three branches — merged by pull request.
Branch-per-task with PR merging.
Security scan per task.
Managed backend on Kubernetes.
Flat-cost models.
A dedicated AI Engineer agent for wiring model endpoints, APIs and data stores.
Your own Kubernetes namespace per project.
Multi-model gateway.
Isolated workspaces.
Auto-scaling infrastructure.
Architecture-first: system requirements document, microservice design, database schemas, API contracts and evolving component diagrams.
Persistent project memory across sessions.
Workflow · 04 stages
How it works
One continuous line from your idea to running software — architecture first, agents in parallel, human review at the gates.Describe → Plan → Build → Ship
- 01You
Describe
State the idea in plain language — the website, SaaS app, chat or loop you want built.
- 02AI agents
Plan
Agents decompose the idea into architecture, tasks and milestones before any code is written.
- Architecture
- Microservices
- API contracts
- 03AI agents
Build
Parallel multi-agent execution across frontend, backend, infrastructure and tests.
- Frontend
- Backend
- Infra
- Tests
- 04Deployment
Ship
Dockerized, tested and documented — a production-ready deployment.
- Docker
- Kubernetes
Human-in-the-loop
Humans + AI
Approval gates and human review controls preserve human control over agent work. The named AI agents act on behalf of the Platform User and surface their work for human review.
ControlHuman approval gates
Agent roster
- Tech Lead
- System Architect
- Project Manager
- Frontend
- Backend
- DevOps
- Designer
- QA
F(X) DATA LABS · Operating principles
Why 8080
Four rules govern how the agents plan, build and hand over software — in that order, every time.
- 01POSITION
Opinionated, Not Rigid
- 02OBSERVE
Observable by Default
- 03SIGNAL
Fail Loudly
- 04ARCHITECT
Human-First Architecture
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