01 · Assemble
The AI factory for prompt to production SaaS
Public Projects
Explore All →
trackitIQ
Gizingle Singhcreate a saas to track daily expenses
U-KRATER
An obstacles on the streets warning map alert where people share their findings so that other drivers do not run over the same obstacle on the street, the UI will be a MAP where the user will see the location of the places where the obstacle is found
mithuinvoice
Muhammed ShaduliInvoice follow-up / accounts receivable nudges Boring, narrow, and easy to sell to service businesses and bookkeepers. Repeatedly listed as a money-making niche because it directly speeds up cash collection. build me this
Platform track record
4,800+projects built on the platform
2,100+public projects with completed designs
Kubernetesreal staging and production deploys
Proof you can click — open any public project's designs, requirements, and architecture in the gallery.
Made for you
One platform. Five ways in.
Pick who you are — tap a tile to jump to the feature.
Platform
Everything you need to ship
production-grade software.
02 · Design
System Architect Agent. AI that designs clean, scalable architecture before writing a single line of code.
03 · Plan
Project Manager Agent. Built-in task decomposition, sprint tracking, and intelligent prioritization.
04 · Ship
Microservice Kubernetes Deployment Agent. Ship to production-grade infrastructure in minutes, not months.
05 · Verify
Visual Testing & Replay. AI-driven testing that sees your app like a real user does.
06 · Control
Integrated Code Editor. Full VSCode editor in the browser — supports every language and framework.
07 · Scale
Auto-Scaling Infrastructure. Your projects scale horizontally without manual intervention.
The process
Not a black box. A process.
Seven steps from one sentence to a running product — agents do the work, you hold the gates.
- 01
Share your idea
You describeType what you want to build. A project is created instantly, and a supervisor agent decides which specialists to bring in — requirements, design, user flows, architecture, tasks.
- 02
Requirements, written down
You approveThe System Requirements Agent writes your requirements document — the source of truth everything else derives from. Every later change arrives as a diff you accept or reject.
- 03
User flows, mapped
You approveThe User Flow Planner turns requirements into screen-by-screen journeys for every user persona — and the pipeline pauses until you approve them.
- 04
Designs you can click
You approveYour home page is designed first and sets the style. Approve it, and the remaining pages generate in parallel — planned, coded, screenshotted, and checked by an AI design critic.
- 05
Blueprint and task plan
You approveThe System Architect draws five architecture diagrams; the Project Manager breaks the build into tasks, sprints and phases. Review it all in a guided walkthrough, then press Start Building.
- 06
AI engineers, in parallel
Agents buildEach task runs in its own isolated cloud workspace through a six-phase pipeline — setup, environment, branch, code, tests, staging — and merges into your repo by pull request.
- 07
Your app, live on a URL
You open itEvery finished task auto-deploys to your project’s staging environment on Kubernetes. Open the live URL on any device, or press Deploy for the latest build.
The same process runs behind every project in the gallery — open one and read its requirements, designs and task board.
Collaboration
Humans and AI. One team.
Set up your org, share the board, and hand work back and forth.
Your org, one workspace
Invite your team into an organization — owners, admins and members share every project, seeing the same designs, requirements, tasks and architecture in real time.
Kanban handoff, human ↔ AI
Work moves across one board whether an agent or a teammate owns it. Assign any task to a human — a decision, a review, an API key — and agents pick the thread back up the moment it lands. List, sprint, Gantt and phase views included.
Humans steer, agents ship
Review gates keep people in control: approve requirements, designs and plans, then agents carry the approved work through code, tests and deployment.
The moat
AI pays a quadratic tax. We removed it.
Flat-cost intelligence for software engineering — built on the models the world already has.
To produce each new word, today's models re-read everything they have read so far: 2× the reading means 4× the cost, 10× the reading means 100× the cost. Nowhere does that tax bite harder than coding — so today's assistants read fragments, forget the rest, and guess.
1M+ tokenswhat a real enterprise codebase measures — far beyond what quadratic models can afford to read
×100 costthe quadratic penalty for reading 10× more code — before a single line is written
2.15× fasterour flat-cost models on long code — and the gap widens as codebases grow
The fix
Flat cost — on models that already exist.
Routed attention: each new word reads a small, relevant slice of memory instead of everything.
Read what matters, not everything
Our routing layer sends each new word to the few places in memory that matter. Work per word stops growing with document size: O(n²) becomes O(n).
Retrofit, not rebuild
We convert leading open models in days — no from-scratch pretraining, no new chips. Every new model release makes us better the week it ships.
Proven on a real model
A 9B open model, converted: flat generation speed at every context we tested — 2.15× faster at 32k, and the gap widens with length.
The multipliers
Three 10× levers. One platform. They multiply.
Orders-of-magnitude better unit economics for AI that writes software.
Context economics
Routed attention makes reading a whole repository affordable — the cost of context stops growing with the square of its size.
Thinking speed
We train models to run ten independent lines of thought inside a single pass, then merge the best into the answer. Reasoning that took ten passes takes one.
Cost per token
A small, cheap model writes most of the code. We read its confidence word by word; the moment it wavers, a large model steps in, fixes, and hands back.
Incumbents answer exploding context demand with GPU capex. Our answer is math — it ships as software, at software margins, and every new open model upgrades us in days. The ecosystem's R&D compounds into our moat.
Trusted
Trusted by leading brands.
Fortune 500s, governments and startups trust the team behind 8080.AI.
The team
Built by developers from
Experience
10+ years of experience in AI.
One of the first AI labs.
8080.AI is built by fxis.ai — F(x) Data Labs, Inc, founded in 2016 — an AI lab shipping data science and machine learning systems long before the AI wave. Every claim on this page is public and verifiable.
10+Years in AI
2016Incorporated, on public record
4.9/5Clutch rating · 65 reviews
Reviews
Rated 4.9 out of 5 by clients.
Independently verified on Clutch.
“They are truly an incredible company.”
Improved platform usability and AI model accuracy, with exceptional project management and on-time delivery.
“Ability to execute at a high standard on genuinely complex problems.”
Refactored our LLM architecture and shipped new features with clear progress tracking and patient technical explanations.
“They bridge business needs with technical execution.”
Delivered a 30–40% reduction in manual tasks and a 20–25% improvement in lead engagement.
“They understood both business and technology.”
Built a voice AI system that handles customer calls after hours, cutting missed leads and routine phone time.
“Delivered insights reshaping how we plan campaigns.”
An AI-powered marketing suite that improved campaign response rates by 55% and lead conversion speed by 30%.
“Built AI tools that felt practical, not technical.”
A recruitment chatbot that reduced manual screening workload by over 60% with real-time candidate conversations.