Your AI engineering team builds exactly what you asked for. Nothing more. Nothing less.
AI-powered. Human-approved.
Edge cases nobody anticipated. Integrations nobody designed. Architecture nobody thought through. Now every fix risks breaking three things that were working — and you're afraid to even open the file.
AI doesn't know what to leave alone. It refactors stable code, introduces parallel implementations, and makes changes you never asked for. Every commit becomes a forensic exercise — what else did it touch?
AI optimises for "it works," not for "it's safe." Authentication bypasses, exposed data, misconfigured permissions, things that pass every test until someone actually tries to break in.
No requirements. No design record. No history of what was decided and what was rejected. When it breaks, you can't trace back to the decision that caused it. When you hand it off, nobody can pick it up.
AI makes the first version feel easy. The pain usually starts when you need to change it, fix it, or explain why something broke.
The code compiles. The demo works. But did the AI understand what you actually meant, or did it just produce something that looked right?
Source: Stack Overflow 2025 Developer Survey (49,000+ developers, 177 countries)Faster output without control does not remove the cost. It pushes the cost into review, verification, rollback, and rework.
Source: Google DORA 2025 / 2026 analysis of AI-assisted software developmentThe issues don't always show up when the code is written. They accumulate after. Every rushed build can leave behind debt the next build has to carry.
Source: "Debt Behind the AI Boom," arXiv 2026 academic preprint (302.6K AI-authored commits analysed; 22.7% of issues still surviving)Everyone wants agents. Almost nobody has clear approval gates, audit trails, monitoring, or human decision boundaries.
Source: Deloitte State of AI in the Enterprise 2026 (3,235 leaders, 24 countries)The first version proves AI can write code.
The second version proves whether you have a delivery system.
Test them against your last AI build.
Agents build only what the requirements specify. No extra code you didn't ask for. No silent refactors of working modules. No TODOs left behind for you to clean up.
Agents read your existing codebase before writing anything. They reuse the patterns and modules already there instead of reinventing them. Six months from now, your codebase still looks like one person built it.
Your team digs for multiple contributing root causes, not just the first plausible one. If the same bug pattern affects other modules, your team flags those too — before you discover them yourself three weeks later.
Before any code changes, impact is mapped across affected components. Fixes are regression-tested against acceptance criteria. The goal: stop playing whack-a-mole with your own codebase.
When something breaks, you can trace it back to whether it was a code issue, a design gap, or a missing requirement. Every decision is recorded. Hand your codebase to a contractor next year and they can read the full history.
Human approval gates at every critical stage. Approve from Telegram, the dashboard, wherever you are. You're the decision-maker, not the implementation team.
You don't need to write perfect requirements. Tell your Product Manager what you want in plain English. They read your actual codebase, structure it into a code-aware Product Requirement Document (PRD), and your Senior PM reviews it for gaps. By the time your Build Team starts, the spec is tight.
Your BA writes the stories. Your architect designs the system. Your product owner sequences the sprint. Your developer builds each story. Your QA tests it against acceptance criteria. You approve at three gates along the way. Between those gates, your team handles it.
Most tools find one plausible cause, patch it, and move on. The bug comes back. Your Incident Team — Incident Engineers, System Analysts, developers, QA — digs for multiple contributing root causes before proposing any fix. You approve the diagnosis. Then the plan. Two approval gates.
Every fix becomes a lesson your codebase remembers.
16 specialists across three teams. Every role you saw in the workflows above — the BA who writes your stories, the architect who designs your system, the senior counterparts who challenge their work. They work for you.
Work on your marketing. Answer customer queries. Take a sales call. Live your life. Your AI team keeps working and reaches you only when a human decision is needed.
Approve a design between meetings. Dashboard and Telegram stay in sync.
Release a sprint while travelling. Inline buttons, no context switching.
Reject a bug-fix analysis during a coffee break. The fix loops back with your context.
You're the decision-maker. Not the delivery team.
Yesterday's root cause analysis becomes tomorrow's guardrail. Every bug fix, every design decision, every edge case becomes part of your codebase's permanent record. The next time any agent touches that area, it loads what went wrong before and why.
Your Incident Team investigates, you approve the diagnosis, the fix is applied. The root cause analysis is permanently tied to the code that was changed.
Before writing a single line of code, the agent loads the history of what went wrong here before. It builds around the trap, not into it.
The agent reads 4 prior decisions, 2 RCAs, and 1 architectural constraint before proposing a design. It doesn't just see what the code does. It sees what was tried, what failed, and why the current approach was chosen.
Build 1 teaches Build 5 what not to do.
Every stage produces an artifact. Requirements, design decisions, test results, root cause analyses. When something breaks six months from now, the trail is already there. When you bring on a contractor, they can read exactly how and why every piece of code came to exist.
User story, acceptance criteria, edge cases, captured before any code is written.
Architecture choice, rationale, alternatives considered, recorded and approved by you.
Each story tested against its acceptance criteria. Pass or fail with evidence.
When something breaks: root causes, fix rationale, code locations, tied to the codebase permanently.
3 ways to bring your own model. 0 markup, 0 token resale.
Use Claude, OpenAI, or a local model you already pay for. Your provider key stays on your machine — Fostery never sees it. Switch providers anytime.
Your provider key lives on your machine, not on Fostery's servers. You pay your provider directly at their published rate. Nothing for Fostery to resell, mark up, or get breached.
If your AI account is suspended, rate-limited or re-priced, re-point Fostery at a new key and keep building. The workflow is yours.
Point Fostery at a local model and the entire process runs on your machine. Nothing leaves your environment. Same pipeline, same agents, fully offline.
Your source code goes straight from your machine to your AI provider. Fostery orchestrates the workflow. The code itself never passes through us.
Your AI provider key, your source code, your prompts — none of it passes through Fostery's infrastructure.
Every decision the system takes, which agent ran, what was approved, what artefacts were produced, is recorded. If you need to understand how a piece of code came to exist, the trail is already there.
Prompting for code is fast but unstructured. Your AI engineering team is fast and structured.
| Capability | Prompting for codeAI coding assistants | Your AI engineering teamFostery — 16 specialists |
|---|---|---|
| Time to first version | Under an hour | Over an hour |
| Requirements captured before code | No | Yes, you approve |
| Design recorded before build | No | Yes, you approve |
| Bug investigation depth | First plausible cause, then iterate | All contributing causes upfront |
| Audit trail | Chat history | Per-stage artefacts |
| Memory across builds | Resets each prompt | Root causes and decisions tied to code |
| You stay in the loop | You write every prompt | You approve at every gate |
| Cost model | Subscription plus tokens | Your own model bill, no markup |
Instead of a single AI you prompt, Fostery is a coordinated engineering team that takes your requirements through specification, build, test, and ship — with your approval at every gate that affects shipped code. You make the decisions; your team does the implementation.
No. If you use a cloud model, your code flows directly between your machine and your chosen AI provider. If you use a local model, it stays entirely on your machine. Fostery orchestrates the workflow, but your code does not pass through Fostery's infrastructure.
You bring your own. Fostery works with Claude, OpenAI, or a local model running entirely on your own machine. Your subscription, your tokens, your terms of service. No markup, no middleman. Switch providers anytime.
You report the bug in plain English. Your Incident Team investigates and digs for multiple contributing root causes, not just the first plausible one. You approve the diagnosis. The team then proposes a fix plan, which you approve separately. Only after your sign-off does any code change. The full record stays tied to the code so your team learns from every fix.
Yes. Your Product Team includes a Product Manager and Senior PM agent that read your actual codebase and turn rough requirements into structured, code-aware product requirement documents. The PRD goes through a writer-reviewer loop with multiple rounds of human review. You don't need to write perfect requirements. You define what you want. Your team refines it into something buildable.
Fostery is in closed beta. The process is proven. The platform is new. Sign up below to get in early and help shape what it becomes.
Anyone who wants to direct software delivery instead of doing the implementation themselves. That includes founders, developers, product managers, engineering leads, and agency owners. If you want to operate at the requirements and decision level and let a team handle the rest, Fostery is built for you.
Those tools give you an AI assistant that helps you code. Fostery gives you a coordinated team that produces a spec, debates it, builds against scoped boundaries, and tests against acceptance criteria — with your approval at every gate that touches shipped code. Same underlying AI. Different operating model.
Governed AI means every decision that affects shipped code passes through a human approval gate, every stage produces an artefact, and every artefact is auditable. The AI does the work; the human owns the decisions. Every code change carries a recorded sign-off.
Yes. Point Fostery at a local model running on your own machine and the full process runs without anything leaving your environment. This is the recommended mode for regulated, sovereign, or sensitive work.
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