Requirements stay vague
Ideas are discussed in chats and calls, but never become concrete user stories and acceptance criteria. AI agents receive blurry context and produce blurry output.
An AI agent at every stage of delivery, from idea to acceptance.
Spexus helps turn a raw idea into requirements, select the architecture, pass context to AI coding agents, and automatically verify the result against acceptance criteria.
AI can code. Product delivery is more than code.
Architecture is chosen by habit
Without systematic requirements analysis, teams make architectural decisions based on habit instead of product needs.
Code gets written, but not accepted against the real goal
Tests prove that code runs, but not that it solves the business problem. Acceptance criteria live separately from the implementation.
Spexus is an AI copilot at every stage
Requirements
Turn a raw idea into concrete epics, user stories, and acceptance criteria in dialogue with AI. Structured, testable, and aligned with EARS.
Architecture
Spexus helps you choose an architecture that fits product requirements instead of team habits. Decisions are captured in steering documents.
Coding
The built-in MCP server gives AI agents the full context: requirements, architecture, and standards. AI writes code from specifications instead of prompt retellings.
Testing
AI agents verify the implementation against acceptance criteria on their own. Acceptance stops being a manual ritual and becomes automated verification.
We build Spexus with Spexus
We are the team that went through the full journey from chaotic prompting to systematic AI-assisted delivery. We tried keeping requirements in text files, architecture in Miro, and then manually retelling all of it to AI agents. It did not work: context was lost at every stage. So we built Spexus, a platform where AI helps from idea refinement to code acceptance. We use it every day to build Spexus itself.
Built-in MCP server with native integration for Claude, Cursor, and Gemini
AI assistant for refining requirements and architecture directly inside the platform
Automated code verification against acceptance criteria
The product is used in production to build Spexus itself
From idea to shipped product in three steps
1
Refine the idea
Describe a raw idea and AI will help turn it into structured epics, user stories, and acceptance criteria aligned with EARS. Capture architectural decisions in steering documents.
2
Connect AI agents to delivery
The MCP server passes the full context into your tools: Claude Code, Cursor, and Gemini. AI codes from specifications instead of assumptions.
3
Verify the result automatically
AI agents validate the code against acceptance criteria. You see what is done, what passed, and where the gaps are.
How it works
Spexus covers the full delivery cycle from a raw idea to accepted code. At the requirements stage, an AI assistant helps turn the idea into structured epics, user stories, and EARS-aligned acceptance criteria. At the design stage, it helps choose an architecture that matches concrete requirements and records those decisions in steering documents. At the coding stage, the built-in MCP server passes the full context to AI agents automatically: requirements, standards, and constraints. At the testing stage, AI agents verify the implementation against the acceptance criteria. The whole team works in one shared space, and AI becomes a full participant at every stage.
See Spexus in action
Video walkthrough
- 0:00-0:40 - Requirements: raw idea -> dialogue with AI -> finished user stories and acceptance criteria
- 0:40-1:10 - Architecture: AI analyzes requirements, proposes an architecture, and decisions are recorded
- 1:10-2:00 - Coding: an AI agent gets context through MCP and generates code from specifications
- 2:00-2:40 - Testing: AI checks code against acceptance criteria and produces a compliance report
- 2:40-3:00 - CTA: Request a demo
See how Spexus works for your team
Leave a request and we will show the platform using your product context