NebulaDesk Agentic Workspace
50% faster from idea to approved product spec.
The challenge
A scaling product organisation with four product squads was losing two to three weeks on every product initiative to an unstructured spec process: ideas arrived as Slack messages, evolved through document ping-pong, and finally landed in Jira only after expensive stakeholder alignment meetings. The product and engineering relationship was fraying — engineers complained about under-specified tickets, product managers complained about scope creep after alignment. They needed a structured, AI-augmented ritual that could take a rough idea through to an approved spec without fragmenting the process across four different tools.
Architecture
NebulaDesk is a collaborative workspace where AI copilots and human contributors work on the same canvas. The core architecture is a Next.js application with real-time collaboration via Supabase Realtime. Each product initiative has a structured lifecycle: Idea → Research → Spec → Review → Approved. At each stage transition, an agent is triggered. The Research agent (LangChain + web retrieval) surfaces competitive context and relevant internal data. The Spec agent (GPT-4 with a custom system prompt built from the team's past approved specs) drafts the initial specification. The Review agent summarises open questions and surfaces misalignments between the spec and the stated business objective. Figma API integration means that when a spec reaches the Design stage, the relevant Figma file is automatically linked and component annotations are pulled into the spec document.
How we shipped it
We ran a four-week co-design phase with two of the four product squads — mapping their current process, identifying the highest-friction transitions, and building the agent prompts from their existing approved specs corpus (42 specs across 18 months). The first version of the Research agent went live in week five. We iterated on it weekly based on squad feedback, measuring perceived usefulness after each iteration. The Spec agent launched in week eight after we resolved a key problem: early drafts were too generic because the agent was not grounding its output in the team's specific domain vocabulary. Retraining the system prompt with 15 curated example specs fixed this.
Results
After 90 days across all four squads: average time from idea to approved spec dropped from 18 days to 9 days. Engineering-reported spec quality scores (measured in a monthly survey) improved from 5.8/10 to 8.1/10. Stakeholder alignment meetings dropped from an average of 3.2 per initiative to 1.1. The contextual governance layer — which flags specs that contradict existing product policies — caught 11 policy conflicts before they reached engineering in the first 90 days.
What we would do differently
The most important design decision was keeping humans in the approval loop at every stage transition. Squads that tried to skip the Review stage (because the Spec agent output looked good) consistently produced specs that failed in engineering. The agent corpus quality matters enormously: the first version of the Spec agent trained on all available specs performed worse than the second version trained on only the 15 highest-quality ones.
Written by Mudassir Khan
Agentic AI Consultant & AI Systems Architect · CEO of Cube A Cloud · Islamabad, Pakistan
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