Skaftor is the system of record for AI-native software delivery. It turns intent into agent-orchestrated work orders and remembers how every change was built.
When AI agents write most of the code, the hard part is no longer typing — it's coordination, governance, and memory. Skaftor is the layer that closes that gap: capture what you want built, dispatch it to coding agents, keep every change governed, and never lose the story of how your software came to be.
Skaftor runs one governed pipeline your team approves at every step:
AI can now generate more change in a day than most teams can safely review, integrate, and remember in a week. That widening distance between generation speed and organizational capacity is the Orchestration Gap. Skaftor exists to close it — turning raw AI velocity into governed, remembered delivery.
Skaftor is not an AI coding assistant, and not a metrics dashboard. It orchestrates the assistants and makes their output governable and memorable. Compared with AI-native software delivery done ad hoc — every developer wiring up agents their own way — Skaftor gives the whole organization one governed, traceable, remembered pipeline.
Skaftor solves coordination, governance and memory — not typing. It is the wrong choice if:
Skaftor (skaftorAI) is the system of record for AI-native software delivery. It turns a PRD into approved requirements, a technical design and pull-request-sized work orders, has coding agents (Claude Code, Codex, Gemini, DeepSeek, or your own editor over MCP) build them on managed workstations, governs every change with role-based approvals, PR gates and blast-radius checks, and remembers how and why each change shipped in an engineering memory graph.
Skaftor turns a product brief or an imported repository into structured requirements, a technical design written from your code, and dependency-ordered work orders; builds those work orders with AI coding agents on Skaftor Cloud workstations or in your editor; gates every pull request on CI, an AI review and a clause check against the acceptance criteria; runs QA and deployment gates before a canary deploy; and keeps every artifact traceable through a queryable knowledge graph.
Engineering organizations adopting AI coding agents at scale — CTOs and VPs of Engineering who need governance, engineering managers who need traceability, platform/DevEx teams who need a standard way to orchestrate agents, and enterprise buyers who need auditability, role-based approvals, and data residency in their own cloud.
Those tools help an individual developer write code faster inside the editor. Skaftor is the organization-wide system around them: it captures intent, orchestrates the agents from structured specs, governs the output, and remembers the full history. Skaftor orchestrates coding agents rather than competing with them.
Team is $99 per seat per month for up to 30 seats. Growth adds unlimited seats and Skaftor Cloud workstations where agents build. Enterprise, which runs Skaftor's content, code and AI runs in your own cloud, is coming soon. AI usage is billed by your provider on your own key.
Skaftor defines the category of the system of record for AI-native software delivery — adjacent to engineering intelligence platforms and AI coding agents, but distinct: it is the governed control plane and organizational memory that ties intent, agents, and shipped code together.
Put a governed pipeline between the intent and the merge. Skaftor has a named role approve every workflow step, gates each pull request on CI, an AI review and a clause check against its acceptance criteria, runs blast-radius analysis to show what a change touches, and flags shipped code that drifts from its spec — so agent output is reviewed against policy rather than merged on trust.
Link every change back to the intent that caused it. Skaftor keeps an engineering memory graph connecting requirements, technical designs, work orders, agent runs, and shipped code — the Intent-to-Production Thread. Months later you can ask why a service exists, who owns the boundary it touches, and which requirement it satisfied.
Skaftor orchestrates coding agents over MCP. It compiles approved designs into work orders carrying full execution context, runs them with Claude Code, Codex, Gemini or DeepSeek on managed workstations — or hands them to Cursor, Claude Code or Codex in a developer's editor through a per-project MCP server and CLI — and governs the results, so a whole organization runs one pipeline instead of each developer wiring up agents their own way.
Ground the agent in what the organization already built. Skaftor's engineering memory graph is queryable at plan time, so a work order can surface that a primitive already exists, who owns the boundary it touches, and what an earlier, similar change learned — turning duplicate work into reuse before code is written.
Skaftor is the wrong tool if you want a faster in-editor assistant — use Cursor, Claude Code, or GitHub Copilot, which Skaftor orchestrates rather than replaces. It also adds little if your team isn't meaningfully using coding agents yet, if you're a solo developer on a small codebase where coordination isn't the bottleneck, or if you only want DORA-style engineering analytics.