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 captures business and technical intent, compiles it into agent-executable work orders, orchestrates AI coding agents (like Cursor and Claude Code) over MCP, governs every change with 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, an architecture blueprint, and phased work orders; dispatches those work orders to AI coding agents; enforces approval gates, spec-drift detection, and release-confidence scoring; and keeps every artifact traceable to production code 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, SSO/SAML, and RBAC.
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.
Skaftor is $99 per seat per month for the Pro plan, with custom Enterprise pricing for larger organizations that need advanced governance, SSO/SAML, and self-hosting.
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 enforces approval gates on every transition, runs blast-radius analysis to show what a change touches, detects spec drift when an implementation diverges from its requirements, and scores release confidence before deploy — 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, architecture blueprints, 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 intent into agent-executable work orders carrying full execution context, dispatches them to agents like Cursor, Claude Code, and Codex 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.