CogniDev Workbench · The Cognitive IDE
Every AI editor waits for your next prompt. The Workbench works the other way around: open a repository and it already understands the whole system — then it tells you what the code needs, plans it, builds it task by task, and proves the result. You approve the moves; it does the work. Take the wheel whenever you want.
Runs on your machine. Works with any model. Shared across your team.
01 · The problem
Four things every team on an AI editor is hitting right now — and what changes when the IDE understands the system before it touches it.
The model sees a few files per prompt, never the whole system. Layers blur, the same thing gets built twice, and it breaks above the file you were looking at.
A structural model of the entire codebase — layers, flows, use cases, state — built before a single line changes.
Every prompt re-reads the repository. Every developer pays for the same understanding, over and over. Nobody can see where the spend went.
Analysis is cached and shared across connected repos. The second repo is cheaper than the first, and every call is metered in plain sight.
A prompt has no opinion about order. It will pick a database before the features exist. That is how a demo becomes an unmaintainable app.
Understand, propose, plan, approve, execute, prove — the same gates on every job, whether you are building, migrating, or governing.
When it is done, you get a chat transcript — nothing a reviewer, a tech lead, or an auditor can actually accept.
An approved plan, one git commit per verified task, a verification log, and a final report. On your machine — your code never leaves it.
02 · How it works
Other editors wait for instructions. The Workbench reads the repository and leads. Anything a step can derive from the code, it never asks you.
It parses the code natively and builds a structural model — layers, flows, use cases, routes, state.
It explains the system in plain language, mapped to the real files. Legacy included — COBOL to Next.js.
It proposes the work the code actually needs — modernize the stack, migrate the framework, add a feature, close governance gaps — from evidence in your codebase, not guesses.
It writes the full file-level plan. You approve it before any code changes.
Task by task. Each task compiles and typechecks before it lands. One git commit per green task.
A report of files, commits, tasks, and verifications. Stop any day, resume any day.
The IDE does the driving and you stay in the approval seat — or take the wheel any time. The built-in assistant answers from your codebase's real structure, not guesses.
03 · Features
Your codebase parsed into layers, flows, use cases, routes, and state — persisted with your repo and versioned. Same code, same analysis, every time.
Migrations, modernizations, new products, features, governance — each a versioned process with approval gates, not a prompt.
Analysis from one machine is shared to connected repos. A teammate opens the repo and starts from what the team already knows.
Connected repos see a change in an API, schema, or contract they depend on — before it breaks a build.
A step sends the model only what it needs. Every call is metered — tokens and cost, visible per step.
Whatever structural intelligence can answer costs nothing. Model output is cached, so re-runs are free.
An approved plan, one commit per verified task, and a final report of files, commits, and checks.
One binary. No account, no telemetry, no cloud index. Works offline, with any model — including local.
The IDE comes first. The same playbooks run as a CLI, in CI, or as an agent skill — org-wide governance and dashboards from the same engine.
04 · Beyond the editor
Inside the IDE, playbooks are simply there. Outside it, the same playbooks turn recurring work — governance, compliance, audits — into something you schedule and trust. The IDE is the first surface, not the only one.
Run one governance playbook across every repository — slop, hallucinated APIs, layering, secrets, dependency risk. Read-only: it reports, never edits.
The same scan, as a check in CI. A finding over your threshold fails the build — with the evidence attached. Pass or fail, not opinion.
Every run commits its report to a dashboard repository. The git history is the audit trail. Nothing to host, nothing to breach.
One playbook, one command, every surface. Engineers drive it in the IDE; pipelines run it headless; agents call it as a skill.
A run that changes code always lands on a branch as a pull request — reviewed, never auto-merged.
05 · Compare
| CogniDev Workbench | AI editors Cursor · Copilot · Windsurf | |
|---|---|---|
| Who drives | It proposes and drives — take over any time | You, prompt by prompt |
| Codebase understanding | Persistent structural model — on disk, diffable | Retrieval per prompt, discarded after |
| Cost of understanding | Computed once · cached · shared across the team | Re-paid on every prompt, by every developer |
| Token spend | Metered per call, visible in the IDE | Opaque |
| New feature | Spec → architecture → approved plan → tasks | Prompt → diff |
| Legacy migration | Resumable runs, per-block verification | Out of scope |
| What the reviewer gets | Plan, per-task commits, verification log, report | A chat transcript |
| Your code at rest | Your machine · offline · any model, including local | Their cloud index |
| Tab autocomplete | Structure-aware — completions drawn from the real architecture | Pattern-based, from nearby text |
| Extensions | Imports your VS Code extensions — and authors new ones as AI playbooks | Thousands in the marketplace |
You can drive, or be driven. Either way the work is governed, shared, and proven.
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Tell us your stack and team size and we’ll get you set up. Desktop builds are rolling out — we’ll make sure you’re first in line.
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