📊 vs · data-platform × aider

Aider vs GreatCTO for data platform

Short answer: both. Aider fast file-level edits via CLI, git-aware. GreatCTO orchestrates the SDLC around it — gates, parallel reviewers, archetype-specific compliance. Same plugin works inside Aider.

$ npx great-cto init 📊 data-platform archetype →
What each does well

Different layers of the same problem.

Aider

  • â–¸fast file-level edits via CLI, git-aware
  • â–¸Category: terminal-native AI pair programmer
  • â–¸Where it stops: at the code. Doesn't enforce gates, doesn't run specialist reviewers, doesn't carry memory across sessions.

GreatCTO on top of Aider

  • ✓34 specialist agents (architect → pm → senior-dev pool → reviewers → devops → l3-support)
  • ✓Auto-detects data-platform archetype → wires the right compliance gates
  • ✓Two human gates per feature (plan, ship) — everything in between runs unattended
  • ✓Memory layer: lessons + decisions persist across sessions and projects
  • ✓Aider edits files. GreatCTO ships the same agentic SDLC layer into Aider — your edits go through gate:plan + gate:ship just like in Claude Code / Cursor.
Architecture · data-platform

What gets wired automatically when GreatCTO detects data-platform.

Run npx great-cto init in your data-platform project. GreatCTO scans manifests, picks the archetype, attaches the right reviewer agents and compliance gates. You don't write the gates; you override them if your specifics differ.

STAGE 1 · PLAN

architect

Drafts ARCH.md + ADR + cost estimate. You approve scope at gate:plan. No implementation starts before your approval.

STAGE 3 · IMPLEMENT

senior-dev pool (parallel)

Aider does the editing. GreatCTO orchestrates which agents claim which tasks (from the PM decomposition), runs them in isolated worktrees, and feeds the diff to reviewers.

STAGE 5 · REVIEW

5 reviewers in parallel

qa-engineer · security-officer · performance-engineer · data-platform-reviewer · code-reviewer. Verdicts aggregate to a single APPROVED / BLOCKED chip at gate:ship.

STAGE 7 · OPERATE

l3-support + memory loop

P0 incidents extract a lesson. Pattern hash + detection order written to .great_cto/lessons.md. Next iteration's agents read this in Step 0.

Full state machine with every node clickable to its agent on GitHub: /architecture.

When to pick which

Decision tree.

Aider alone is enough if

  • You're prototyping; production isn't in scope.
  • The codebase is small enough that one human can review everything end-to-end.
  • No regulated data flows (no PCI, no PHI, no EU AI Act high-risk).
  • You don't need cross-project memory of past incidents.

Add GreatCTO if

  • You ship in a regulated industry (fintech, healthcare, voice-AI, gov, …).
  • Reviews are the bottleneck — you want 5 specialist reviewers in parallel instead of one human + one model.
  • You want explicit gates and an audit trail (SOX, SOC 2, EU AI Act post-market monitoring).
  • You want to compound lessons across features and projects.
Receipts

Don't take my word for it.

01 · ARCHITECTURE

Live state machine

Every box on the diagram is a clickable link to the agent's source on GitHub.
02 · PROOF

One real run, full timeline

Voice-AI pack rollout, 14 timeline steps, ~$3.40 LLM cost, 47 e2e assertions, public artifacts.
03 · METHODOLOGY

94 % MTTR claim, audited

47 paired P0 incidents · 4 memory-miss cases documented · raw data under NDA.
Install

Works in Aider today.

$ npx great-cto init
✓ scanning manifests…
✓ archetype: data-platform
✓ adapting for: Aider
✓ 34 agents ready

Free, MIT, runs locally. You pay your own LLM API. No SaaS dashboard, no telemetry by default.