What is the VOID Loop?
A simple, repeatable framework to go from idea → workflow → learning → next idea.
Why it exists
Most SEO/AI projects fail due to poor framing and lack of documentation. The VOID Loop restores discipline and transparency so every workflow is measurable and extensible.
The 4 Steps
V — Vision
Purpose
Identify what deserves automation. Observe patterns in data, user behavior, or internal processes.
Inputs
Signals, anomalies, pain points, recurring tasks, business questions.
Outputs
A short hypothesis + a problem statement.
Checklist
- Describe the pattern in one sentence
- Capture the source(s) of truth (GSC, SERP, CRM, support logs…)
- Define who benefits (persona/team)
Pitfalls to avoid
- Starting from a tool, not a problem
Open the Vision template
O — Objective
Purpose
Define inputs, outputs, constraints, and success metrics before writing a line of code.
Inputs
Vision brief.
Outputs
Spec sheet (parameters, data contracts, evaluation).
Checklist
- Inputs listed (format, size, freshness)
- Outputs defined (schema, granularity)
- Metrics set (precision/recall, coverage, latency, cost)
- Guardrails (limits, quotas, privacy)
Pitfalls
- Vague outputs
- Measuring vanity, not value
Open PRD template
I — Implementation
Purpose
Prototype, test, and iterate quickly. Build scripts, apps or workflows that meet the spec.
Inputs
PRD + sample data.
Outputs
A runnable module (script/app) with logs.
Checklist
- Deterministic steps documented
- Config via env/params (no hard-coding secrets)
- Minimal tests (I/O sanity, schema checks)
- Observability (timing, errors, usage)
Pitfalls
- Over-engineering the v1
- Skipping tests
- No logs
D — Deep Dive
Purpose
Evaluate, interpret, and document learnings. Turn results into reusable knowledge.
Inputs
Run outputs + metrics.
Outputs
Insights, decisions, next actions.
Checklist
- Compare metrics to target
- Note edge cases and failure modes
- Make a keep/kill/iterate decision
- Update docs/PRD based on findings
Pitfalls
- Stopping at "it runs"
- Not writing anything down
Example Walkthrough
Use case
Seasonal shifts in People Also Ask questions impact content coverage.
Vision
Hypothesis: PAA topics drift seasonally → track & cluster weekly.
Objective
Inputs: seed keywords, locales
Outputs: clusters + drift metric
Metric: coverage% vs. seed intents
Implementation
Python + Playwright scraper; embeddings clustering; weekly job; logs.
Deep Dive
Coverage increased +12%; identified 3 recurring clusters (care, sizing, materials). Next: integrate into editorial planning.
Principles
Clarity over hype
We document before we code.
Small steps, shipped
Prefer modules you can replace.
Observable by design
Logs, metrics, and explicit data contracts.
Ethical & private
Respect data boundaries; minimize collection.
Share the path
Show process, not only results.
Quickstart
Getting Started
- Pick a pattern you face weekly (e.g., clustering, PAA, internal links)
- Write a 1-page Vision → translate into a PRD
- Prototype a minimal module (v0.1) and run on a small sample
- Log metrics → write a Deep Dive note
- Iterate or discard. Repeat the Loop.
FAQ
Pourquoi une méthode ?
Parce que sans cadre, l'automatisation devient une boîte noire fragile. La méthode garantit clarté, mesure, transfert.
Le VOID Loop est-il compatible no-code/low-code ?
Oui. Le cadre s'applique quel que soit l'outil (n8n, Sheets, Python, etc.).
Puis-je utiliser vos modules avec mes données ?
Oui, nos apps priorisent la transparence et les paramètres d'entrée/sortie.
Open-source ?
Beaucoup de bases le sont. Les versions "Studio" ajoutent UX, observabilité et intégrations.