A proposal for Brand24
Capture decisions. Keep them. Make them findable.
Maciej Jankowski · April 2026
A question
The decision happened. The logic was sound. The answer is in someone's Slack thread,
someone's head, or someone's last day.
The problem
The same questions resurface because the answers live in someone's Slack thread, someone's head, or someone's last day. When people leave, knowledge leaves with them.
This is not a Brand24 problem. It is a company-at-scale problem. Everyone has it. Almost nobody solves it cleanly.
Why traditional solutions fail
| Approach | Why it doesn't stick |
|---|---|
| Confluence / Notion | Second place to look. People default to Slack. Wiki decays within 90 days. |
| "Document everything" | Writing docs is overhead. Nobody writes them under deadline pressure. They go stale. |
| Record meetings | 45-minute recordings nobody rewatches. The decision is in minute 37. |
| AI search | Searching garbage returns garbage faster. The input quality problem is upstream. |
The core issue is not storage. It is capture discipline - making it effortless to log the decision at the moment it's made, and effortless to find it at the moment it's needed.
Three layers
The Decision Card inside Slack. 60-90 seconds. One template. No new tool.
A knowledge graph that links decisions to each other, to people, to topics.
AI retrieval: search, contextual suggestions, onboarding digests.
Layer 1 · Capture
Fields evolved empirically from my personal framework - each field added after a specific mistake made me wish I had that field last time.
Slack command: /decision. Posts to #decisions. Writes to memory layer. 60-90 seconds. Zero new tools.
Decision triage
Bezos's 1-way / 2-way door doctrine. Most teams over-deliberate on reversible decisions and under-deliberate on irreversible ones. The card makes the distinction explicit at the moment of deciding.
One source of truth
Markdown. Browsable in Slack, GitHub, wiki. Humans scroll.
## DEC-047: Switch to Paddle Why: EU VAT handling Confidence: 0.85 Impact: high / 2-way door
JSONL. Queryable by AI, indexable by vector search, exportable. AI retrieves.
{"id":"DEC-047",
"decided":"Switch to Paddle",
"confidence":0.85,
"impact":"high",
"reversibility":"2-way"}
Same source. Two reading modes. Neither is a second-class citizen.
Layer 2 · Memory
Text is searchable. A graph is relatable. Same decisions, but now they connect.
| Node | Edges |
|---|---|
| Decision | supersedes, depends_on, contradicts |
| Person | made_by |
| Team | owns |
| Topic | topic |
What a graph gives you:
Real-world reference: MemPalace (github.com/MemPalace/mempalace) - an open-source semantic graph MCP server. I run a loaded instance with 20,564 entries across typed wings and rooms. Architecture works at real scale with real data.
Layer 2 · Visualization
Killer filter for the CEO: "high impact + 1-way door + confidence below 0.8" - the decisions most likely to hurt the company. That view alone justifies the entire system.
Layer 3 · Retrieval
Not as a chatbot. As a search interface that understands context.
"What did we decide about billing?" → Returns the card, not a Slack thread.
3 seconds vs. 15 minutes
Someone opens a Slack thread about billing → system surfaces "Related: DEC-047, made 2026-04-10."
Prevents re-litigation
New hire → "Here are the 12 active decisions in your domain. 3 up for review this quarter."
Day 1 context, not month 3
Layer 4 · Quality
Three checks run automatically when a Decision Card is filled:
Has this topic been decided? If yes, surface it with alternatives, confidence, review date.
AI generates the strongest counter-argument to the chosen option. Not generic doubt - specific steel-manned objection.
Flags sunk cost, groupthink, planning fallacy, bandwagon, urgency bias patterns in the card text.
Example adversary test: "You chose Paddle for VAT. Stripe Tax launched a flat-rate EU option in March. At your volume, that's $1,500/mo vs Paddle's 5%. Have you priced Stripe Tax at current rates, not the rates from when this was last discussed?"
High-stakes decisions only
For the 5-10 decisions per quarter that shape the trajectory. Pricing changes, market entry, senior hires, tech stack.
The AI catches logical flaws. Human protocols catch the silence - the person who saw the problem but didn't speak.
Implementation
| Week | Action | Outcome |
|---|---|---|
| 1-2 | Build Slack /decision workflow. Deploy to one team. | Capture mechanism live. First 20-30 cards logged. |
| 3-4 | Collect feedback. Adjust fields. Deploy to 2-3 teams. | ~100 cards. First "I found it in the log" moments. |
| 5-8 | Connect AI retrieval. Contextual suggestions in Slack. | Search + suggestions active. Repeat-discussion rate measurable. |
| 9-12 | Build topic graph. Generate onboarding digests. First knowledge audit. | Full system operational. Baseline retention metrics. |
Recommendation: Start minimal (week 1: Google Sheet + Slack bot, one team). Prove the capture habit works. Then build toward the full graph over weeks 5-12 using the data already captured. The graph is only as good as the data in it - and the data comes from the habit, not the technology.
Scope clarification
Wikis are where knowledge goes to die, maintained by the one person who cares until they leave.
Chatbots answer questions about knowledge that's already been captured. This system captures the knowledge in the first place.
It makes decisions visible, searchable, persistent - regardless of who made them or whether they still work here.
The AI is the retrieval layer, not the capture layer. Humans make decisions. The system makes them findable.
Beyond internal use
Brand24 already sells signal detection. This toolkit captures the internal response to those signals - closing the loop from signal to action to memory.
Every tool helps companies find past decisions (Notion, Guru, Confluence). None help companies make better decisions at the moment of deciding. The adversary test, bias scan, PARDES engine, structured dissent - that's the layer nobody has built.
The product is the challenge layer, not the archive.
Turns this project from a cost center into product R&D.
About the methodology
I've been iterating on AI-assisted decision frameworks for my own work since summer 2025. Four major versions so far. The pattern in this proposal is what survived across all of them.
300+ decisions logged across projects
137 in the largest single project log
20,564 MemPalace entries
Zbigniew - cold adversarial analysis
Bozenka - fact-checking
Konrad - buyer recon
+20 others
Management 3.0, US Army Red Team doctrine, Quaker decision-making, sociocracy, PARDES exegesis, special ops after-action review.
The proposal in one sentence
The alternative is: someone asks the same question in 3 months,
nobody remembers, the team re-discusses for 45 minutes,
and reaches the same conclusion.
The card costs 60 seconds. The re-discussion costs 45 min × 4 people.
The math does the enforcement.
Maciej Jankowski
maciej.artur.jankowski@gmail.com