Structured problem understanding
Phase 1 maps the system behind your problem — causes, dependencies, unknowns, and assumptions — before any solution is on the table. Clear thinking starts here.
How it works
Describe what you're seeing. Phase 1 builds the structure automatically — grouping evidence into clusters, identifying causal relationships, and surfacing what you still don't know.
1
State what you observe, what you want instead, and the boundaries you're working within.
2
The system groups your observations into clusters and maps causal relationships between them.
3
Answer targeted questions to validate hypotheses, fill gaps, and sharpen weak links in the model.
4
When structural readiness is met and you feel confident in the map, close Phase 1 and move forward.
Under the hood
You add what you observe. The system finds the shape. Every causal claim is traceable back to specific evidence — the model never invents structure.
Step 1
You describe what you're seeing — facts, events, measurements, and reported beliefs. Each observation is typed and embedded for grouping.
Step 2
Related observations are grouped into clusters — each representing one underlying phenomenon. Cluster identity is stable: it's determined by which observations belong to it, not by its label.
Step 3
The system ranks candidate causal pairs and classifies each one. Confidence is computed deterministically from the evidence types behind each link.
What you get
Every output is evidence-backed and confidence-rated. Nothing is invented — if it's in the model, there's a chain of observations behind it.
Observations are grouped into clusters that describe the same underlying phenomenon, with relationships mapped between them.
Each relationship carries a confidence score derived from the types of evidence behind it — facts carry more weight than beliefs.
Gaps in the causal map are surfaced explicitly — you know what you don't know, and the system asks the questions most likely to resolve them.
Assumptions that heavily influence the model are flagged for validation before they silently shape every downstream decision.
One question at a time, ranked by expected information gain — weak relationships, causal gaps, and key assumptions first.
A clear signal when the model has enough coverage to support moving forward — not just when it feels intuitively "done."
Use cases
Any problem where you're not sure you're looking at the right thing — where the symptom is obvious but the system behind it isn't.
Engineering · Featured
Model surfaced
The real bottleneck was unclear ownership at the product–platform boundary — decisions that required both teams stalled silently rather than escalating.
Customer success
Model surfaced
A misalignment between what sales promised and the workflow customers actually adopted — diverging at first meaningful use.
Operations
Model surfaced
Three independent blockers — spec ambiguity, async review lag, and missing decision authority — each sufficient on its own.
Leadership
Model surfaced
Work had become invisible — impact felt only through its absence, with no formal recognition pathway.
Product
Model surfaced
No one had authority to cut scope — everything stayed in progress rather than shipping or being killed.
Personal
Model surfaced
Two distinct clusters — loss of craft identity and anxiety about relevance — each needing a different response.
Confidence model
Relationship confidence is computed deterministically from the types of observations supporting each causal link. A relationship backed only by reported beliefs is automatically flagged as weak — and becomes a question target.
The investigation loop
Questions are generated from exactly three sources — weak causal links, gaps in the causal map, and load-bearing assumptions. Nothing else. Each answer updates the model before the next question is selected.
Targets relationships evidenced only by beliefs, or flagged as hypothesized. A yes/no answer directly updates the relationship's confidence and status.
Targets clusters that appear to be root causes but have no upstream explanation. Your answer becomes a new observation, routed into the model.
Targets assumptions whose validity would significantly reshape the causal map. Contradicting an assumption triggers a re-evaluation of dependent relationships.
“If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”
Attributed to Albert Einstein
Phase 1 gives you the structure to understand a problem deeply before anyone has to agree on what to do about it. Start with one real problem, today.
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