Value × Attention intelligence

Let GPT & Claude debate until they understand your business cold.

S1NK orchestrates adaptive questioning, dual-model comparisons, and a meta-consensus layer so you know exactly where to invest your focus.

Start adaptive onboarding View saved analyses

Adaptive prompts: 5 micro Qs
Consensus pass: 3-model stack
Avg. clarity gain: +42%

Dual-model debate

GPT-5.1

My hypothesis: S1NK automates value discovery by chaining targeted questions with consensus prompts.

Claude 3.7

Agree on the loop. I want to validate customer intimacy vs. market signal within the attention map.

Meta consensus

Align on: (1) adaptive question clarity, (2) conflicting opportunity area — craft follow-ups.

Meta-consensus waits for your review before locking the report.

S1NK stack

Your business, interrogated thoughtfully.

Each stage feeds the next so you never see hallucinated reports or vague action items.

Adaptive questioning

Micro-prompts that recalibrate depending on your previous answers. No more generic intake forms.

Step 1

Dual-model comparison

Run GPT-5.1 and Claude 3.7 side-by-side with structured rubrics.

Step 2

Meta-consensus reasoning

A supervisor model reconciles disagreements and surfaces follow-up prompts.

Step 3

User validation loop

You approve or adjust the AI understanding before reports are locked.

Step 4

Value × Attention ledger

Every insight is scored on projected value vs. leadership attention required.

Step 5

Pipeline preview

Collect signals with five adaptive micro prompts.

Dynamic prompts
Confidence scoring
Zod validation

Flow confidence20%

Stage 1

Onboarding

Collect signals with five adaptive micro prompts.

Stage 2

Analysis

Send structured context to GPT + Claude while monitoring divergence.

Stage 3

Consensus

Meta-chain reconciles outputs and builds follow-up questions.

Value × Attention

Report Snapshot

Generated in under 4 minutes

High Value • Low Attention

Compounding bets

Customer evidence vault
Executive AI briefing

High Value • High Attention

Leverage checks

Pricing experiment backlog
Deep partner syncs

Medium Value • Low Attention

Quick optimizations

Lifecycle emails
AI FAQ automation

Low Value • High Attention

Deprioritize

Legacy event SOW
Custom onboarding build

Meta consensus insights

Attention drag is caused by conflicting GTM narratives. The report suggests async “understand us” modules before exec reviews.
Open question: is product-market concentration too narrow? S1NK proposes 3 follow-up questions to confirm.

Ready?

Launch S1NK in minutes.

Deploy to Vercel, connect Supabase + Prisma, and invite your first operators.