The problem
When you ask ChatGPT a complex question, you get a confident, well-written response. But you have no way to know what it left out, what assumptions it made, or whether the opposite conclusion might be equally valid.
Surface-level answers
ChatGPT gives you one confident answer. You have no idea what it ignored.
Hidden assumptions
Most AI answers are built on assumptions you never agreed to.
No opposing views
Conventional tools don't seek contrarian evidence. Blind spots stay blind.
Can't verify reasoning
The reasoning chain is invisible. If it's wrong, you'll never know where.
How it works
Instead of generating one shot answer, Ward runs your question through a structured pipeline. Each stage is visible, editable, and re-runnable.
Break it down to first principles
Complex questions are opaque by design. Ward breaks your problem into 3–5 atomic subproblems, each with identified key variables, governing formulas, and explicit assumptions. No hidden leaps of logic.
Why this matters: Without decomposition, AI gives you one monolithic answer built on hidden assumptions. You can't verify what you can't see.
Apply the right mental framework
Each subproblem gets matched to a mental model — Bayesian reasoning, systems thinking, inversion, second-order effects, and more. The model shapes how evidence is gathered and weighed.
Why this matters: Without explicit models, reasoning defaults to availability bias — whatever comes to mind first. Mental models force structured thinking.
Dual-channel investigation
Every subproblem is investigated through two lenses: consensus findings (what most evidence supports) and contrarian findings (credible dissent). Each finding gets a confidence score.
Why this matters: Single-channel research creates confirmation bias. By explicitly seeking contrarian views, Ward surfaces risks and blind spots that conventional analysis misses.
Coherent report, not a data dump
Findings are woven into a structured markdown report with executive summary, per-subproblem analysis, cross-cutting themes, and actionable recommendations.
Why this matters: Raw research output is unusable. Synthesis transforms scattered findings into a narrative you can act on — with the reasoning chain fully visible.
Built for clarity
Every step — decomposition, model selection, research findings — is visible and editable. Re-think any node with custom guidance.
Consensus and contrarian findings with confidence scores. See what most evidence supports AND credible dissent.
Track every LLM call: token counts, latency, cost estimates. Full observability into your AI spending.
Pricing
No feature gates. No per-query limits. Cancel anytime.
Everything you need for deep, structured research.
Free trial available. No credit card required.