Findings you can defend
Every claim is scored, tied to a verified participant, and traceable to the moment it was said. The Truth Engine turns raw sessions into evidence that holds up.
Evidence, re-engineered
Every quote and every screen event lands as a piece of evidence in its own right, not a line in a transcript. Words and behaviour are processed in parallel, scored the moment they land. This is the foundation the AI works from: structured evidence, with a verified human behind every record.
Two records from every session
What they said comes from the transcript. What they did comes from the recording, through vision analysis on unmoderated tests and event data straight from the Figma. We tie both to the same person, so you never read one without the other.
Every quote, structured
What participants said, extracted from the transcript and tied to the participant who said it. Every utterance scored the same way, whether it came from an interview or a website test.
What they did, not just what they said
Vision analysis of every screen recording surfaces where users paused, hesitated, or backed away. For Figma prototypes, event data comes directly from the API.
There is an architecture beneath every answer
Behind every answer is a structure that knows how the evidence connects. Ask Askable AI a question and the claim it returns is already built: scored, sourced to a real person, and linked to every piece it rests on, across every session you have run. Findings, Ask AI, Connectors. They all read off the same structure.
Every finding is clickable.
Every clip is real.
Finding
Supporting quotes and screen events
Participant
Session moment
If the evidence exists, the finding surfaces. If it does not, the finding does not.
Enforced by the architecture, not policed by humans.
One layer. Four ways to reach it.
The layer shows up wherever decisions get made.
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Study results
Open the results of an AI moderated interview or live website test. Findings extracted from that study's sessions appear in a built-in results view: scored automatically, with playable highlights, participant attribution, and source links.
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Ask AI
Ask a question. Findings come back with inline citations, supporting quotes, and source clips. Every claim traces back to a verified participant.
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In docs and reels
Generate a written doc or a video reel from any set of findings. Editable in place. Shareable by link, email, or team access. Comments, embedded clips, source links.
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Connectors in your stack
Connect Askable to Claude, ChatGPT, Cursor, Copilot, Figma Make, Lovable. Query the layer from where you already work.
FAQs
The Truth Engine is the architecture that turns raw research sessions into evidence that holds up. It scores every piece of evidence, clusters it mathematically on verified data first, and only then has AI describe what it found. The AI describes a pattern, it never invents one.
Askable measures every piece of evidence against the same quality bar before it can reach a finding. The bar is applied consistently to every session, so nothing is promoted because it sounds interesting or demoted because it is inconvenient. Strong evidence drives conclusions, weak evidence does not.
Structurally, no. Findings are built only from evidence records that already exist, each tied to a verified participant and a real session moment. A claim with no evidence behind it has nothing to be built from. This is enforced by the architecture, not requested in a prompt.
No. Askable enforces zero data retention contractually with every model provider. Your research is processed and discarded, never retained and never used for training. Askable holds ISO 42001, the AI management standard.
Every finding is clickable. Follow it to the supporting quotes and screen events, then to the participant who produced them along with their screening responses, then to the timestamp and the exact video clip. The whole chain is auditable, so a stakeholder asking where a claim came from gets an answer rather than an assurance.
Sessions are checked before their evidence counts for anything. Anything showing adversarial or bot-like behaviour is rejected outright, as is any session that ends before the research goals have been covered. A rejected session does not get a low score, it never enters the evidence base at all.
Ask the question.
Build from the answer.
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