Askovo vs. Your Current Research Stack
Others make research cheaper. Askovo makes it accountable — you always know which sentence your evidence can't back.
AI research tools produce reports where every sentence sounds equally confident. The grounded and the invented look identical. If you sign decisions on that, you’re absorbing risk you can’t see — one Hacker News commenter called deep-research outputs “a way to launder liability.”
That sentence is why I built askovo around one discipline: every claim gets an evidence grade, and the tool tells you when a conclusion outruns its data.
The comparison, row by row
| ChatGPT / deep research | Dovetail | Manual UXR project | askovo | |
|---|---|---|---|---|
| What it organizes | A conversation | What you collected (repo, tags, highlights) | The study itself | What your conclusions can claim |
| Claim typing | — | — | Depends on the researcher’s rigor | 6 types, each with its own evidence standard |
| Evidence grading | None visible to the reader | None (repository/synthesis scope [a]) | Implicit in the report | E0–E5, machine-written per claim |
| “This conclusion outruns the data” | Rarely volunteered | Out of scope [a] | Depends on reviewer | A first-class verdict (exceeds_evidence), exit code 1 |
| Product reasoning chain | — | — | Varies | scene → user → pain → need → feature → product, rationale on every edge |
| Gates in CI/pipeline | — | — | — | Exit codes (0 pass / 1 evidence shortfall / 3 no viable source / 4 safety hold) |
| Collection | Web reading | You bring data | Interviews etc. | GitHub/Reddit APIs + authorized exports; blocked platforms stay blocked |
| Cost | Subscription | Subscription, per seat | $$$ per project | MIT, local-first |
[a] Based on Dovetail’s public positioning as a research repository & analysis workspace. If their scope has changed, tell me and I’ll update this table.
Two rows deserve explanation.
Claim typing. Need, acceptance, usability, performance, commercial, safety each require different evidence. Interviews never prove pricing. Task completion never proves demand. A clean lab run never proves road safety. Cross-type inference is the most common way research gets laundered into decisions — so each claim type carries its own evidence standard inside askovo.
Evidence grades. E0 scene → E1 pain → E2 alternatives & cost → E3 solution acceptance → E4 willingness-to-pay → E5 paid & retained behavior. A pricing conclusion resting on interview quotes gets exceeds_evidence. That verdict is not an insult to your research; it’s the cheapest correction available before the decision meeting.
When not to use askovo
- You need a research repository for a team — use Dovetail; askovo doesn’t store your corpus life.
- You need polished deliverable writing — askovo grades, it doesn’t ghostwrite.
- You want numbers like “73% of users…” from public posts —
analyzerefuses: descriptive only, no prevalence, no causality. - You want the answer in one prompt with no discipline — that’s exactly the thing askovo exists to slow down.
Try it
pipx install git+https://github.com/roy-tong/askovo.git
askovo demo
Expected: status: pass, supported: 1, overclaims: 0 — which proves the structure and evidence chain hold together, and does not prove market demand. The tool says so itself; that’s rather the point.