Plain-language answers and summaries.
You don't have time to watch ten thousand sessions. LogClip's AI does — then hands you a short list of what's broken, why it matters, and the lift you'll get from fixing it.
From ten thousand sessions to a short list.
5 stops, each one a real screen from the console.
Failures ranked by what they cost
Ranked by what a failure costs, not how loudly it fires — the noisiest error is rarely the expensive one. Identical failures collapse into a single card, and each card carries Watch, which opens a session at the failing moment, and Track, which turns it into a signal.
What to do, and what it's worth
One card per change, and the whole argument for it is on the card: the finding, why it matters, what to do about it, what it should cost you to do, and the metric it should move. Nothing to go and look up before deciding.
The failure, written up with its evidence
A recurring failure becomes a written investigation — sequence, impact, severity with its reason, reproduction steps. Every bullet links into the session it came from, so the write-up can be checked rather than believed. Send it to GitHub or Linear from here.
Eight open investigations, with sessions, last seen and assignee. Open, resolved and closed are tabs.
Five thousand sessions, eight themes
Pick a cohort and a window; sessions group by landing page, dominant friction and whether they converted. The grouping is exact arithmetic — only the naming and the prose are AI, which is the part worth knowing before trusting a summary.
Ask in plain words, get an answer with receipts
Ask in plain words and the question becomes a cohort, shown to you before it runs. Every claim cites the sessions behind it, each moment has a Watch link, and the answer is saved — so the evidence outlives the conclusion.
Everything under ai insights
AI reads your sessions, names the friction, groups it into themes, and tells you what to fix first — with an estimated impact.
Cause cards
Identical failures — the same error, the same failing request — grouped into one card and ranked by the conversions they cost. Count, cause, cost, fix.
Watch the moment
Every card and every claim links to a real session at the exact failing moment, not the start of the recording.
Friction hotspots
Where people rage-click or dead-click, grouped by the exact element — a cheap, direct signal that a control is broken or confusing.
Daily anomaly watch
Today against the last seven days for sessions, conversion, errors, rage and failed requests; anything that moved 40% or more gets a plain-language note.
Recommendations
Specific fixes with what's wrong, why it matters, what to do, an effort estimate and the expected lift. Apply or dismiss each one.
Issues
Recurring failures written up as investigations — summary, what happens, user impact, severity, repro steps — each bullet citing the sessions it came from.
Summaries
A whole cohort clustered by landing page × friction × outcome, each group AI-named with a next step and sample recordings. The grouping is exact maths; only the prose is AI.
Ask AI
A question in plain words becomes a cohort filter, the sessions get watched, and the answer cites the recordings it drew on. Every answer is saved.
Self-driving signals
Findings — recurring failures, hotspots, poor vitals — become ready-made signal suggestions with an estimated match count. One click to adopt.
Why it matters.
Insights in plain language
What's working, what's not, and what to do next — each with a priority, an estimated impact, and the effort to ship it.
Behavioral summaries
AI clusters sessions into a few clear themes — friction on a page, slow loads, network failures — each with a recommendation.
Daily anomaly watch
Every day, LogClip compares your metrics against their baseline and flags what moved — before it becomes a fire.
Minutes, not quarters.
Read the short list
Cause cards and hotspots on one page, ranked by what they cost — not a feed of every error.
Check the evidence
Watch opens the session at the moment the card describes. Trust the link, not the prose.
Act, then measure
Track a card as a signal, apply a recommendation, open an issue in GitHub or Linear — and the anomaly watch tells you if it moved.