LogRocket

It is 2:14 in the morning and a support ticket says the checkout button "doesn't work." No screenshot. No browser. No clue. The engineer on call does what engineers have always done: stares at logs and guesses. Somewhere in Boston, a company exists for the express purpose of ending that ritual.

LogRocket is what happens when you decide guessing is not a strategy. The platform records real user sessions - clicks, scrolls, rage-taps, network calls, console errors, the whole forensic trail - and stitches them to product analytics and error tracking so a team can watch the exact moment a user gave up. Where most tools hand you a stack trace and wish you luck, LogRocket hands you the tape.

Today the company employs roughly 520 people, counts thousands of customers, and has raised $55 million. But the thing it sells has barely changed since two friends sketched it out in a San Francisco sublet: show me what actually happened, not what the dashboard guessed happened.

Everyone could measure what. Nobody could see why.

By the mid-2010s the analytics shelf was crowded. You could count pageviews, funnels, conversion rates, churn. You could be drowning in numbers and still have no idea why a perfectly healthy-looking signup flow quietly leaked a third of its users. The dashboards told you the patient had a fever. They never told you the cause.

Error trackers had the same blind spot from the other side. A red alert fired, a stack trace landed, and an engineer spent an afternoon reverse-engineering a human being's bad afternoon. The data existed. The story did not.

Pictured in spirit: a thousand engineers refreshing a logs tab at 2 a.m., collectively inventing the market for a product that did not yet exist.

Two kids who shared a play date - and later, a thesis.

Matthew Arbesfeld and Ben Edelstein met at one month old, courtesy of parents who arranged the introduction. They have been building things together since roughly the third grade, which is either charming or slightly alarming depending on how productive your own childhood was. Arbesfeld went to MIT and became a Thiel Fellow; Edelstein went to Columbia. Both drifted to San Francisco and both ended up writing front-end code - Arbesfeld at a startup, Edelstein at Google.

The bet they made was unfashionable. Recording full user sessions sounds expensive, heavy, and faintly invasive. Conventional wisdom said sample your data, aggregate it, and move on. LogRocket's wager was the opposite: capture the whole session, make it cheap enough to keep, and the playback alone would be worth more than any chart. They were, as it turned out, right.

One platform, three jobs that used to need three tools.

LogRocket's quiet trick is consolidation. Session replay, product analytics, and error tracking historically lived in separate tabs, separate vendors, separate invoices. LogRocket put them in one place and - more importantly - linked them. Click a dip in a conversion funnel and you can watch the actual sessions where it happened. See an error spike and you can replay the human who triggered it.

Then came the AI chapter. In late 2023 LogRocket launched Galileo, an AI layer that watches sessions and reads support data to surface the most painful moments in a product on its own. The follow-up, Ask Galileo, lets a product manager simply ask a question - from inside LogRocket, from Slack or Teams, or from Claude, ChatGPT, Gemini, and Cursor - and get an answer grounded in their real product data. LogRocket reports it handles roughly 90% of queries about as well as a human analyst would.

The receipts: customers, capital, and a long investor memory.

A pitch is a pitch until someone famous starts paying for it. LogRocket's customer list reads like a cross-section of the internet you use every day - retailers, media companies, fintechs, and the apps in your pocket.

Reading the room: when Reddit, Ikea, and a TV network all reach for the same replay button, the "nobody records full sessions" objection quietly retires.

There is a tell in the cap table. Battery Ventures and Matrix Partners did not just show up once; they kept re-upping across rounds, with Delta-v Capital joining to co-lead the Series C. Investors who stay are usually investors who have seen the retention numbers the rest of us have not.

Make the invisible user visible - then act on it.

Strip away the feature list and LogRocket is selling a single belief: that you cannot build a good product for people you cannot see. Every session recorded is an argument against guessing. Every friction point Galileo surfaces is a small refusal to let a confused user disappear into a churn statistic.

It is, if you squint, a slightly old-fashioned idea dressed in machine learning - the notion that paying close attention to actual humans beats theorizing about average ones. The irony is that it took a pile of session data and an AI to make "just watch your users" feel new again.

When software writes itself, someone still has to watch it.

As more code is generated by AI and shipped faster than any human can fully review, the gap between "what we built" and "what users experience" only widens. The teams winning will be the ones that can see that gap in real time and close it. LogRocket's bet on full-session capture, made when it looked extravagant, looks a lot like infrastructure now.

Back to that 2:14 a.m. ticket. With LogRocket on, the engineer does not guess. They open the session, watch the user tap a checkout button that silently failed on a flaky network, see the console error in the same frame, and fix it before breakfast. The bug did not get more honest. The team just stopped being blind to it.

// LogRocket · Boston, MA · established 2016 · still watching the tape