Amplitude, Jira, Metabase, support notes — you’ve got solid systems, each with real depth, and there’s no reason to replace them. The problem isn’t the tools themselves. It’s that there’s nothing connecting them: your data is spread across five to ten systems, and a big chunk of the workday goes not into actual analysis but into pulling it all together and linking one thing to another. This article is about an agent that takes on exactly that connective tissue — not another “AI analytics that knows everything on its own,” but a navigator that remembers where your stuff lives and knows how to go get it. Below: what a typical work question looks like today, what that same question looks like with an agent, how it works under the hood, and — most importantly — where the line is between it and a real human specialist.
Let’s start with how things work right now.
One question, two hours of navigation
A typical Tuesday for a PM. A message lands from the CEO: why did retention drop in the April cohort — the share of users who came back to the product after signing up.
You open your product analytics and go look at the cohort. The drop is real, but why — unclear. You try a different angle, through your second analytics tool, and get slightly different numbers, because events are defined differently between the two systems, and you’re once again reminding yourself which one to treat as the source of truth. You go into the task tracker to see what shipped in April and find a couple of releases and one feature flag rollout. You go through your working notes to read what support wrote about feedback that month and run into complaints about an auth bug affecting some users. Now you need to figure out exactly how many people it hit, so you go into the data warehouse to write a SQL query — and it throws an error because the table schema reference is outdated. You go into the chat to ask the data engineer what the field is called now.
Two hours later you’re back in front of the CEO with an answer. By that point you’ve had a dozen tabs open, jumped between four tools, and reconstructed your mental map of what lives where — several times over. The actual answer took maybe twenty minutes. Everything else was navigation between sources, translating from one system’s language to another — something you carry in your head and redo from scratch every time.
And this isn’t just a PM problem — analysts and business analysts live the same way. Their core pain almost never sounds like “we don’t have enough data.” There’s usually plenty of data, sometimes too much. The pain is that it’s spread across a pile of systems, and most of the time what eats the day isn’t thinking — it’s assembly. An agent that goes to all those systems for you changes exactly one thing, but it’s the important one: you stop being the translator between your data sources.
The same question, with an agent
Same Tuesday, same question from the CEO about the April cohort. Only now you don’t open eight tabs — you type it in a single sentence to a chat.
From there the agent does exactly what you would have done, just on its own and in order. It checks product analytics, pulls the cohort data, confirms the drop is real, and records the scale. It goes to the task tracker for April’s releases and feature rollouts, cross-references the dates — which of them could have touched retention by timing. It pulls support feedback and working notes from the same period, finds the auth bug. If it needs to count exactly how many users were affected, it writes a SQL query against the data warehouse and runs it, working from a saved map of the schema rather than your memory.
A minute or two later you get not a dashboard and not a raw export, but a coherent answer: here’s the number, here’s what shipped in April, here’s when the complaints started, here’s how many people the bug hit, and here’s a hypothesis that ties it all together — with links to sources so you can verify. Want to dig deeper? Keep going in the same chat: break it down by platform, look at the same thing for a specific feature, compare with March. And each of those follow-ups isn’t a new expedition through tabs — it’s another line in the conversation.
It’s not that the agent is smarter than you. Its hypothesis is rougher than what you’d come up with, and you’ll often need to push back on it. The difference is that all the mechanical work — going, fetching, cross-referencing, assembling — happens in seconds on its own, and what’s left for you is what you’re actually there for: looking at the assembled picture and thinking.
How it works
From the outside this looks like magic, but it’s built simply — and understanding the construction is exactly how you see why the agent goes by itself rather than waiting for instructions at every step.
First — connections to your sources. SQL access to the data warehouse, API tokens for product analytics, integrations with the task tracker and working notes, access to email and chats if needed. All of this gets set up once during onboarding and you never touch it again: the channels are open, the agent uses them.
Second — and arguably the most important part — a map of your data, written down in the rules. These are plain-text descriptions of what lives where and what the logic is. Which events are set up in analytics and what your team counts as an active user. Exactly how your team calculates retention, because every team has its own formula. Where releases and feature flags live in the task tracker. How your working notes are structured. Which SQL queries you reuse over and over. All of this used to live only in people’s heads — a new team member would spend weeks extracting it through questions. Now it’s written down once and accessible to everyone with access to the chat. The map isn’t static: every new question sharpens it a bit, and the agent’s answers get more accurate over time.
Third — the chat interface, the thing you actually type into. For each message it first checks the rules, figures out which sources to hit and what to look for there, then goes and does it: fetches the data, cross-references the dates, writes and runs a query if needed, assembles everything into a short answer. It doesn’t respond with “here’s a link to the dashboard” — it brings back a ready breakdown.
The closest analogy is a new analyst versus someone who’s been on your team for six months. The new one is smart but starts from zero every time: where your stuff lives, how a metric is calculated, which tool is the source of truth — you have to explain all of it again. The one who’s been around just knows it and carries a map of your setup in their head. An agent with a written-down ruleset behaves like the second: you don’t re-explain the context, it already lives in it.
Where this makes the biggest difference
The gap is most visible on ad-hoc questions from leadership — those “why is this number off,” “give me a regional breakdown,” “compare to last quarter” requests. Before, each one cost you an hour of assembly, and so many of them the CEO simply didn’t ask: he knew it was half a day of your time and spared it. Now it’s five minutes, and something interesting happens — people start asking more, and questions that used to quietly die unasked actually get answered. That shifts not so much the speed of work as how well the team understands what’s going on.
Regular digests fit here naturally too. A morning or weekly summary of key metrics, assembled by the agent from all sources, with a short human-readable comment it writes itself: here’s what went up, here’s what dropped, here’s what’s worth a closer look. You set the frequency yourself.
Separately — prep for product team meetings. Before each one the agent pulls fresh metrics, current release status, open tickets, and the latest research data on its own, so you show up with the picture already assembled rather than empty-handed with a promise to “follow up after the call.” The same thing comes in handy during incident diagnosis, when something breaks or a metric starts behaving strangely: it pulls together monitoring data, analytics, and tickets into one picture in minutes instead of half a day of team-wide back-and-forth.
Where this isn’t about replacing a specialist
It’s worth drawing that line directly, to avoid inflated expectations — because the temptation to promise an “AI analyst” is real, and the honest version is narrower.
This is not an AI analyst that will replace you or your team. Deep analysis, hypotheses, experiments, product decisions — that’s still your work. The agent is just a layer on top of your tools that strips out the tedium of collecting and stitching data and gives you back time for the things you’re actually on the team for.
It’s not a replacement for dashboards either. Dashboards live in a different genre: they’re for regular, recurring metrics, for a broad team, for a steady view of the business month over month. Chat with an agent is for ad-hoc questions and fast answers right now. One doesn’t cancel the other — they’re for different things.
And this isn’t a story about “AI drawing its own conclusions.” The agent assembles data, sketches a hypothesis, gives a coherent answer — but the decision is still yours. If the hypothesis looks shaky, you probe it, and it usually gets refined quickly through the same conversation. It’s a navigator and assembler, not the one deciding what to do about any of it.
What to do with this
If your day currently looks like an endless trip between analytics, your task tracker, notes, the data warehouse, and chats — you’re not alone. That’s how most PMs and analysts live, and they tend to recognize themselves here without much effort.
We built kvelo — a pre-configured agent you don’t have to stand up from scratch. It’s not an empty box and not another one-size-fits-all assistant: the basic logic for working with data sources is already in there, and from that baseline it gets tuned to your specific stack and team. We handle the initial setup. Some integrations are quick — they’re standard APIs. Others require mapping your data — where things live and how your team calculates things — and that’s the real work of onboarding. Usually within the first week the agent is handling a meaningful share of your ad-hoc questions, and accuracy keeps improving as context builds up.
Worst case, you spend an hour talking and realize it doesn’t fit your stack yet. Best case, within a week you’ve got something sitting alongside you that remembers where your stuff lives and has the answer ready while you finish your coffee.
You can find out more on the site. Or drop a request below — we’ll walk you through how it gets assembled around your tools, and if you want, you can pick a time to talk.
