The elevator pitch

• You always knew what chart you needed. The hard part was everything between picturing it and actually seeing it.
• Computer, by DevRev, turns plain-language requests into live dashboards drawn from the shared memory of your entire business - tickets, accounts, conversations, code changes, and every custom object your team has built.
• Ask for a chart. Drag it into place. Ask for the next one. A few sentences in, you are looking at a dashboard that would have been a two-week project anywhere else.

The chart was never the hard part

You always knew what you wanted to see. You could picture it: resolution times by priority, broken out by account, with customer sentiment sitting right beside it.

The hard part was everything between picturing it and seeing it.

The data lived in three places. Building the view meant a query you could not write yourself. So you described what you wanted to someone who could, filed a ticket, and waited. By the time the dashboard came back, the question that prompted it had moved on. You asked for one more change. You waited again.

The people who knew exactly what they needed to see were the ones who could not build it.

That part never made sense.

The "chat with your data" ceiling

Every analytics tool now offers a chat box. Type a question, get a chart. The pitch is everywhere.

But they report on a fixed handful of datasets - the tidy, expected ones. The moment your question crosses a boundary (support data joined with product data, or a custom object the vendor never imagined), the conversation ends and the analyst queue begins.

The interface got friendlier. What it could reach did not change.

The AI hit a prompt ceiling because it can only answer within the narrow band of data it was wired to see. The question is not whether you can type a natural-language query. The question is whether the answer can go wherever your work actually goes.

What the ideal solution has to do

Before naming any product, here is what an analytics layer needs to actually solve this:

1. Unified data - One query surface that spans every system your team touches, without pre-built connectors for each combination.
2. Live and current - Not snapshots from last night's import sync. The state of the business right now.
3. Permission-aware by default - Anyone can look, but they only see what they are allowed to see. No exceptions.
4. Extensible without engineering - When your team adds a field or builds a custom object, it is immediately chartable. No reconfiguration.
5. Trustable at click-depth - Every number on every chart opens into the exact records behind it. The count and the rows are always the same set.

Computer turns a sentence into a dashboard

Computer, by DevRev, is the only AI with native shared memory. It does not query a reporting database someone had to set up first. It answers from the living record of your business data: tickets, accounts, conversations, code changes, and the custom objects your team built for the way it actually works.

So a chart request can cross systems in one sentence.

"Show me resolution times by priority for the last month." Done.

"Break that down by account." Done.

"Put customer sentiment next to it." Done.

Each request becomes a chart. Each chart lands on the canvas. And the canvas is yours to shape - drag it, regroup it, change the date range, add the next one beside it. Describe what you want or build it by hand. Both paths end at the same dashboard.

Here’s how it works

Conversational creation

Ask Computer for the chart you have in your head, in plain language. Sentiment from conversations, next to resolution quality from tickets, for the accounts in your renewal window. Cause and effect on one screen - because underneath, they were never in separate places.

Cross-system in one sentence

Because Computer operates on shared memory (not siloed connectors), a single request can join data that lives across support, product, sales, and engineering. No pre-built report templates required.

Instant extensibility

Add a field to track something new, and the dashboard picks it up instantly - ready to chart, no reconfiguration. Build a custom object for a process only your team runs, and it is as reportable as tickets are. The dashboard evolves at the pace of the work, not the pace of the rollout plan.

Click-through trust

Any bar on any chart opens into the exact records behind it. The count on the chart and the rows underneath are the same set, always, because one is drawn from the other. The dashboard cannot drift from the truth. It is the truth, arranged so you can see it.

Permission-aware by default

Every chart respects what each viewer is allowed to see, because permissions are inherited from the source systems. Share a dashboard with someone who lacks access to the objects underneath, and those charts simply do not render for them. Nobody backs into numbers they were never meant to see.

Numbers you can put in front of a room

Everyone has been in the meeting where two dashboards disagree and the next twenty minutes go to figuring out which number to believe. An AI that builds charts on request makes that fear sharper, not softer. If the agent assembled the number, why trust it?

These numbers come from a public benchmark built to answer the question every AI buyer is asking: why not connect a frontier model to your systems and build this yourself?

So we tested exactly that. Computer and the frontier model it runs on were given the same 13 enterprise tasks on one company's data - spread across support tickets, engineering issues, sales records, and documents - and scored by the same judge. One agent had shared memory. The other was the do-it-yourself build.

The tasks where the gap opened widest are the analytical ones: the multi-step questions that join records across sources. Precisely what a dashboard request is.

• Computer: 91.9% of tasks answered correctly.
• Frontier model alone: 63.5%.
• The gap held as the dataset grew 64x larger.

Ask the same question twice and the answer holds - because it is computed from what is true in your systems, not from how a model happens to read a prompt on a given day.

Build alone, or build together

A dashboard starts private. You build it, sit with it, get it right without an audience. When it is ready, share it view-only to the people who need to read it, or editable to the ones building alongside you.

Or start together. Dashboards live inside Computer's multiplayer sessions, so a team can build one in the same room, on the same live data. Invite a teammate and the context arrives with them: the accounts, the tickets, the deal history are already there, because the session runs where the data lives. Nobody re-explains the account or re-uploads a spreadsheet.

The support lead adds a chart on the resolution spike. The AE pulls the renewal view beside it. Anyone can ask Computer for the next chart mid-discussion, in the same plain language that built the first one.

This is also the quiet fix for the meeting where two numbers disagreed. That meeting happened because two people exported two snapshots at two different times. A dashboard built together on live data leaves nothing to reconcile. One version, always current. Click any number and the records settle the argument.

Working together does not loosen who sees what. Permissions travel with the data, not the invitation. Bring someone into a session and the charts built on objects they cannot access stay dark for them - same as everywhere else in Computer.

See it for yourself

Book a demo and we'll build a dashboard live – from test data, or whatever you’re happy to share with us.

Together, we’ll move from big data sets and a blank canvas to full, interactive, fully-cited dashboard in minutes.