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ScopeDB

Model in events.
Query your world.

A serverless analytics database for logs, product events, and AI traces. Query, join, and explore data as it arrives, without defining every field up front.

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Different events become insightsSix cards representing code, interactions, traces, logs, cloud data, and messages appear one beat apart with matching actions: a pointer clicks, code unfolds, logs write, a trace travels, a cloud receives data, and message dots type. Each card springs into the aligned grid. The cards wind back half a beat before a compression wave squeezes and twists them clockwise through a tightening spiral into the center over one beat. The compression then releases four charts that settle two beats later in an aligned grid: a rolling trend, a distribution, a matching breakdown, and a heatmap of event categories over time. These are conceptual examples, not measured product data.

Agents turn events into answers

Your agents choose the next query, connect related events, and follow the evidence in ScopeDB to explain what happened.

  1. Checkout errors spiked around the 14:06 release. I'll compare failures before and after it to measure the change.

Understand every event

Query product events alongside logs and traces to see where users drop off, which requests slow them down, and what happens inside an agent run.

Track conversion

Follow visits through signup and activation, and see where users drop off.

Signup conversion
Visits convert to signups and activationsA growing cohort starts with 1,000 visits, 620 signups and 410 activations. Each new batch adds 60 visits, then 38 signups and 25 activations. Bar lengths share the same scale so the drop between steps is visible.

Find slow requests

Spot requests above your latency threshold, then trace the wait to logs and dependencies.

Request duration
Request durations over the last minuteEach bar is one request. New requests enter at Now and move left. Purple bars exceed the dashed 650 millisecond threshold. The scale stays fixed from zero to 1,000 milliseconds.

Trace agent runs

Follow parallel tool calls, a failed search, and its retry before the agent answers.

Agent run
Parallel tools, a retry, then an answerThe agent first plans the task. Its decision reveals parallel search and query calls. Search times out and a retry appears while query continues. After both tools finish, the answer is revealed and generated. Checkmarks mark completed steps.

Scale with demand

Compute scales up as demand rises and back down when it falls.

Elastic capacity
Elastic capacity follows changing demandElastic capacity adds and releases compute as workload changes, leaving headroom. Filled square cells represent elastic compute capacity in relative units. The foreground curve shows workload. New observations enter on the right and move left over time.
  • Workload
  • Elastic

Independent compute, shared data

Run ingestion and analytics in separate compute groups, with one shared dataset in S3.

Architecture

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