Find the bottleneck. Move the work forward.

The investigation workflow follows a predictable path. An alert fires, someone opens a query, and the team traces backward through the data until the root cause is visible. SyneHQ structures this path so each step is recorded and reusable.

Start with a saved query or open a new one in Data Explorer. Pull the relevant time window, filter to the affected service or entity, and let the results guide the next question. When the investigation requires more than a single query, move into Quantum Lab and build an ordered notebook: SQL blocks for the raw data, Python blocks for transformations, and Markdown blocks to capture what you are seeing and why it matters.

Kole, the AI agent, can assist at each stage. Point it at the relevant connection, describe the issue, and let it propose queries against your schema. It reads the available tables, inspects column types, and suggests a starting point. You review and approve each proposed action before it runs, so the investigation stays under your control.

Once the root cause is identified, save the notebook. The next time a similar alert fires, your team opens the saved investigation instead of starting from zero.

Not every operational question starts with an incident. Teams also need a standing view of how systems are performing: processing rates, error rates, latency percentiles, and throughput over time. SyneHQ Dashboards let you build these views from the same queries your team uses during investigations.

A dashboard can combine chart blocks for time series data with Markdown blocks that explain thresholds, ownership, and escalation paths. Pin the queries that matter to a shared dashboard, set a refresh interval, and let the on-call engineer open one page instead of running five queries manually.

The value compounds over time. Each query you write during an investigation becomes a candidate for a dashboard panel. Error rate spikes that triggered a manual investigation last quarter become a chart that the team watches in real time this quarter.

Operations work follows patterns. The specific systems vary, but the shape of the investigation is consistent. Here are concrete examples of how teams use SyneHQ to handle recurring operational scenarios.

The most expensive part of an operational issue is not the fix. It is the time spent rediscovering what happened, why, and what was done about it. When investigations live in chat threads or personal notebooks, that context evaporates.

SyneHQ keeps investigations in a shared, searchable environment. Saved queries in Data Explorer, ordered notebooks in Quantum Lab, and standing dashboards all belong to the team. When someone rotates off on-call, the next person inherits the same tools, the same saved investigations, and the same dashboards.

Kole accelerates the routine parts: proposing queries, inspecting schemas, and surfacing relevant saved work. The human operator still decides what to investigate, what the data means, and what action to take. The agent handles the lookup; the engineer handles the judgment.

Start by saving your next investigation. Open Quantum Lab to build an ordered notebook for an active issue, or set up a Dashboard to track the metrics your team checks every day.