We define KPIs, build dashboards, and turn analysis into a clear data story. Looker, Power BI, Tableau, and Hex are part of the toolkit. Shared measures connect every view to the work behind the number.
LookerW-104 took 21 minutes. What changed in its route through the work?
One row per completed work item. Open items are excluded.
An AI interaction can involve retrieval, model calls, tools, and retries. We bring those events into a consistent analytical model so teams can investigate quality, latency, usage, and cost against the outcomes they care about.
The retrieval step returns permitted source references. Its execution record preserves the task ID, retrieval version, and elapsed time.
The retry succeeded. Task evaluation remains separate from execution success; usage estimates remain separate from reconciled charges.
Separate structured-record and document paths preserve source IDs, versions, freshness, and access rules. Changes travel through the derived data and retrieval path.
09:00 UTC · v3 is current. A permitted request returns a source reference. Restricted items are excluded by an authorization check.
Refresh policy: five minutes. Freshness, permission checks, and answer evaluation remain separate.
We build dashboards, KPI views, and data stories around the decisions your teams need to make. Each measure has an agreed calculation, grain, period, and set of exclusions, connected to its underlying model. Power BI, Looker, Tableau, and Hex serve different needs; we choose the tool and view around yours. Shared definitions carry across dashboards, so every team can trace the numbers back to the same logic.
We examine the segments, timing, source changes, and operating conditions behind a reported movement. That can mean separating an activity shift from a tracking change, comparing resource use across workloads, or examining the records behind a cost increase. A chart introduces the question. The investigation establishes which explanations the data can support.
Scientific analysis depends on more than a result column. We preserve sample and replicate grain, units, run identifiers, method versions, and source quality flags. Accepted readings and values held for review remain distinct. Researchers define method validity and scientific interpretation; the data model keeps that context available to the analysis.
An interaction can involve retrieval, model calls, tools, and retries. We connect those execution events to versioned tasks, feedback, expert assessments, latency, and attributed cost. Successful HTTP responses, completed tasks, and evaluated quality are separate measures. Research evaluations also need stable case identifiers, dataset versions, configuration records, and unresolved assessments that remain visible.
We explain what the evidence supports, where assumptions matter, and which questions remain open. Recommendations can shape an operating change, a capacity decision, a product investigation, or the next measurement to collect. Customer activity and financial analysis belong in that range as well; a usage signal can guide investigation without proving a cause or predicting an outcome.
What does your team need to decide, and which part of the current analysis makes that difficult?