Data you can actually query
Pipelines and models that turn scattered operational data into something a dashboard can answer from.
What you get
- ETL and ingestion pipelines
- Warehouse schema design
- Metric definitions and dashboards
- Data quality checks
- Reporting and export
What changes
- One definition of “active user” instead of three
- A question gets answered without an engineer writing SQL
- Bad data is caught at ingestion, not in a board deck
When teams call us
- Two teams reporting different numbers for the same metric
- Analytics that require a CSV export and a spreadsheet
- Operational data spread across systems that never join
Process
How the work runs
The same four phases whichever engagement model you pick, each ending in something you can read.
Feasibility
Whether a model genuinely beats deterministic code here, tested on your data before anyone commits a budget.
- Data review
- Baseline comparison
- Go/no-go in writing
Evaluation harness
Built before the feature, not after. Without a scoring set you cannot tell a prompt change from a regression.
- Golden test set
- Scoring criteria
- Regression runs
Build & tune
Retrieval, prompts, tools and guardrails developed against that harness, with cost and latency tracked per change.
- Versioned prompts
- Cost per request
- Latency budget
Ship & monitor
Live with logging of inputs, outputs and tool calls, so quality is observed rather than assumed.
- Output logging
- Drift alerts
- Spend caps
Not sure which service your project needs?
Most useful projects cross two or three of them. Describe the outcome and we will map it.