Data & Analytics
Analytics is only as good as the data underneath it. We build the pipelines, models, quality controls, and infrastructure that make numbers trustworthy and fast to use.
Analytics firms, internal data teams, and data-driven businesses all hit the same wall eventually. Pipelines break silently, metrics are defined three different ways, and analysts spend more time cleaning data than analyzing it. Adding AI on top of untrusted data only makes the problem faster.
We design and build data foundations, including schemas, ETL and ELT pipelines, validation and reconciliation checks, and documented metric definitions, so every report draws from a single source of truth. Telemetry and event tracking capture product and customer behavior consistently from the start.
On top of a trusted foundation, AI becomes useful. Retrieval and agent workflows can answer questions about the data, draft reports, and monitor for anomalies, with evaluations to prove accuracy. Infrastructure as code and observability keep pipelines reliable and costs predictable.
What makes data & analytics different
Silent pipeline failures
Broken imports and schema changes corrupt reports without anyone noticing until decisions go wrong.
Conflicting metrics
The same metric defined differently in several places erodes trust in every number.
Time lost to cleanup
Analysts spend much of their time preparing data instead of analyzing it.
AI on shaky foundations
AI tools amplify bad data, so quality and evaluation must come first.
How we help data & analytics businesses
AI on top of trusted data
Question answering, report drafting, and anomaly monitoring with governed AI agents built on validated data, with evaluations proving accuracy.
See how it works →DATAData pipelines and warehouses
Reliable pipelines, documented models, and validation and reconciliation checks that make analytics trustworthy for data teams and analytics firms.
See how it works →INFOFractional data leadership
Senior direction for data strategy, platform decisions, team structure, and vendor choices for companies building or rescuing analytics.
See how it works →DATAProduct and customer event data
Design and implement event tracking that produces clean, consistent behavioral data for analysis, experiments, and AI.
See how it works →Frequently asked questions
Can you rebuild our pipelines without downtime?
Usually. We run new pipelines alongside existing ones and reconcile results before switching over.
Do you work with our warehouse?
Yes. We design around the warehouse and tools you already use, or recommend options when you are starting fresh.
Can AI answer questions about our data?
Yes, once the data is reliable. We build question-answering with access controls and measure accuracy with evaluations.
Let’s talk about your data & analytics business.
Tell us what needs to work better. We reply within one business day with a clear next step.