Teams with a specific customer, product, operations, or growth question
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Custom Data Analytics
Applied analytics for businesses that need clean models, useful segmentation, measurable funnels, and decision-grade reporting.
Engagement shape
A bounded analytics engagement that clarifies the business question, prepares the relevant data, and delivers usable analysis with an implementation path.
Typical timeline
Typically 2-4 weeks for a focused question with available source data.
Who it is for
Designed for teams with a concrete operating problem.
Organizations that need applied analytics before investing in a larger platform effort
Operators who need analysis that connects directly to decisions and workflow changes
Deliverables
Concrete artifacts, not vague advisory output.
Analytical dataset, assumptions, and data-quality notes
Segmentation, funnel, cohort, performance, or operational analysis
Decision summary with recommended workflow, reporting, or platform changes
Outcomes
What this work should leave behind.
Clean data models for customer, product, and operational analysis
We keep the deliverable tied to operating use: records people can own, workflows people can inspect, and technical contracts agents can use safely.
Segmentation and funnel views that teams can act on
We keep the deliverable tied to operating use: records people can own, workflows people can inspect, and technical contracts agents can use safely.
Clear handoff between analytical insight and operational workflow
We keep the deliverable tied to operating use: records people can own, workflows people can inspect, and technical contracts agents can use safely.
Related Lab Notes
Relevant thinking from the platform work.
AI-ready data foundations
Connect warehouses, metrics, and operational records so AI agents and operators can use trustworthy business context.
Custom analytics should create operating leverage
Custom analytics is most valuable when it changes a decision, workflow, segment, or follow-up path.
Data quality is an operating problem
Data quality improves when ownership, workflow, validation, and reporting expectations are designed together.
BI needs a clear metrics contract
Why BI dashboards need metric definitions, ownership, source assumptions, and governance before teams can trust reporting.
Slab5 beta
Give your business workflows a governed operating layer.
Start with one real operating flow: records, REST APIs, MCP access where enabled, AgentGrid approvals, audit logs, and the context business operators need to trust the work.