01://SCOPE
Best fit
Operations, finance, marketing, support, or product teams that have data but still rely on manual cleanup, lookup, reporting, or judgement work.
2://SERVICES
We turn data and AIinto systems that launch,transfer, and operate.
From strategy and data engineering to analytics models and AI applications, our services are structured as delivery modules that move decisions, systems, and team capability forward together.
02://SCOPE
First phase
A typical first phase uses 2–4 weeks for audit, source mapping, prototype, and acceptance criteria before expanding into a production system.
03://SCOPE
What to prepare
One decision or workflow to improve, the reports/documents/systems used today, and an internal user who can validate the first version.
2B://PACKAGES
Common first-phase packages
Turn an abstract need into a scoped first phase, ship something users can validate, then decide whether to expand.
01
2 weeksData Audit Sprint
Data is scattered and reporting is manual
- Source map
- Field and access audit
- Phase-one acceptance criteria
Client prepares
Discuss this packageCurrent reports, system list, core users
02
3–4 weeksDashboard MVP
Leadership or teams need a stable decision view
- KPI definition
- Interactive dashboard
- Runbook and handoff
Client prepares
Discuss this packageMetric definitions, sample data, meeting cadence
03
3–5 weeksRAG Knowledge Base
Documents, support, or internal knowledge lack approved answers
- Document cleanup
- Retrieval flow
- Sources and access rules
Client prepares
Discuss this packageDocument samples, common questions, user roles
04
4–6 weeksAI Workflow Pilot
A process has repeated judgement or review work
- Workflow design
- AI-assisted steps
- Monitoring and review logs
Client prepares
Discuss this packageWorkflow steps, edge cases, review rules
2C://ALIGNMENT
What we clarify before implementation
You do not need a complete spec first. We turn a rough need into a route that can be scoped, validated, and adopted.
Decision scene
Which decision, workflow, or workload should improve, and who will use the result daily?
Data state
Which systems, sheets, or documents hold the data, and what fields, permissions, and refresh cadence matter?
Implementation route
Should phase one start with audit, dashboard, RAG, workflow automation, or a prototype?
Acceptance and handoff
How do we know phase one worked, and who owns operation, maintenance, and iteration?
3://SERVICE PROCESS
This is not a service list. It is an implementation path.
01
Audit
Read the current state, data gaps, tooling, and team constraints.
02
Prototype
Build an AI or data prototype real users can try.
03
Launch
Turn the prototype into an operable workflow.