AI Accelerator
AI-Ready Data
Build the walking skeleton your AI and analytics actually need.
At a glance
- 6–10 weeks
- Fixed-fee build
- Foundational
- Maturity stage
- Working pipeline
- What you leave with
Scaled to domain complexity
Underpins AI-Enhanced & AI-Agentic
For one real domain
The problem
AI and analytics initiatives keep stalling on the same root cause: fragmented source systems, no canonical entities, inconsistent keys across platforms, and no lineage. Teams end up rebuilding the same joins and cleanup logic project after project.
What it is
A fixed-scope data-platform build using a “walking skeleton” approach — a thin, working, end-to-end pipeline built fast and then thickened — rather than a big-bang platform rebuild.
Scope
What's included
Canonical entities & ontology
Entity and ontology design for your priority domains.
Medallion pipeline
A bronze → silver → gold, lakehouse-style pipeline for a defined data slice.
Keys & lineage
Cross-system key management and lineage tooling you can trust.
Docs & runbook
Documentation and a handoff runbook so your team can operate it.
The next slice
A recommended next domain for expansion, sequenced and scoped.
Who it's for
Organizations with data spread across multiple systems — CRM, ERP, POS, ticketing — who need a trustworthy foundation before layering on AI, agents, or advanced analytics.
Why Baufest
Why Baufest
Modern data architecture
Real depth in medallion and lakehouse patterns — core work, not a bolt-on.
Skeleton in weeks
Pragmatic, incremental delivery: a working slice in weeks, not a year-long platform program.
Built to extend
The first domain proves a pattern designed to thicken into a full platform.
Proof point
Modeled on a data-ontology and walking-skeleton engagement delivered under a fractional-principal model for a data-heavy client.
FAQ
Why not just rebuild the whole platform?
Because big-bang rebuilds stall. A walking skeleton proves the end-to-end pattern on one real domain first, so you commit to the full build with evidence and a working reference.
How do you pick the first domain?
We start where the pain and the value concentrate — the domain whose fragmented data is blocking the most AI or analytics work today.
What do we have at the end?
A working, documented pipeline for one real domain — canonical entities, medallion layers, keys, and lineage — plus a scoped next slice to expand into.
Start with one domain.
Pick the domain whose data is blocking your AI and analytics work. We’ll stand up a working skeleton — and a plan to thicken it.