Backbone generates a production-ready data warehouse, semantic layer, and read-only Claude connector from your business ontology. So the AI tools your company wants to use have something clean and well-documented to work with.
A senior leader comes back from a conference. They've heard about agents that write pipelines, about Claude Code, about how AI is going to do the whole thing end to end. And by the way: why isn't your team already doing this?
So you smile. You say you'll look into it.
Then you go back to your desk. Back to the warehouse the last team built and the last team understood. Back to the three definitions of revenue that have coexisted in production for longer than anyone wants to admit. Back to the finance team reconciling in Excel because nobody trusts the numbers in the dashboard.
The pressure to do more with AI is real. The foundations that would make AI useful don't exist yet. And nobody in that conference room is going to help you build them.
Before
The Monday morning dashboard
You open the margin dashboard and spend 2 hours cross-checking against finance's spreadsheet before the CEO sees it. Last month the German entity was off by €180K because a cost file arrived late and the ETL silently loaded zeros.
After
The Monday morning dashboard
You open the dashboard at 9am and trust it. Margin is defined once in the ontology. When a cost file arrives late, last week's numbers don't change; this week's incorporate the correction with a full audit trail.
Before
“Which revenue?”
The CMO Slacks you: “Your dashboard says Q1 revenue is €34M. Finance says €31M. The board deck is due Friday.” You spend two days tracing the gap through 4 layers of logic. Both numbers are “right” by their own definitions. Neither is authoritative.
After
“Which revenue?”
Revenue is defined once, in the ontology. The warehouse, semantic layer, and every report are generated from that single definition. There is no second interpretation because there is no second place where revenue is calculated.
Before
“Why can't we use AI?”
The CEO has seen Claude query a database. “Why can't we do this?” You know why: your main table has 150 columns, 10 of which say “revenue.” If the LLM picks the wrong one, the CEO gets a confident, wrong answer. You say “we're working on it.”
After
“Why can't we use AI?”
Your team asks Claude through a read-only connector that Backbone generates. The semantic layer tells the LLM which column is net revenue, which is gross, which is marketing-attributed. The CEO asks a question and gets a correct, sourced answer.
Before
The acquisition
The company just acquired a brand. Different ERP, different chart of accounts, 20-year-old on-prem database. Your team estimates 4–6 months to integrate. Meanwhile 30% of group revenue is invisible.
After
The acquisition
You map the acquired entity against your existing business ontology. Same KPI definitions, same dashboards, live in weeks. The CEO sees consolidated group performance for the first time.
Define what decisions matter before a line of SQL gets written. Backbone's ontology layer captures your metrics, entities, and relationships. Starting from the business, not from tables.
The warehouse is generated from metadata. Not code your team writes and then has to maintain, debug, or explain to the next person. Data Vault foundations, integrated views, business vault. All generated.
The semantic layer and read-only Claude connector are generated automatically from the same ontology. Your AI tools get clean, documented context: definitions, lineage, source-of-truth. No separate 6-month project.
Data stays in your Snowflake. Code lives in your Git. No black box, no vendor lock-in. If you ever walk away, the warehouse stays. We're not a platform you depend on. We're a backbone you own.
Multiple ERPs, conflicting chart of accounts, different naming conventions. The ontology resolves integration at the business level, not the table level. Designed for consumer goods companies that grow by acquisition.
Definitions, rules and every ruling are versioned and traceable in your Git. Checks run automatically on every load; decisions stay with your people.
Implementation
From €20,000
We implement Backbone in your Snowflake account and deliver your first use case, usually in 3–4 weeks: a reconciled data warehouse, semantic layer and read-only Claude connector, generated from your business ontology. Everything it builds stays in your Snowflake and your Git. We're not selling the software on its own right now.
Forward-deployed engineers
~€1,500/day
Our engineers work alongside yours to get the foundations right, upskill the team, and sort out governance and definitions. And develop the solution further based on what you need. We know we're early.
The real comparison
3–6 months of custom data engineering to build what Backbone delivers in 3–4 weeks. Your team focuses on the 5% that's genuinely unique to your business, not the 95% that's standard.
That moment is coming. We work with a small number of scaling consumer goods companies at a time. Leave your work email and we'll be in touch to set up an intro call.
Built on 28 years of data platform experience across global FMCG, industrial, and retail. Dozens of builds. We encoded what works.