Data Foundation
If your data isn't ready, your AI isn't either.
Pragmatic data engineering scoped to what AI actually needs, not full enterprise modernization.
In plain English.
We get your data into the shape your AI use cases actually require, meaning clean, connected, and queryable, without a two-year enterprise data-warehouse project. We scope the work backward from the specific use cases on your roadmap, so you build exactly what's needed and nothing you don't.
That usually means consolidating sources into Snowflake or BigQuery, wiring reliable pipelines with tools like Segment or RudderStack, cleaning up CRM data in Salesforce or HubSpot, and modeling it so retrieval and analytics are fast and trustworthy.
The result is a foundation that makes every downstream AI effort (RAG, agents, attribution, analytics) cheaper, faster, and more accurate, because the model is finally working from data it can rely on.
When you need this
- Your AI or analytics efforts keep stalling on messy, scattered data.
- A readiness assessment flagged data as your top blocker.
- Data lives in silos and nothing can answer questions across them.
- You need clean, connected data before a RAG or attribution project.
The deliverables, plainly stated.
- Source consolidation into Snowflake or BigQuery
- Reliable ingestion pipelines via Segment or RudderStack
- CRM data cleanup and modeling in Salesforce or HubSpot
- Data models tuned for AI retrieval and analytics
- Data-quality checks and basic observability
- Documentation so your team can extend it
Typical duration
3 to 6 weeks
Investment band
$$$Significant investment
We scope in bands, not fixed numbers. Final pricing follows a quick scoping call.
A process built for this service, not a generic playbook.
- 01
Scope to the use cases
We start from the AI use cases on your roadmap and define exactly what data each one needs, and no more.
- 02
Consolidate and ingest
We bring sources together in Snowflake or BigQuery and build reliable pipelines with Segment or RudderStack.
- 03
Clean and model
We resolve CRM data quality in Salesforce or HubSpot and model the data for fast, trustworthy retrieval and analytics.
- 04
Validate and document
We add data-quality checks and observability, then document the foundation so your team can build on it.
Team composition
A lead data engineer and an analytics engineer, with a solutions architect aligning the foundation to upcoming AI use cases.
Tools & frameworks
- Snowflake and BigQuery as the warehouse
- Segment and RudderStack for ingestion
- dbt-style modeling for transformation
- Salesforce and HubSpot as primary CRM sources
What we tie this engagement to.
Every engagement carries a revenue-tied KPI. These are the outcomes this service typically anchors on.
Clean, connected data ready for AI use cases
Faster, cheaper, more accurate downstream AI projects
A documented foundation your team can extend
Works with your stack
We deliver Data Foundation inside the tools you already run.
Data Foundation: common questions
What is a data foundation engagement?
It's pragmatic data engineering that gets your data clean, connected, and queryable for specific AI use cases. That typically means consolidating sources into Snowflake or BigQuery, wiring pipelines with Segment or RudderStack, and cleaning CRM data.
Is this a full data-warehouse modernization?
No. We deliberately scope the work backward from the AI use cases on your roadmap, so you build exactly the foundation those use cases need rather than a multi-year enterprise modernization.
Why does AI need a data foundation?
AI is only as good as the data it works from. Clean, connected, well-modeled data makes RAG, agents, attribution, and analytics cheaper, faster, and more accurate, and it prevents the stalls that messy data causes.
How long does it take?
Typically 3 to 6 weeks depending on how many sources need consolidating and how much CRM cleanup is required, ending with validated pipelines and documentation your team owns.
Which tools do you use?
We build on Snowflake or BigQuery for the warehouse, Segment or RudderStack for ingestion, dbt-style modeling for transformation, and connect primary sources like Salesforce, HubSpot, and Postgres.
Often paired with this.
Ready to put Data Foundation to work?
Tell us where you are and we'll tell you what's blocking revenue.