Unify your data.
Your data is your biggest asset when used correctly.
A single data warehouse that consolidates your current systems. Easily access information from your ERP, CRM, and everything else in one location.
Built for the people in your business who need clean reports, and the AI agents that need clean data. Cloud, your cloud, or on-premise.
Fixed fee + monthly · 8-12 weeks
The pain you know
Your data is stored across multiple systems, and no one knows which one is correct.
A manager or analyst carries the weight: up early on the first Monday of the month, pulling numbers by hand, reconciling, and formatting. The first twenty minutes of every leadership meeting are spent debating whether the numbers are right.
AI pilots fail on top of this for the same reason. That's what Foundation fixes.
What it does
One source of truth. Two consumers. Humans first.
Foundation is a single data warehouse that continuously pulls from your existing systems and normalizes everything into a clean model. Two consumers built in from day one.
First, the humans in your business: the controller who needs the rollup, the analyst who runs the weekly review, the ops manager who answers the follow-up question in the meeting, the executive who wants to know how the quarter is shaping up.
Second, the AI: agents and assistants that need consistent, well-modeled data to behave reliably.
Humans first matters here. A lot of data infrastructure ends up serving dashboards for executives and not much else. Foundation is built so that the controller and analyst who actually work with the data get capacity back. Once the data layer serves your team, AI agents can run on top of it. The order is deliberate.
What gets connected
Read-only connections to your existing systems. Your team keeps working with the tools they already know.
Manufacturing
ERP (NetSuite, Microsoft Dynamics, Epicor, SAP Business One) plus MES, quality systems, CRM, and shop-floor data.
Construction
Procore plus Sage, QuickBooks, Vista, or Foundation. Field apps. Payroll. Subcontractor portals.
Healthcare
EHR, practice management, billing, scheduling, and the clearinghouse. Patient experience platforms where they exist.
Legal
Matter management, time and billing, accounting, document management, and CRM. iManage, NetDocuments, Aderant, Elite, Clio.
If your stack isn't on the list, it's almost certainly something we've worked with before or something that exposes a standard integration pattern. We confirm in the Audit.
The normalized data model
Every Foundation engagement ships with a normalized data model purpose-built for your industry and your operation. Whichever entities matter to your business get modeled cleanly with explicit lineage back to the source.
Built for your workforce, not just your dashboards
Discovery starts with the people doing the work, not just the executives reading the report. The controller knows where the reconciliation breaks. The analyst knows which join nobody else can write. The ops manager knows the spreadsheet that's been holding the rollup together for three years. We start there.
The model that gets built makes your team more capable, not less needed. Your controller learns to query the warehouse. Your analyst builds new dashboards. Your BI authors point Tableau or Power BI at clean, documented data and own the reporting layer themselves. Data fluency grows across the team, not just at the C-suite. That's the deal: workers gain capability, the business gains visibility, and the deployment ships better because the people who know the work are in the room while it's built.
What Foundation isn't
Foundation isn't a BI tool. It doesn't replace Tableau, Power BI, or Looker. It feeds them. The point is that the numbers in the BI tool reconcile because they're coming from a single, normalized source.
Foundation isn't a data lake. A data lake collects everything and figures out the schema later. Foundation models the schema first, then pipes the data through it. The difference matters when your users care whether "revenue" means the same thing in two reports.
Foundation isn't an ETL vendor like Fivetran or Stitch. Those tools move data from source to destination on a schedule and stop there. Foundation includes the destination, the normalized model, the multi-entity logic, and the AI-readiness layer.
Deployment options
Three shapes. You choose.
This is where Foundation matters most to regulated buyers. Three deployment shapes, picked by your data sensitivity and your existing infrastructure choices.
Cloud
Snowflake, BigQuery, or Databricks managed by us in our environment, or set up in your accounts if you've already standardized on one.
Fast to spin up, fully managed, low operational overhead. Most non-regulated mid-market deployments land here. Suitable when your data sensitivity is standard business confidentiality, not regulated PHI or privileged client data.
Your cloud (VPC)
Foundation runs inside your AWS, Azure, or GCP account. Warehouse, ETL pipelines, modeling layer, and orchestration all sit in your environment.
Your IT team has full visibility. Your data never leaves your boundary. Most PE-backed platforms, healthcare groups, and growing manufacturers land here. It's the deployment shape that scales to acquisitions without becoming a compliance question every time.
On-Premise
Foundation runs on your servers. Warehouse is PostgreSQL, ClickHouse, or a similar self-hosted option. ETL runs on-prem. Modeling happens locally.
Nothing leaves your network. This is the deployment shape for the most sensitive workloads: legal client data with privilege concerns, healthcare PHI in air-gapped environments, IP-sensitive manufacturing where pricing and supplier data can't sit in a vendor cloud.
How it works
Four phases. Audit. Build. Adopt. Run.
Same four-phase model every Databender engagement runs on. Foundation is the longest because the modeling work is real. Eight to twelve weeks isn't padding. It's how long it takes to get the data right.
Audit
2 weeks · Fixed fee
Forward-deployed diagnostic. A Databender works alongside your leadership team and your operators to map every source system, validate access, document the entities and metrics that matter, identify the multi-entity dimensions (locations, portcos, channels), confirm the deployment shape, and rank the workflows that get unblocked first. The Audit report tells you whether Foundation fits, what it'll cost, and which downstream products become viable once the data layer is in place.
Implementation
8-12 weeks · Fixed fee
Source integrations get built. The warehouse gets stood up in your chosen environment. The normalized data model gets defined, reviewed with your team, and populated. ETL pipelines run continuously. Data quality checks fire on every load. By week six, your controller and one or two power users start querying the warehouse directly. By week ten, reports start migrating off spreadsheets. By week twelve, the system runs in production with named owners on your team.
Adopt
2-3 weeks · Included
The warehouse is the substrate, not the user interface, so adoption looks different from Search or Agents. We work alongside your analysts and controllers until they own the data model. Your BI authors learn to point Tableau, Power BI, or Looker at the warehouse and build the reports your business actually asks for. Your finance team retires the spreadsheets that the warehouse now answers. The Adopt phase ends with your team holding the keys, not us.
Run
Ongoing · Monthly run fee
Continuous ETL. Continuous monitoring of data quality, freshness, and lineage. Schema changes in source systems get caught and propagated. New entities (locations, portcos, product lines) are modeled. Same-day fixes for production issues. Your team owns day-to-day analytics. We own the substrate.
What you own
When we're done, it's yours.
The warehouse and every database object inside it
The normalized data model, fully documented
ETL and pipeline code
Source integration code
Data quality rules and tests
Lineage documentation
The orchestration setup
Deployment runbooks for your environment
Access control mappings
Monitoring dashboards
The query library your team builds on top
If you bring this in-house in three years, your team has the code, the docs, and the runbooks to keep it running without us.
The choice of Snowflake, BigQuery, or on-prem PostgreSQL is yours. The choice between dbt, SQLMesh, and a custom orchestrator is yours. We have opinions, and we share them in the Audit, but the result is your stack, in your name, on your bills.
Pricing
Three tiers. Cloud or on-prem at every tier.
Tightly scoped. Add-ons priced transparently for the cases where customers genuinely want more. The Audit determines which tier fits.
Foundation Starter
$20,000 cloud · $27,500 on-prem · 4-6 weeks
Best for: Businesses running on a small stack that want clean reporting and one source of truth, without the overhead of a full enterprise warehouse engagement.
Includes. 2 source systems, 10-15 normalized entities, 1 warehouse setup (MotherDuck or DuckDB for cloud; PostgreSQL or ClickHouse for on-prem), ETL pipelines with continuous sync, 5 data quality rules per source, 3 reports or dashboards, BI tool integration (Evidence, Metabase, Streamlit, Power BI, Tableau, or Looker), 1 week of Adopt with 2 training sessions, and the first 60 days of Run included.
Foundation Standard
$50,000 cloud · $65,000 on-prem · 8-10 weeks
Best for: The typical Foundation engagement. Most mid-market deployments land here.
Includes. Everything in Starter, plus: 5 source systems total, 25-35 normalized entities, 1 multi-entity dimension (location, portco, channel, etc.), 8 data quality rules per source, 10 reports or dashboards, and 2 weeks of Adopt with 5 training sessions.
Foundation Enterprise
$85,000 cloud · $105,000 on-prem · 12-14 weeks
Best for: Complex multi-site businesses, PE platforms, regulated industries, or any organization where the data model needs to support multiple entities, channels, or contract structures.
Includes. Everything in Standard, plus: 7 source systems total, 40-60 normalized entities, 3+ multi-entity dimensions, layered pricing modeling, 12+ data quality rules per source, 15 reports or dashboards, 3 weeks of Adopt with 8 training sessions, and a full readout cycle with revision rounds.
Run · Monthly
After delivery, Run covers continuous ETL and pipeline operations, data quality monitoring, schema-change handling, ongoing model evolution, and same-day fixes for production issues. Cloud infrastructure costs are passed through at cost.
- Starter: $2,000/mo cloud · $2,500/mo on-prem
- Standard: $2,500/mo cloud · $3,000/mo on-prem
- Enterprise: $4,000/mo cloud · $5,000/mo on-prem
Foundation is the base most other Databender products build on. Add Insights, Search, or Agents and the Platform Bundle lowers setup and Run across the whole stack, with a further drop for a longer Run term.
Common add-ons
Tight tiers are the headline. Add-on prices are transparent so you know the cost upfront, no negotiation.
| Add-on | Cloud | On-prem |
|---|---|---|
| Additional source system | $5,000 | $6,500 |
| Multi-entity dimension setup | $7,500 | $9,000 |
| Layered pricing modeling | $10,000 | $12,500 |
| Custom data mart | $3,500 | $4,000 |
| Report bundle (5 reports) | $3,500 | $3,500 |
| Historical data backfill (per source) | $2,000 | $2,500 |
| Extended data quality (anomaly detection) | $5,000 | $6,000 |
| Additional Adopt week | $3,500 | $3,500 |
| Acquisition integration (new entity, single source) | $7,500 | $9,000 |
Per-add-on monthly Run impact: each additional source adds $500/mo, each multi-entity dimension adds $500/mo, layered pricing adds $1,000/mo. Reports, marts, and training don't affect Run.
Foundation pairs naturally with Insights for automated reporting and with Agents for production AI workflows. Most deployments add at least one downstream product within six months because the data is finally ready for it.
The Audit confirms which tier fits, the final scope, and the deployment shape.
Common Questions
FAQ
Foundation solves it once. Every product downstream gets sharper.
Every tool your team has tried gave wrong answers because the data wasn't ready. Imagine every system pulling from one trusted source.