Databox for AI implementers
The agentic analytics platform for AI implementers
Build AI systems on a foundation you can govern
Trusted by teams behind 20,000+ businesses
Stop rebuilding the foundation behind every AI system
The system you build is only as reliable as the data, definitions, and context underneath it.
- Every implementation starts from scratch
- Two systems calculate the same metric differently
- The model fills gaps with plausible assumptions
- Every system you ship adds logic you maintain separately
- You need a data warehouse just to analyze with AI
With Databox
- Connect once, reuse the foundation for every system
- One governed definition — the same number, everywhere
- Metric definitions and context reach the model through MCP
- Systems share one foundation — a definition changes once
- Databox stores your data — no warehouse required
- With Databox from anywhere
How it works
Set up the foundation once, then build everything on it
Every system you build starts from the same connected data, governed metrics, and business context.
Steps
Step 1 of 5
Connect each client
Bring each client’s performance data into its own account, using a setup that already works.
130+ integrations
Plus spreadsheets, warehouses, and the Databox API.
Account templates
Apply a proven setup to the next client in minutes.
Separate per client
Each client’s data lives in its own account, access controlled per person.
What “governed” means here
The foundation is governed before the first system touches it
Governance is what makes the answers right by the client’s own definitions, in every system you build.
A single source of truth
The semantic layer holds the full definition, the owner is accountable, and verification shows which version the business trusts.
Context lives next to the data
Goals, plans, and performance history are stored with the metrics — so systems reason from how the business actually works.
Every system inherits the same analysis
Comparisons, trends, correlations, forecasts, and anomaly detection are built into the foundation.
You control what each system can reach
Access to metrics, datasets, dashboards, and reports is controlled per person and per client.
Inherited by every system you build
Across your AI stack
One governed foundation, every system you build
AI agents. Act on trusted data
Agents you build reach trusted performance data, metric definitions, and client context through Databox MCP, and act on numbers the client already agreed to.
- Trusted data
- Client context
- MCP access
AI assistants. Answer with the same numbers
Claude and ChatGPT answer from the client’s governed definitions, so the assistant you configure gives the same numbers as the dashboards you deliver.
- Same definitions
- Same numbers
- Same context
Automations. Automate with consistent inputs
Recurring workflows in n8n or your own stack run on the same metrics and context, so automation output matches everything else the client sees.
- Recurring workflows
- Governed inputs
- Consistent output
Set up one client’s foundation and point your first system at it.
Try it free - no credit card needed. Book a demo - see the foundation behind a working implementation.
Implementer stories
Systems already running on this foundation
“Our first-party data told us to target a different region than the analytics platform did. Same model, different context, opposite recommendation. That’s the whole reason we grounded everything in Databox.”
Nian Wetsteijn
Co-Founder, FunnelView
Alternatives
Compare Databox to other ways to build the foundation behind AI
Each alternative gets you part of the way.
Databox is the governed foundation underneath everything you build.
| What's included | What's on you | |
|---|---|---|
| Databox | Connected business data, governed metrics, business context, built-in analysis, and MCP access | Connecting and defining each client’s metrics and context, plus, the use case itself |
| Individual APIs / MCP servers | Access to single tools and data sources | The cross-source foundation, shared metric logic, governance, and analytical capabilities |
| Warehouse + Semantic layer | Centralized data and governed definitions | Source ingestion, the business context, the AI experiences and workflows, and ongoing technical maintenance |
| General AI tools | Reasoning, connectors, and agent capabilities | Every connection, every metric definition, and the client’s context, rebuilt per project and maintained by hand, plus the governance that keeps the systems you ship agreeing on the same numbers |
Governance and control
The foundation stays governed as more systems use it
Permissions
Control who can view and work with each metric, dataset, dashboard, and report, per client account.
Ownership and verification
Assign owners and mark official assets as verified so the trusted version is clear.
Activity history
Track governance changes so you can see who changed or verified an asset and when.
Security
Databox is SOC 2 certified and GDPR compliant, with access controls and encryption protecting client data.
The implementation work is billable, and the platform pays you too
Put clients on Databox through the Solutions Partner Program and earn recurring commission, while the setup and governance work is billable implementation.
Recurring commission
Resell Databox and keep the margin
Put your clients on Databox through the Solutions Partner Program and earn recurring commission on every subscription.
- A simple toggle gives a client their own access and collects their billing information.
- 30% commission for the lifetime of that client’s subscription.
- Your own team stays on unlimited users - reselling changes nothing on your plan.
“We run 55 agents and 51 skills across 25 brands in 30 markets. When the system spots a risk or an opportunity it creates a task for a person, and then it checks back to see whether finishing that task moved the metric it was supposed to move.”
Founder & CEO, Arcanian
Billable onboarding
Set up and train clients on the system
Configure each client’s metrics, goals, and context, then teach their team to use it. The implementation work is billable and gives you access to their sales, financial and operational performance too.
- Billable setup: metrics, goals, and business context per client.
- Account templates let you apply a setup that already works to the next client.
- Visibility beyond marketing - sales, financial, and operational performance.
“Our content system pulls context from our own knowledge base and performance data from Databox to decide what to publish, what to optimize, and what to promote.”
VP Revenue Operations, Modgility
Billable onboarding
Build systems on top of Databox
Using MCP, the API, and Databox’s governed data, agencies build AI systems that automate their clients’ work and improve their clients’ results faster. Productize your services or bill for implementation.
- Databox MCP, the API, and the CLI on top of governed metric definitions.
- Productize what you build, or bill for the implementation.
- Compare notes with other builders in the AI Implementers Mastermind.
“Our first-party data told us to target a different region than the analytics platform did. Same model, different context, opposite recommendation. That’s the whole reason we grounded everything in Databox.”
Co-Founder, Managing Partner, FunnelView
Point your first system at a governed foundation today
Connect one client, define their metrics and context, and expose it through Databox MCP.
Build for clients? Get paid for the work too
Join the Solutions Partner Program for recurring commission, or the AI Implementers Mastermind to compare builds with other implementers.
Learn more
Frequently asked questions
How does Databox MCP work?
Databox MCP connects Claude, ChatGPT, Cursor, n8n, and other MCP-compatible tools to the metrics and business logic in Databox. Systems work with governed performance data, with the definitions and context supplied.
Do I need a data warehouse?
No. Databox connects directly to cloud tools, spreadsheets, APIs, databases, and custom sources, and to warehouses where clients have them.
Can I use my own metrics and business logic?
Yes. Start from prebuilt metrics or create your own datasets, calculations, and definitions to reflect how each client measures performance. Ownership and verification keep the trusted versions clear.
How is Databox different from using APIs or MCP servers directly?
With MCP servers, it’s difficult to control how much historical data gets pulled, how metrics are calculated, and how data gets aggregated. With APIs, you have to understand how the data comes back, whether it needs pagination, and how the pulls are rate-limited. Databox handles this complexity and stores the data, calculated consistently and accurately.
How is Databox different from a semantic layer?
A governed semantic layer is one part of Databox. The platform also connects the underlying data and provides analysis, reporting, Skills, Routines, and MCP access on top of that layer.
Can I keep client environments separate?
Yes. Client accounts keep each client’s data, definitions, and context separate, with access controlled per person. Account templates carry a proven setup to the next client.
Why does governance matter if the model is already good at analysis?
The model still needs to know what a metric means, how it’s calculated, which definition the business trusts, and what context applies. Governance supplies those rules; ungoverned, the model infers them, differently each time.
I want to build AI services for clients. Where do I start?
The AI Implementers Mastermind is where agencies and consultants compare what they’ve built and what clients pay for. For a specific build, request a consultation.