Modular Products

Claude Code, connected to your data stack. In 1 day.

i-spark configures Claude Code into your dbt and Power BI environment, governed from the start, tested against your actual setup, and handed over so your whole team can use it from day one.

For those that don’t have Claude Code connected to their data stack, i-spark configures it properly, governed from the start, and hands it over in one day.

Most teams know Claude Code exists.

A quick local install produces generic output — DAX that references tables you don’t have, models that don’t know your sources exist. Someone with deep technical knowledge ends up editing AI drafts rather than being unblocked by them. The tool adds a step instead of removing one.

Why do you need a proper Claude Code setup?

Getting it properly connected is important.

Without a proper setup

Claude Code guesses at your model. 

And here's the thing, it guesses pretty well. The output looks completely fine. It looks like something a competent engineer wrote on a Tuesday afternoon. So you take it, you try to use it, and then it starts breaking. Table names that don't exist in your project. A date table structure that doesn't match yours.

And you're sitting there thinking, okay, who has to fix this? It's the one person on the team who knows the codebase well enough to spot what's wrong, the same person who could have written it correctly in the first place.

With a proper setup

Claude Code reads your project before it generates anything.

It knows your tables, your sources, the relationships you've defined, and the tests you've already written. So when you ask it to generate a new staging model, it's looking at what you've already built and working within that.  Same with DAX. A measure for revenue year-over-year will reference your actual date table, your actual sales table, the relationship between them as it exists in your model. The output is specific.

And when something is specific, people across the team can read it, verify it, and use it.

Configured for your team

Tested against your actual models

Not a generic install. The configuration is validated against your project before handover. You don’t inherit a setup that works in theory.

Governed from the start

Credentials stored securely, rotation documented, ownership assigned. The Power BI connection is more credentials-sensitive than most teams expect, we handle this at setup, not as an afterthought.

Documented so everyone can use it

The MCP server configuration lives in the repository alongside everything else. The next person who joins knows how to use it. No tribal knowledge required.

Full handover, no dependency

You end the day with a walkthrough. The setup is yours, there is no ongoing dependency on i-spark. You own it completely.

Once it's properly wired in,
the whole team benefits

The connection changes what’s possible for everyone on the team, 

not just the person who built the setup. Here’s what it unlocks, per product.

  • 01. Write DAX that fits your actual model. Anyone on the team can describe what they need in plain language and get a working first draft back — DAX that references your actual tables, your actual relationships, your actual date table.
  • 02. Understand measures nobody remembers building. Complex nested calculations written by someone who left, no comments, no documentation. Claude Code reads the expression and explains what it does, step by step.
  • 03. Write Power Query M without memorising the syntax. Describe the transformation in plain language. Get working M code back — null handling, edge cases and all.
  • 04. Plan data models from reporting requirements. Describe what you need — dimensions, aggregations, time intelligence. Get a suggested table structure and relationship design before you build.
  • 05. Document your dataset systematically. Claude Code reads the DAX expressions across your dataset and generates descriptions for measures and columns — covering in hours what would otherwise sit undone indefinitely.
  • 01. Build new models in minutes, not hours. Describe a new source once. Get a working draft of the SQL, YAML, tests, and documentation.
  • 02. Refactor accumulated technical debt. Claude Code reads an existing model and restructures it into clean, modular dbt — CTEs split properly, naming conventions applied, logic that should be a ref flagged as such.
  • 03. Debug failing runs with context. Describe the error in plain language. The diagnosis accounts for your actual model structure — not a generic error lookup.
  • 04. Catch up documentation at scale. Undocumented dbt models are one of the most common pain points in data teams. Claude Code works through existing models systematically — reading what’s there and generating descriptions across the project.
  • 05. Answer questions about your data model in plain language. Which models depend on this source? What tests exist on this column? Where is this metric calculated? No grep, no lineage graphs.

Testimonials

See what our happy customers say

I have been working pleasantly with i-spark for quite some time. The combination of thinking along and practical application unburdens me and is exactly what I am looking for in a partner.

Inger Clancy

Sdu

The expertise of i-spark is a working and welcome addition to that of our teams at ID&T.

Michael Guntenaar

ID&T

Thanks to i-spark’s expertise, our data is no longer a by-product; it’s a growth engine. We now enrich our internal systems, optimise operations, and inform customs authorities with ease.

Data team

WWL

Book the intake call. We'll confirm fit before anything else.