Data & analytics consulting

Your data stack is holding you back.
We fix that.

Clean, scalable data foundations on Snowflake, dbt, Databricks, and GCP. So leaders can move faster with confidence.

Pipeline health · live

healthy

0.00%

uptime, last 30d

0

sources

0

models

0

tests

32+ projects delivered
$2.3M+ saved in infra costs
10+ years in data
Snowflake partners
dbt practitioners
Databricks-certified
EU & US clients
Fixed-scope or retainer
32+ projects delivered
$2.3M+ saved in infra costs
10+ years in data
Snowflake partners
dbt practitioners
Databricks-certified
EU & US clients
Fixed-scope or retainer

Trusted by teams at

FinSights AIeMarketerSecureW2BoldspaceDigital Financial OfficersShotVetBoldstreamEyeful

Challenges

Which of these sounds like you?

Every company hits a wall. Click in — we'll tell you honestly what it takes to break through.

01

very common

We have no real data platform

You're running on spreadsheets, manual exports, and dashboards held together by hope.

  • Your team relies heavily on Excel and Google Sheets
  • Analysts spend more time gathering data than analyzing it
  • Dashboards are manually updated every week (or month)
  • + 3 more symptoms
Start from zero

02

widespread

Our architecture is a mess

Cron jobs, half-written scripts, pipelines built by whoever was available. Every new source causes a fire.

  • Pipelines spread across Fivetran, custom scripts, and cron jobs
  • Models duplicated across multiple BI tools
  • KPIs defined differently by each team
  • + 3 more symptoms
Untangle it

03

common

We don't have a data team

Your business is growing faster than your data function. Analysts are doing engineering work.

  • No dedicated data engineer or analytics engineer
  • Business analysts doing manual data work
  • Custom dashboards built per stakeholder request
  • + 3 more symptoms
Be my team

04

recurring

Our dashboards don't match

Marketing, finance, and ops all have different numbers. Every meeting starts with 'which report is right?'

  • Different revenue numbers in every meeting
  • CAC, LTV, and MRR calculated differently across tools
  • No single definition of 'active user' or 'customer'
  • + 3 more symptoms
Settle the debate

05

emerging

We need customer-facing analytics

Your product needs embedded dashboards, but your internal data isn't strong enough to power them.

  • Customer-facing reporting manually built for each client
  • Onboarding new customers involves sheets and scripts
  • No scalable multi-tenant data structure
  • + 3 more symptoms
Ship it to customers

06

emerging

We want AI, but our data isn't ready

Leadership wants LLMs and automation. But the data is inconsistent, unstructured, and unreliable.

  • LLM prototypes that produce incorrect outputs
  • No vector store or semantic search capability
  • Pipeline quality too low to trust for automation
  • + 3 more symptoms
Make it AI-ready

By the numbers

0+

Projects delivered across EU & US

$0.0M+

Saved in client infrastructure costs

0+yrs

Data engineering experience on every engagement

How we work

Four steps. No mystery.

  1. 01

    Discovery call

    We diagnose your challenges in 30 minutes.

  2. 02

    Architecture review

    Map your current state. Design the target state.

  3. 03

    Implementation

    We build it. Pipelines, models, dashboards.

  4. 04

    Handoff

    Documentation, training, and ongoing support.

Not sure which fits?

30 minutes. We'll tell you honestlywhat's broken.