Metrics & Trust
Pipelines & Freshness
Data Quality
Org & Process
Services
We diagnose the system, fix the foundations, and help your team operate on numbers they can trust.
Primary focus
Go beyond the default install: custom charts, tighter permissions, embedded experiences, and behavior that matches how your org actually reviews numbers.
Primary focus
Rebuild the dashboards and semantics that matter on an open stack you control — whether you’re on Tableau, Power BI, Qlik, Looker or another closed platform: fewer license surprises, clearer lineage, one place for self-serve BI.
We rebuild pipelines so they don't silently fail — with clear ownership, recoverability and predictable behavior. When the backlog is the bottleneck, we design code-generation platforms so analysts can ship jobs themselves, and we work embedded with your team to land changes without handoff delays.
One definition of metrics across the system — no conflicting dashboards, no debates about numbers. We audit the stack end-to-end to find where numbers diverge and where the logic breaks, then leave a clean semantic layer teams can trust.
Superset fork
Every tile below is a feature we've shipped on top of Apache Superset 4.1. Click any to open the deep-dive.
Explore all featuresAudience
Some already have a modern stack that no one trusts. Others are still running critical decisions through spreadsheets that the business has outgrown.
Broken systems
Pipelines & BI gapsYou have pipelines, dashboards, BI tools, and a warehouse — but the system underneath them is unreliable.
If reporting meetings keep turning into debates, the issue is no longer visibility. It is system reliability.
Spreadsheet ceiling
Exports & reconciliationExcel got the business this far, but manual reporting and disconnected files are now slowing the company down.
Spreadsheets feel cheap until they become infrastructure. Then every report depends on memory, manual work.
Delivery capacity
Quantified bench strength across data analysts, data engineers, and Superset developers—ready for discovery, build, and ongoing delivery.
15+
Metric design, reporting, and decision support from people who own the analytics layer.
10+
Hands-on implementation capacity for pipeline, platform, and reliability work.
5+
Experienced dashboard and semantic-layer builders for Apache Superset.
Embedded · enterprise
Same dashboards. Your brand. No iframe.
Pick a brand to re-skin the embedded dashboard:
Data analytics
When every team owns a different definition of the same metric, every meeting becomes a debate. We collapse the noise into one semantic layer everyone can point to.
MariaFinance10:14
Revenue Q3 closes at $42.1M. Going to the deck.
IvanGrowth10:16
my pipeline says $44.8M for Q3?
AnnaProduct10:17
the warehouse view gives me $39.6M though
MariaFinance10:18
which one are we presenting tomorrow??
$42.1M
+5.8% vs Q2 · +12.4% YoY
Updated 2h ago · synced from semantic_layer.revenue
Breakdown
Revenue Q3
$42.1M
+5.8%
Subscriptions
$37.6M
+8.1%
Services
$4.5M
−2.4%
Before — Three teams, three numbers, no answer to “which one?”
After — One definition, one owner, traceable to the semantic layer
Case study · Data analytics
A quick look at a recent engagement. Open the full write-up for context, problem, solution and results.
How we replaced 20+ scattered reports with a single self-service instrument — giving product managers and business leaders a unified view of digital performance across 30 markets.
Read the full caseResults
3M+
monthly active users across app & web
20×
scattered reports replaced by one tool
Days → min
time-to-insight for ad-hoc questions
App & web events
clicks, opens, orders
ETL & metric layer
unified definitions
Multi-dimensional model
country × channel × time
Dashboard modules
6 views, 10+ KPIs
Product & business teams
daily reviews, ad-hoc
Data engineering
When the same table has five forks and nobody owns any of them, the platform spends every Monday recovering from Friday. We rebuild the lineage so the path from source to dashboard is one line, not a knot.
Before — Forks, copies and broken edges, no clear ownership
After — One path, one owner, tests at every step
Case study · Data engineering
A quick look at a recent engagement. Open the full write-up for context, problem, solution and results.
How a 4-engineer team served data to 3 000+ users — from analysts to business stakeholders — by automating Spark job creation with template generation.
Read the full caseResults
2 500+
jobs shipped via the template generator
76 of 85
contributors are not data engineers
4
engineers covering 3 000+ data users
Analyst config
YAML/JSON
GitHub Action
pipeline trigger
Jinja template
canonical patterns
~600 lines of PySpark
auto-generated
Deploy to Databricks
production-ready
Blog
Practical write-ups we publish as we ship: GitOps for BI, Superset in production, and how we keep dashboards honest.
certifying datasets via GitHub Actions
How we treat Superset datasets as code: a GitHub-as-authority workflow with schema validation, Jinja parsing, SQLFluff, and a certification badge that points back to the exact CI run.
Read articlePublished
9 min read
By Rt Kazakov
Topics
Why wait?
We'll look at your setup, find what's broken, and tell you what to fix first. No deck, no delay.
