Data Engineering Services
Pipelines and warehouses you can trust.
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Data Analytics & BI Services
One source of truth: KPI models, dashboards and self-serve analytics your teams actually use.
adoption training included
Stacked 3D bar and line charts with a FORCE KPI line risingProblems solved
What this removes.
no single source of truth
every team brings its own number
slow manual reports
decisions wait on spreadsheets
dashboard graveyards
200 charts, zero decisions
metric definition drift
churn means four different things
no adoption
BI licenses become shelfware
What's included
What you get.
- KPI tree + metric catalog
- semantic layer (dbt metrics)
- executive dashboards (Superset/Metabase)
- self-serve exploration workspace
- embedded analytics for customers
- alerting on thresholds
- data dictionary + owners
- adoption training program
- usage analytics for the BI itself
- 90-day improvement loop
Scope
Scope, clearly drawn.
| Core scope | Optional add-ons |
|---|---|
| Coremetric workshop + KPI tree | Add-onembedded analytics for customers+$8kRecharts/Nivo or Superset embed |
| Coresemantic layer build | Add-onmobile KPI alerts (Slack/Teams)+$3k |
| Coredashboard suite (exec + ops) | Add-onexperimentation dashboard+$5kA/B results in one place |
| Corealerting + subscriptions | Add-onBI migration (Looker/PowerBI -> open)+$6kparity-checked |
| Coredata dictionary publish | Tailored to scope |
| Coreadoption training | Tailored to scope |
Core scope
- Coremetric workshop + KPI tree
- Coresemantic layer build
- Coredashboard suite (exec + ops)
- Corealerting + subscriptions
- Coredata dictionary publish
- Coreadoption training
Optional add-ons
- Add-onembedded analytics for customers+$8kRecharts/Nivo or Superset embed
- Add-onmobile KPI alerts (Slack/Teams)+$3k
- Add-onexperimentation dashboard+$5kA/B results in one place
- Add-onBI migration (Looker/PowerBI -> open)+$6kparity-checked
Our approach
How we run it.
Metric workshop
definitions + owners agreed
Semantic layer
one definition, everywhere
Dashboard build
exec + ops + self-serve
Adoption
training + subscriptions + alerts
Improvement loop
usage analytics on the BI itself
Pricing signal
What it typically costs.
Typical range
$15k-$90k
Midpoint $52.5k · final quote after a fixed-scope discovery.
adoption training included
Technologies
Tools we reach for.
Case evidence
Proof.
-70%
weekly reporting time
92%
team adoption in 60 days
5/5Verified client
“Exec meetings now start from one dashboard.”
Reviews
What clients say.
Slide 1 of 1
- 5/5Verified client
“Exec meetings now start from one dashboard.”
COOChief Operating Officer, logistics - 5/5Verified client
“Embedded analytics closed two enterprise deals.”
CROChief Revenue Officer, SaaS - 5/5Verified client
“The semantic layer ended our metric wars.”
Head of DataHead of Data, retail
FAQ
Questions, answered.
Open-source BI only?
Preferred - Superset/Metabase; we integrate Looker/PowerBI when required.
Can you embed for our customers?
Yes - Recharts/Nivo custom or Superset embedded with row-level security.
Do you define metrics?
Yes - workshops produce a KPI tree with owners and formulas.
Can you migrate from Looker/PowerBI?
Yes - parity-checked migration with side-by-side validation.
How do you govern self-serve?
Certified datasets + exploration sandboxes; no raw access by default.
What refresh cadence?
Batch by default; realtime where latency earns its cost.
Data Analytics & BI Services without the guesswork.
One source of truth: KPI models, dashboards and self-serve analytics your teams actually use.