Which BI tool should we use?

The one your teams will adopt and your data platform supports well. We work across the major tools and recommend based on your users, licensing and existing skills.

Can you fix our existing dashboards instead of rebuilding?

Yes. Often the fix is in the data model and definitions underneath, and we start there.

How do you handle performance on large datasets?

Aggregation layers, incremental refresh, query optimization and caching, designed with the platform rather than against it.

Do we need a CDP product or can we build on our warehouse?

Both work. Warehouse-native approaches suit companies with a strong data platform; packaged CDPs suit teams that need activation tooling quickly.

How do you prove personalization works?

Controlled experiments with holdout groups and clear metrics, so lift is measured rather than assumed.

Data Science & Engineering

Analytics, BI & Customer Insights

Semantic layers, dashboards and customer analytics people actually use.

Talk to an engineer

See related work

Analyst reviewing a business dashboard on a laptop

Overview

Most dashboards are built, admired once and abandoned. The ones that survive answer a question someone asks every week, use numbers everyone agrees on and load before the meeting starts. That is the standard we build to.

Our analytics work starts with the decision and the person making it. We design the semantic layer so metrics mean the same thing everywhere, build the dashboards and self-service models on top and train teams to use them.

This page also covers customer and product analytics: unifying customer data and instrumenting products to learn what drives value.

Offering 1

Analytics and business intelligence

The dashboards that survive answer a question someone asks every week, use numbers everyone agrees on and load before the meeting starts. We design the semantic layer, build the dashboards and train teams to use them.

Semantic layer and metrics

Governed definitions for revenue, churn, margin and every other number the business argues about.

Dashboards and reporting

Executive, operational and embedded dashboards in Power BI, Tableau, Looker and open-source tools.

Self-service analytics

Curated datasets, training and guardrails so teams can explore without breaking definitions.

Analytics engineering

dbt models, tests and documentation that keep BI on solid ground.

Typical use

Executive scorecards with one version of the truth across finance, sales and operations
Operational dashboards for supply chain, plant and support teams
Replacing spreadsheet reporting with governed, refreshed views

Offering 2

Customer and product analytics

Knowing your customers means joining what they buy, what they do in your product and what they tell support, then acting on it while it is still relevant.

Marketing team reviewing a sales analytics dashboard together

Customer data platform (CDP)

Identity resolution, unified profiles and activation across marketing, sales and product tools.

Product analytics

Event tracking design, funnels, retention and feature usage analysis for web and mobile products.

Segmentation and lifecycle analytics

Behavioral segments, lifecycle stages and triggers for engagement.

Experimentation platform

A/B and multivariate testing infrastructure with rigorous statistics.

Typical use

SaaS companies improving activation, retention and expansion
Retailers and marketplaces building personalization and loyalty programs
Product teams instrumenting features to learn what drives value

Want this for your product? Talk to an engineer who has built it.

Book a working session

How we work

How we deliver

01
Frame
The questions, the audiences and the definitions that need agreement.
02
Model
Semantic layer, datasets and tests on the data platform.
03
Build
Dashboards and self-service models with performance and usability testing.
04
Adopt
Training, feedback loops and usage analytics to keep improving.

Stack

Tools we work with

We are neutral on tooling and pick what fits your environment, your team and the cost you can sustain.

Power BI, Tableau, Looker, Metabase, Apache Superset
dbt and semantic layers such as Cube and LookML
Snowflake, Databricks, BigQuery
embedded analytics SDKs
Segment, RudderStack, mParticle
Mixpanel, Amplitude, PostHog
GrowthBook, Optimizely, LaunchDarkly
Python for advanced analysis

Bring the problem. We bring the team.

Talk to an engineer

Industries

Where we apply this most

Shopper checking her phone while picking fruit in a grocery store

Retail & E-commercePersonalization, demand forecasting, search and customer data platforms.Explore

Row of white robotic arms on an automated production line

Manufacturing & IndustrialPredictive maintenance, quality inspection, digital twins and plant analytics.Explore

Colleagues shaking hands after a successful interview

HR Tech & StaffingCandidate matching, recruiter copilots, workforce analytics and ATS/HRMS platforms.Explore

Low angle of a modern office building against a blue sky

Real Estate & PropTechProperty platforms, valuation models, tenant experience and building operations data.Explore

FAQ

Questions we hear often

Related services

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

Start a conversation

Analytics, BI & Customer Insights

Analytics and business intelligence

Semantic layer and metrics

Dashboards and reporting

Self-service analytics

Analytics engineering

Customer and product analytics

Customer data platform (CDP)

Product analytics

Segmentation and lifecycle analytics

Experimentation platform

How we deliver

Frame

Model

Build

Adopt

Tools we work with

Where we apply this most

Questions we hear often

You may also need

Data Engineering & Platform Modernization

Data Science & Predictive Modeling

Data Strategy, Architecture & Governance

Ready when you are.