Real-time visibility into
what's actually happening.
Custom analytics platforms, dashboards, and self-serve reporting infrastructure built around the decisions your business needs to make — not the reports that get scheduled and ignored.
What the platform delivers
< 5 min
P95 warehouse lag
340ms
P99 dashboard load
91%
Weekly active users
The data exists.
The visibility doesn't.
Most companies have more data than they know what to do with. Transactional records in their ERP, customer activity in their CRM, operational events in their systems, financial data in their accounting platform — and no coherent way to see across all of it and understand what it means.
The result is decisions made on gut instinct, month-old reports, or whatever someone had time to pull together in a spreadsheet. The data exists. The visibility doesn't.
We build the analytics infrastructure that changes that — custom platforms and dashboards tailored to your KPIs, your operational data, and the specific decisions your teams need to make.
Seven analytics
capabilities, one platform.
Custom Analytics Platforms
End-to-end analytics platforms built around your data model and your decision-making structure — not a generic BI tool configured to approximate what you need. Data ingestion, transformation, storage, query engine, and visualization layer, designed as a coherent system.
Executive & Operational Dashboards
Real-time dashboards built for action, not review. Most dashboards summarize what happened. We build dashboards that surface what needs attention right now, what's trending in the wrong direction, and what decisions need to be made today.
Self-Serve Reporting Infrastructure
Giving your teams the ability to answer their own questions without filing a data request and waiting three days. Self-serve platforms with governed data models, semantic layers, and query interfaces that let business users explore data safely.
Real-Time Streaming Analytics
Operational intelligence that runs in seconds, not hours. Real-time event pipelines that process high-volume operational data and surface insights as events happen — not in the next morning's batch report.
Product & Customer Analytics
Understanding how customers actually use your product, where they drop off, which features drive retention, and what the leading indicators of churn look like before churn happens. Event tracking, funnel analysis, cohort retention, and segmentation models.
Financial & Revenue Analytics
Revenue recognition, margin analysis, cohort-based LTV modeling, sales performance attribution, and the financial reporting infrastructure that gives your finance team control over their data. Integrated with your ERP, CRM, and billing platform — reconciled and consistent.
Data Warehouse Design & Modeling
The foundation everything else is built on. A well-designed warehouse with a coherent dimensional model is the difference between analytics that scales and analytics that becomes a maintenance burden. Architecture, modeling (dimensional, OBT, vault), transformation layer design, and testing infrastructure.
Visibility across
every industry.
The need for reliable, real-time analytics is universal. The data sources, KPIs, and compliance requirements differ by industry — the underlying problem doesn't.
Real-time sales performance, inventory level monitoring, customer cohort analysis, promotion effectiveness measurement, and margin analytics across SKUs, categories, and channels. Connecting transaction data, inventory data, and customer behavior data into a coherent operational picture.
SaaS & Technology
Product usage analytics, activation and retention funnels, churn prediction models, ARR and MRR dashboards, net revenue retention tracking, and the customer health scoring that tells your success team who to call before they get the cancellation email.
Portfolio performance dashboards, risk exposure monitoring, client reporting automation, regulatory reporting infrastructure, and the operational analytics that keep compliance teams informed in real time. Audit-ready data lineage and the access controls that financial data requires.
Patient outcome analytics, operational efficiency dashboards for clinical operations, revenue cycle performance, and population health reporting. HIPAA-compliant data infrastructure with the access controls, encryption, and audit logging that healthcare data requires.
OEE dashboards, production yield analytics, quality control monitoring, supply chain performance reporting, and the real-time plant floor visibility that lets operations teams catch problems before they become downtime events.
On-time delivery performance, route efficiency analytics, fleet utilization dashboards, exception monitoring, and the customer-facing reporting that differentiates logistics providers. Real-time operational visibility from dispatch through delivery.
Four ways
to engage us.
Engagements are scoped to your situation — from a focused data readiness audit to a full analytics platform build and ongoing partnership.
Data Audit & Readiness
Inventory of data sources, quality profile, lineage documentation, and a written assessment identifying what's usable, what's broken, and what needs to change before the warehouse work begins.
Warehouse & Dashboard Build
Warehouse architecture, dimensional model, dbt transformation layer, core ETL pipelines, and the first set of production dashboards tied to the decisions stakeholders identified in phase one.
Full Analytics Platform
End-to-end platform with semantic layer, governed self-serve, embedded analytics, data quality monitoring, and onboarding for every team that consumes the system. Scales beyond a single use case.
Ongoing Analytics Partnership
New metrics, new source integrations, dashboard iteration, pipeline reliability, and data quality monitoring. Your analytics platform evolves with the business — without rebuilding what already works.
The stack we reach for.
Warehouse selection, BI tooling, and pipeline architecture follow your query patterns, data volume, team capabilities, and what you already have — not vendor incentives.
Analytics that
earns trust.
We build for decisions, not for reports.
Every design choice — metric definition, visualization type, dashboard layout, drill-down structure — is made in service of a specific decision that a specific person needs to make. Reports that don't connect to decisions don't get built.
We take data quality seriously.
An analytics platform is only as trustworthy as the data that feeds it. We invest in data quality testing, pipeline monitoring, and data lineage documentation because a platform your teams don't trust is worse than no platform at all.
We design for adoption.
The best analytics infrastructure in the world fails if the people who need the insights don't use it. We design for the users who will rely on it day-to-day — their technical level, their decision-making context, their time constraints — and we measure success by whether behavior changes.
We build infrastructure that lasts.
Analytics needs evolve faster than almost any other category of software. We build data models and pipeline architectures that are extensible by design — so adding the next data source or KPI doesn't require rebuilding what's already there.
Straight answers.
Not necessarily. For smaller data volumes or focused analytics use cases, a well-designed PostgreSQL database and a lightweight BI tool can get you surprisingly far. We'll recommend the right architecture for your data volume and analytics requirements — which isn't always the most complex one.
That's the work. Source systems rarely agree on definitions, identifiers, or data quality. Building the transformation layer that reconciles inconsistencies, enforces canonical definitions, and surfaces data quality issues rather than hiding them is a core part of what we do. We won't build you a dashboard on top of unreconciled data and call it analytics.
Yes. If you have Tableau, Power BI, or Looker licenses and embedded expertise in those tools, we build the data infrastructure — warehouse, pipelines, semantic layer — that makes your existing tools faster, more reliable, and more trusted. We don't require you to replace tools that are working.
A focused dashboard and data pipeline project for a single business domain typically takes 6–10 weeks. A full analytics platform covering multiple business domains, with self-serve capabilities and a governed semantic layer, typically takes 3–6 months. Data quality issues in source systems are the most common cause of timeline extension.
Metric definitions are documented and agreed upon before development begins — not inferred from whatever the source system happens to store. Transformation tests validate the logic, pipeline monitoring catches data quality issues, and reconciliation against source of record validates the output. We build confidence in the numbers, not just the dashboard.
Build visibility into what actually drives your business.
Tell us what decisions you need to make and we'll build the platform that makes them with confidence.