The data foundation —
your business actually runs on.
Modern data stack, real-time streaming, and AI-ready pipelines. We build the data infrastructure that makes analytics fast, AI features possible, and ops trustworthy — without the "we'll fix it later" tax.
Data quality beats pipeline cleverness. We engineer the foundation, not the demo.
Data engineering changed more in 5 years than the prior 20.
Modern tools made hard problems easy.
Informatica, SSIS, custom scripts. On-prem warehouses. Hand-written SQL everywhere.
Fivetran + dbt + Snowflake/BigQuery. SQL-first transformation. Managed everything.
Kafka mainstream. Flink production-ready. Real-time analytics from event-driven sources.
Vector DBs, embedding pipelines, ML feature stores. Lakehouses unify analytics + ML.
Data contracts, lineage, ownership. Data treated as a product, not a side effect.
What we deliver.
Four capabilities. Most engagements start with the warehouse foundation and expand into streaming and AI as the business case develops.
Data pipelines (ELT/ETL)
Batch and streaming pipelines that move data from source systems into your warehouse — Fivetran or Airbyte for managed ingestion, dbt for transformation, custom Python where the long tail demands it.
Warehouses & lakehouses
Snowflake, BigQuery, Databricks, Redshift. Right-sized architecture, cost-aware modeling, query performance that doesn't blow up at 10x scale. Lakehouses when storage cost matters; warehouses when SQL ergonomics win.
Real-time streaming
Kafka, Flink, Materialize. Sub-second data freshness when the business case justifies it. Event-driven architectures, change data capture, real-time analytics for ops and product.
AI-ready data
Embedding pipelines, vector databases (pgvector, Pinecone, Weaviate), ML feature stores. The data infrastructure your AI/ML team needs without the "we'll fix it later" tax.
A 5-stage methodology — audit, then build.
Data projects fail at the audit, not the pipeline. We start where the leverage is.
Data audit
What sources you have. What data quality looks like. Where it lives. Who owns it. Most data projects fail because the audit was skipped — we start there.
Define contracts
Schemas, SLAs, owners. Data contracts between producers and consumers so the warehouse stops being a graveyard of broken assumptions.
Build foundation
Warehouse setup, ingestion pipelines, transformation models. We build the boring foundation right so everything downstream gets cheaper and faster.
Production engineering
Observability, cost dashboards, governance, lineage. Without these, data platforms get expensive and untrustworthy as they grow.
Iterate
New sources, new use cases, performance tuning. Data platforms are not "ship and forget" — they're infrastructure that compounds with use.
Pick the pattern that fits your workload.
There's no one right data architecture — the right one depends on data volume, latency needs, and ML workloads. Here's the four-way decision.
Modern data stack
You have SaaS sources and want analytics fast
Lakehouse
Large data volumes or ML workloads alongside analytics
Streaming-first
Real-time ops, fraud, anomaly detection, live product analytics
Hybrid (batch + real-time + AI)
Mixed workloads — analytics, ops, ML
The tools we use — and why.
Vendor-neutral. Tool choice driven by workload fit, team skill, and operational profile.
Warehouses & Lakehouses
Ingestion & Transformation
Orchestration
Streaming
Quality, Observability & AI
Ranges we typically deliver.
We measure baseline before and after. Numbers vary with starting condition — but here's the typical impact.
What we'd build for your industry.
Data platforms shift with the regulatory, latency, and integration constraints of each vertical.
B2B SaaS
Product analytics infrastructure, customer 360, usage-based billing pipelines, retention dashboards. Modern data stack on Snowflake or BigQuery with dbt transformations. Embedding pipelines for AI features running on the same warehouse.
Healthcare
Clinical data warehouses with BAA-eligible infrastructure. PHI handling with row-level access controls. EHR integrations (Epic, Cerner). Pipelines feeding clinical decision support, quality reporting, and ML models trained on de-identified data.
Retail & E-commerce
Real-time inventory pipelines. Demand forecasting infrastructure. Personalization feature stores. Order, customer, and product data unified for analytics and ML — supporting both daily reports and real-time recommendations.
Fintech
Transaction processing pipelines with end-to-end audit. Fraud detection feature stores. Regulatory reporting (BSA, KYC, SOX). Real-time anomaly detection on event streams. Compliance baked into data contracts from day one.
Data platforms that can be trusted.
Data lineage + audit
Every column traceable to its source. Every transformation logged. Every consumer mapped. Regulators and engineers both get answers.
PII / PHI handling
Row-level access, column masking, encryption at rest and in transit. BAA-eligible infrastructure for healthcare. Compliance designed in from the warehouse up.
Data contracts
Schemas, SLAs, ownership documented and enforced. Producer changes don't silently break consumers. The warehouse stops being a graveyard of broken assumptions.
Cost attribution
Per-team, per-pipeline, per-consumer cost dashboards. So the team running the expensive query is the team that pays for it — and gets to optimize it.
Foundations that compound.
The fanciest streaming architecture doesn't fix dirty input data. We audit and fix data quality first — the pipeline is the easier problem.
Streaming costs 3–10x batch at scale. We use real-time only where the business case justifies it. Most "real-time" dashboards work just fine on 5-minute batches.
Fivetran + dbt + Snowflake beats hand-rolled ETL almost every time. We build custom only for the long tail — legacy systems, large transformations, niche integrations.
Lakehouses, vector DBs, feature stores — analytics and ML now share infrastructure. We design data platforms that serve both workloads instead of forcing a rebuild later.
Data platforms in production.
Logistics Automation Platform
An AI-native logistics automation platform where intelligent agents handle route optimization, real-time tracking, demand forecasting, and disruption response — replacing the manual coordination layer across complex supply chains.
View case study
Agriculture Supply Chain Automation
An AI-native agriculture supply chain platform where intelligent agents handle crop monitoring, demand forecasting, quality verification, and farm-to-table traceability — replacing the manual coordination between farmers, distributors, and retailers.
View case study
E-Commerce Partner Portal
An AI-native B2B partner portal where intelligent agents run catalog enrichment, dynamic pricing, demand forecasting, and order routing — replacing the manual coordination layer between manufacturers and retailers.
View case study
Water Operations Automation
An AI-native water operations platform where intelligent agents monitor quality, detect leaks, predict maintenance needs, and forecast consumption — replacing manual SCADA polling and reactive inspection rounds.
View case studyHonest answers.
Strategy
Engineering
Engagement
Ready to build the data foundation?
Tell us what your data looks like today — and what questions you can't answer. We'll come back with a scoped plan and a working warehouse within 4–6 weeks.
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