Data pipelines that turn raw tables into answers, not just reports.
We build the ETL/ELT pipelines, warehouses and vector search that turn scattered, messy data into something your team — and your AI features — can actually rely on.
Data your dashboards and your AI features can both trust.
Most data problems aren't a tooling problem — they're pipelines nobody trusts and definitions that don't agree. We fix that before we build anything on top.
ETL / ELT pipelines
Reliable pipelines that move data from your apps, APIs and third-party tools into one place — scheduled, monitored, and built to recover from failure.
Data warehousing
A single source of truth for reporting and analytics, modeled so the numbers agree across teams instead of three different dashboards.
Vector search & embedding pipelines
Embedding pipelines and vector indexes that keep your RAG and semantic search features fed with fresh, relevant data.
Real-time streaming
Event-driven pipelines for the data that can't wait for the next batch job — usage events, prices, sensor data, live product signals.
Analytics & BI dashboards
Dashboards that answer the questions your team actually asks, built on data whose definitions you can trust.
Data quality & governance
Validation, lineage and access controls so bad data gets caught at the source, not three reports downstream.
Chosen for your data and workload, not the hype cycle.
We pick the database, pipeline and streaming tools that fit your data volume, latency needs and team — not the trendiest option.
We build pipelines that hold up, not just demo well.
Pipelines built for the AI era
Data clean and fresh enough to feed RAG and agents reliably, not just populate a monthly dashboard.
Boring-reliable where it matters
Batch or streaming, chosen for what your data actually needs — not whichever is trendier this year.
You own the warehouse
Your database, your schema, your data. No proprietary black-box layer standing between you and your own information.
Common questions
Do we need real-time streaming, or is batch enough?
For most reporting and analytics, a batch pipeline running every few minutes to hours is simpler, cheaper and plenty fast. We reach for streaming when the use case genuinely needs it — live pricing, fraud checks, real-time product signals — not by default.
Can you set up the data pipeline that feeds our AI features?
Yes — this is a big part of what we do alongside our AI & LLM engineering work: chunking, embedding and indexing your data so retrieval actually returns the right answer instead of a plausible-sounding one.
What if our data is messy right now?
That's a normal starting point, not a blocker. We start with an audit of what exists, clean and model the parts that matter most first, and build validation in so it doesn't quietly get messy again.
Do you work with our existing warehouse (Snowflake/BigQuery/etc.)?
Yes — we build on top of what you already have where that makes sense, rather than pushing a migration you don't need. If your current setup really is holding you back, we'll say so and explain why.
Related services
AI & LLM Engineering
The RAG, agents and copilots your data pipelines make possible.
Learn moreCloud & DevOps
The infrastructure your pipelines and warehouse run on, deployed and monitored properly.
Learn moreAutomation & Integrations
Wire your data into the tools and workflows your team already uses.
Learn moreSitting on data you're not using yet? Let's talk.
Tell us what data you have and what you wish you could do with it. We'll reply within one business day with honest feedback and a realistic path forward.