Warehouses, pipelines, RAG

The pipes underneath
the product.

Warehouses, pipelines, streaming, vector search. From raw event to executive dashboard, from clickstream to LLM context. Lakehouse when you need it, Postgres-first when you do not.

Get a quote → What we ship
Events/sec180K+ sustained
Warehouse defaultSnowflake / BQ
Vector DBpgvector first
SOURCES Postgres Stripe Segment Kafka PIPELINE dbt + Airflow staging → marts → exposures WAREHOUSE Snowflake prod_marts BI & DASHBOARDS JanDec ARR ↑ 142%
Scroll to explore ↓
What we ship

Six concrete deliverables.

Every Data & Intelligence engagement maps to a specific deliverable below. We commit to it in the SOW, demo it weekly, and you own the result.

01

Warehouse

Snowflake, Databricks, BigQuery, Redshift, ClickHouse Cloud. Modeled for analyst, not just ingested.

Data & Intelligence
02

Pipelines

dbt + Airflow / Dagster / Prefect / Temporal. Lineage tracked. Tests on every model.

Data & Intelligence
03

Streaming

Kafka, Redpanda, Kinesis. Flink + Materialize for stateful streams. Exactly-once where it matters.

Data & Intelligence
04

Vector + search

pgvector, Pinecone, Weaviate, Turbopuffer. Hybrid retrieval, freshness-aware, instrumented.

Data & Intelligence
05

BI

Looker, Hex, Metabase, Mode. Semantic layer in dbt. Single definition of every metric.

Data & Intelligence
06

ML platform

PyTorch, JAX, HuggingFace. Fine-tune when scope justifies it. Eval-driven model selection.

Data & Intelligence
The stack

The tools we reach for.

Solid line: what we use every day. Dashed line: what we reach for when the brief justifies it. We will work in your stack if you have a strong reason; otherwise these defaults serve us well.

Snowflake Databricks BigQuery dbt Airflow Dagster Kafka Flink pgvector Pinecone Looker Hex ClickHouse Cloud Redpanda Materialize Weaviate Turbopuffer Metabase Mode Cube Lightdash PyTorch HuggingFace
How we engage

Four steps. Real demos every Friday.

From signed SOW to first demo is one week. No discovery loops that bill for months without showing software. No silent stretches between status decks.

01

Data audit

Sources, schemas, lineage, current dashboards. Output: data-architecture diagram + KPIs we can trust.

Week 0-1
02

Modeling

dbt project, semantic layer, dimensional model. First trustworthy metric live in week one.

Week 1-3
03

Pipelines

Ingest from every source. Streaming where it matters. Backfill the history.

Week 2-8
04

Insight

BI dashboards, anomaly alerts, weekly KPI digest. Executive-ready.

Week 6+
Our entire data stack now lives in dbt + Snowflake. Time to a new metric dropped from two weeks to two hours.
VP Analytics · B2B SaaS
Frequently asked

The questions buyers ask first.

Snowflake or BigQuery or ClickHouse?
Snowflake when you need a Swiss-army warehouse with strong governance. BigQuery when you are GCP-native and care about cost. ClickHouse for product analytics and high-cardinality. We pick after we read your data.
Do you set up dbt for us?
Yes. Models, tests, snapshots, exposures, lineage, semantic layer, CI integration. We default to dbt Cloud unless you have a strong reason to self-host.
What about RAG for LLM features?
That is on our AI page too. We handle the data tier of RAG here: ingestion, chunking, embedding refresh, retrieval evaluation, vector store choice. The agent layer is on the AI service.
Can you migrate us off a legacy warehouse?
Yes. Greenplum, Hadoop, Vertica, on-prem Postgres for analytics. Zero-downtime migrations with dual-write and parallel validation.

Trust the numbers.
Ship the dashboard.

Senior data engineer reviews your current setup and replies with a concrete priority list in one business day.

Get a quote → All 12 services
At a glance
Default warehouseSnowflake
Default transformdbt
Default streamingKafka
Default vectorpgvector
Response time< 1 business day

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AI & Agents→APIs & Integrations→Cloud & Platform→

See all 12 services · Or browse pricing · Or read case studies.

“
TroyFunds turned quarterly capital calls from a six-day process into four hours. Our auditor took the export without rework. Worth every dollar.
P
S. ParikhManaging Partner, Venture Studio
Frequently asked

Quick answers.

The questions buyers in this service ask in week one.

Snowflake or Databricks?+

Snowflake for governance-strict, BI-heavy shops. Databricks when the lakehouse pattern fits and ML pipelines are central. BigQuery when the customer is already GCP-native.

Do you do reverse-ETL?+

Yes. Hightouch and Census are our defaults. Custom when SaaS sources are not supported.

How do you handle PII in pipelines?+

Field-level encryption with customer-managed KMS, tokenization at ingest, row-level security in warehouse, audit trails.

Can you build a semantic layer?+

Yes. Looker, Lightdash, or custom dbt semantic models. We treat the metric layer as a product.

Do you do CDC?+

Yes. Debezium for self-hosted, Fivetran or Airbyte for SaaS-source CDC. Kafka Connect for event-sourced architectures.

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