AI-native data architecture platform

Your AI senior data architect,
available 24/7

Pipebase designs your medallion data architecture, writes the transformation code, picks the right tools, walks your team through deployment, and keeps everything documented — automatically.

6+
Platforms supported
∞
Custom layers
4
Governance levels
Auto
Documentation
What Pipebase does

An AI data engineer
sitting next to your team

Pipebase acts as a senior data architect who never sleeps. It looks at your data, designs the architecture, writes the code, and guides deployment.

🔌
Connects to your data sources
Plug in Postgres, Snowflake, Shopify, Stripe, S3, BigQuery or any CSV file. Pipebase reads the schema and profiles the data automatically.
🏛️
Designs your medallion architecture
AI analyzes your data source and domain, then suggests the right number of layers with names, purposes, and the transformation rules between each one.
⚙️
Generates production-ready code
Writes dbt models, Databricks notebooks, Spark jobs, or Python scripts — whichever tool fits your stack and scale best. Code is editable inline.
📤
Deploys or guides deployment
For Auto governance, it deploys directly. For larger teams, it walks engineers step-by-step through deploying to Databricks, dbt Cloud, or AWS Glue.
📋
Writes all the documentation
Generates a full architecture spec, data dictionary, lineage diagram, operations runbook, and business glossary. Always stays up to date.
📡
Monitors and self-heals
Watches pipeline health, catches data quality failures, suggests fixes, and alerts the right people. The architecture gets smarter over time.
Example: E-commerce data architecture
Landing
Raw data exactly as received from source. No modifications. Immutable audit trail.
S3Delta Lake
→
Bronze
Parsed, typed, renamed to snake_case. Schema validated. No business logic yet.
dbtDatabricks
→
Silver
Deduplicated, enriched, business rules applied. Enterprise view of customers and orders.
dbtSpark
→
Gold
Business-ready aggregations. Revenue metrics, LTV, cohorts. Optimized for BI tools.
dbtSnowflake
→
Serving
Dashboards, ML feature store, API-ready datasets for product teams.
LookerTableau
Supported platforms

AI picks the right tool
for every layer

Pipebase knows the strengths and costs of every major data platform. It recommends the best fit based on your data volume, cadence, and budget.

🔧
dbt Core
SQL transformations · Free
🧱
Databricks
Spark + ML · $0.40/run
❄️
Snowflake Tasks
Native SQL · $0.08/run
🔗
AWS Glue
Serverless ETL · $0.44/run
🏭
Azure Data Factory
Azure ETL · $0.25/run
⚡
Apache Spark
Large scale · $0.15/run
🐍
Python Scripts
Custom logic · ~$0.02/run
☁️
dbt Cloud
Managed dbt · Per seat
Step by step

How a build works

From connecting your data source to having a fully documented, deployed architecture — here's exactly what happens.

1
Connect your data source
Add a connector — Postgres, Snowflake, Shopify, CSV, or any other source. Pipebase reads the schema and profiles every column: null rates, data types, cardinality, sample values.
🔌 Connectors page
2
Create an architecture and let AI design the layers
Name your architecture (e.g. "Netflix Analytics"), pick your domain. Click "AI suggest layers" — the AI reads your data profile and suggests 3–5 layers with names, colors, and purposes tailored to your specific data. You can add, remove, or rename any layer.
🏛️ Architecture Builder
3
Define what each layer means in business terms
For each layer, you describe in plain English what the business needs: "Remove duplicate records. Fill null directors with Unknown. Split genres into individual rows. Validate show_id is never null." AI reads your words and generates the exact transformation rules.
🔧 Layer Editor
4
AI picks the right tool and writes the code
Based on your data volume, cadence, and existing stack, AI recommends the best platform with real cost and runtime estimates. It then generates complete, production-ready code — dbt models, Databricks notebooks, Spark jobs — ready to deploy. You can edit the code directly in the browser.
⚙️ Code generation
5
Review, approve, and deploy
Depending on your governance level, transformations go through an approval workflow. Auto: deploys immediately. Supervised: one person approves. Enterprise: full review chain. Once approved, Pipebase either deploys directly via API or walks your team step-by-step through deploying to the platform.
✅ Approvals
6
Documentation is written automatically
Pipebase generates a complete architecture spec: executive summary, data lineage diagram, layer-by-layer transformation rules in plain English, data dictionary, operations runbook, and glossary. Export as Markdown for Confluence or Notion. It updates automatically as the architecture changes.
📋 Documentation
Governance

Scales to any team size

From solo engineers moving fast to regulated enterprises with strict approval chains — Pipebase adapts to how your team works.

⚡
Auto
AI deploys automatically. No approvals needed. Best for startups and early-stage teams.
AI generates code
Auto-deploys to platform
Notifies team
👁
Supervised
One team member reviews and approves before deployment. Recommended for most teams.
AI generates code
Engineer reviews
One-click deploy
🔐
Governed
Architect reviews rules, engineer reviews code. For teams managing production data.
Business rules approved
Code reviewed
Deployed with audit trail
🏢
Enterprise
Full approval chain including security review. For regulated industries and large orgs.
Business approval
Architecture review
Security sign-off

Ready to build?

Connect your first data source and let AI design your architecture in minutes. Free to start — no credit card required.

Pipebase

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