Accelerate Data Engineering.
Leave zero technical debt.

Execute data transformation workflows faster with an Agentic Ecosystem that automatically models, maps, and generates optimized pipelines.

80%Faster Pipelines

Reduce manual coding time across thousands of complex transformations.

100%Lineage Coverage

Zero blind spots. Every field is automatically traced from source to target.

~60%Cost Reduction

Decrease reliance on massive offshore teams for raw ETL development.

Engineer the entire pipeline.
Simultaneously.

Compare massive data schemas at once and extract transformation logic into a declarative, executable matrix view.

Mapping ContractsDefine exact transformation rules (e.g., Casts, Joins, PII Masking) via simple AI prompts without writing manual ETL scripts.

Three-Panel VerificationNavigate seamlessly between the source tables, declarative mapping parameters, and the generated target code (PySpark/dbt) in one unified window.

Datapunkt Mapping
Lineage Registry
Source Schemas
Target Models
Transformation Rules
Quality Contracts

How Mapping Contracts Transform Engineering

Massive Scale Matrix Comparison

Instead of toggling blindly between hundreds of database IDEs and Python notebooks, the Mapping Contract Agent ingests your schemas simultaneously. It maps your exact target intents—like "Combine First and Last name and format as Uppercase"—into a single, structured matrix view. You instantly get a bird's-eye view of your entire transformation architecture.

Declarative Intent to Executable Code

Unlike rigid legacy ETL tools, Datapunkt operates on a flexible, code-agnostic model. You define the extraction schemas via AI prompts or UI matrices. The Transformation Code Agent then synthesizes these declarative rules into highly optimized PySpark, dbt Core SQL, or Snowflake logic natively, reducing technical debt instantly.

Line-by-line validation.
Zero data leakage.

Abandon manual QA and rigid static tests. Pair your pipeline with the Data Quality Agent to uncover edge cases and enforce strict governance.

Critical Anomalies Flagged: Uncovers unexpected null propagations, schema drifts, or referential integrity breaks automatically.

ISO 8000 & Custom Rules: Applies pre-configured industry rules or custom data contracts directly into your processing engine.

USER_PROMPT > Review dim_customer model and ensure ISO 8000 compliance for account balances.
AGENT_REPLY > Anomaly detected: Missing financial boundary protection on 'account_balance'. Generating dbt assertions...
models/schema.yml
models:
  - name: dim_customer
    columns:
      - name: account_balance
        tests:
          - not_null
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0

How Datapunkt Secures Pipeline Integrity

Semantic Validation vs. Simple Checks

Complex data products often hide systemic failures under technically successful runs. The Data Quality Agent performs a deep semantic read of the data models, assessing the true business context of every single column. It proactively flags missing financial boundary protections or aggressive null propagations, ensuring data reliability before it hits the BI dashboard.

The Universal Data Contract

Equip your architecture with our globally curated Quality Playbooks. The AI acts as your automated QA partner, generating executable constraints regardless of whether you are running Databricks, Snowflake, or BigQuery. It continuously analyzes the survival periods of representations against your firm's strict risk tolerances.

Your Autonomous Engineering Assistant.
Ask the Pipeline.

The unified reasoning core of Datapunkt acts as your autonomous Data Engineer. Instead of manually writing boilerplate Terraform scripts or debugging dbt YAML configurations, simply ask the agent.

It synthesizes information across your entire data stack to provide instant, verified code blocks and directly submits transformations for execution.

datapunkt-engineer-agent ~ zsh
dp-agent run --pipeline=crm_to_dwh
> Analyzing requested pipeline...
> Verifying dependent sources in schema registry...
> Found target model: dim_crm_users
> Identifying required transformations: [Join, Cast, PII Mask]
> INFO: Existing terraform state lacks BigQuery permissions for target dataset.
> Generating IAM role updates...
> Applying terraform changes... [SUCCESS]
> Executing dbt build --select dim_crm_users...
> Pipeline successfully deployed. Data is flowing.

Source Metadata.
Ingest the entire estate.

Data engineering requires an absolute understanding of the source systems. The Source Catalog Agent acts as your automated discoverability layer.

By securely connecting to your PostgreSQL, Snowflake, and Kafka environments, the agent continuously harvests schemas, establishing exact boundaries for the AI's modeling logic.

PostgreSQL
Kafka Streams
Snowflake
Datapunkt
Context Engine

Dive Deeper into the Ecosystem

Explore the specialized AI agents driving the Datapunkt architecture.

Source Agent

Harvest schemas across SQL, NoSQL, and streaming sources automatically.

Read Guide

Modeling Agent

Generate rigorous, industry-compliant target data models instantly.

View Capability

Transformation

Synthesize optimized PySpark, dbt, and Snowflake logic from intent.

Explore Agent

Data Lineage

Trace every field end-to-end from source assets to target models.

See Integration

Ready to build your next data product?

Deploy the Datapunkt Agentic System into your data platform and cut engineering lifecycle time by up to 80%.

Start Data Engineering