Agent Ready

Adam

Agentic Data Management

Agentic data management that enables data teams to deploy AI agents that automatically generate production-ready data pipelines from metadata. What used to take 4 hours now takes 30 seconds.

90% faster pipelines
100% convention compliance
Zero copy-paste errors

Data Teams Are Drowning in YAML

There's a better way →

yaml
# ⚠️ Manual YAML Authoring
version: "3.8"
services:
  analytics_pipeline:
    image: dataeng:latest
    environment:
      - DB_HOST=prod-db.company.io
      - DB_PORT=5432
      - DB_NAME=analytics
      - REDIS_HOST=cache.internal
      - REDIS_PORT=6379
      - KAFKA_BROKERS=kafka-1:9092,kafka-2:9092
      - SPARK_MASTER=spark://master:7077
      - AWS_ACCESS_KEY_ID=AKIA...
      - AWS_SECRET_ACCESS_KEY=...
    volumes:
      - /data/pipelines:/app/pipelines
      - /data/logs:/app/logs
      - /secrets/credentials:/app/secrets
    networks:
      - backend
      - analytics
    deploy:
      replicas: 5
      resources:
        limits:
          cpus: '4'
          memory: 16G
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
      interval: 30s
      timeout: 10s
      retries: 3
60-70%

of engineering time wasted on repetitive pipeline configuration

Copy-paste errors across 200+ pipelines
Convention drift over time
Slow iteration when schemas change
of engineering time wasted on repetitive pipeline configuration
AI-Powered Architecture

Meet Your AI Architect

Four specialized agents work together to understand, analyze, and improve your codebase

ADAMOrchestrator

Parse

Code Intelligence

Deep semantic understanding of your entire codebase

  • Multi-language AST parsing (Python, JavaScript, Java, C++, etc.)
  • Dependency graph construction and cross-reference analysis
  • Comment and docstring extraction for context awareness
  • Structural pattern recognition and code smells detection

Discover

Repository Mining

Comprehensive analysis of git history and patterns

  • Commit message sentiment and quality analysis
  • Author contribution patterns and expertise mapping
  • Branching strategy and merge conflict hotspots
  • Churn analysis to identify unstable code regions

Propose

Solution Generation

AI-driven recommendations tailored to your architecture

  • Automated refactoring suggestions with impact assessment
  • Performance bottleneck identification and optimization paths
  • Architecture drift detection and migration strategies
  • Security vulnerability scanning and remediation plans

Assemble

Context Assembly

Unified context synthesis for maximum relevance

  • Cross-agent information fusion and correlation
  • Temporal context integration from git history
  • Semantic similarity scoring for related issues
  • Priority ranking based on business impact analysis

Four specialized agents working together to eliminate manual pipeline authoring

Parse
Discover
Propose
Assemble

From Idea to Production in One Command

Enterprise-Grade Features, Zero Config

Command
adam generate-pipeline postgres_prod --enable-pii
Output
🔍 Scanning for PII...
  ✓ EMAIL detected in 3 columns (confidence: 95%)
  ✓ PHONE detected in 1 column (confidence: 98%)
  ✓ SSN detected in 1 column (confidence: 100%)
  ✓ Generated masking strategies

10 PII Types

EMAIL, PHONE, SSN, CREDIT_CARD, IP_ADDRESS, NAME, ADDRESS, DATE_OF_BIRTH, PASSPORT, DRIVER_LICENSE

6 Masking Strategies

hash, partial, redact, nullify, tokenize, encrypt

Automatic Detection

Scans column names and sample data

Connect Your Stack

Works with the tools you already use

Sources

PostgreSQL
MySQL
Databricks
BigQuery
dbt Cloud

Targets

Databricks DLT
Snowflake
S3 Delta
BigQuery
Command
adam generate pipeline \
  --source postgresql://prod-db \
  --target s3://data-lake/raw/ \
  --format delta
pipeline.yaml
pipeline:
  name: prod_postgres_ingestion
  source:
    type: postgresql
    host: prod-db.internal
    port: 5432
  destination:
    type: s3
    path: s3://data-lake/raw/
    format: delta
  transformations:
    - deduplication
    - schema_validation
  schedule: "0 */6 * * *"
100% convention compliance (LLM-enforced)
Introspection across sources (Unity Catalog, PostgreSQL, more)
Type-safe transformations
PII detection & masking
Auto-generated quality rules
State management & resumability (M2)

Deploy Your Way: Self-Hosted OR Hybrid Cloud

🏢 Self-Hosted

  • Pure ADAM licensing
  • Your infrastructure, your LLMs
  • Complete data sovereignty
  • No external API calls
  • Run Ollama, private Claude, or any LLM
llm:
  provider: ollama
  model: llama3.2
  host: localhost:11434
Best for:

Enterprise, regulated industries, high volume

☁️ Hybrid Cloud

  • ADAM license + pay-per-use inference
  • Powered by Maio Labs inference engine
  • No infrastructure to manage
  • Scale on demand
  • Usage-based pricing
llm:
  provider: maio_inference
  api_key: ${MAIO_API_KEY}
Best for:

Teams wanting simplicity, variable usage

Same features. Same speed. Your choice.

Proven Results

The Numbers Tell The Story

results.subtitle

0%
Reduction in pipeline development time
0sec
Per pipeline (vs 4 hours manual)
0%
Convention compliance
0
Copy-paste errors
Built for Databricks Delta Live Tables
Enterprise-ready
Licensed by Maio Labs

Get Started in Minutes

From installation to value—fast

01

01 Install

pip install adam-core

pip install adam-core
02

02 Configure

Set up your data sources and conventions

03

03 Deploy

adam generate --source your-catalog

adam generate --source your-catalog
04

04 Value

Production pipelines in 30 seconds

Multiple Interfaces

Use Adam however you work best

Adam CLI Agent

Interactive command-line agent for guided pipeline generation with natural language commands

$ adam agent > Generate pipeline for orders table ✓ Analyzing schema... ✓ Generating config... ✓ Done!

Adam CLI for Automation

Scriptable CLI for CI/CD pipelines, scheduled jobs, and automated workflows

$ adam generate \ --source prod-db \ --output ./pipelines \ --format dlt ✓ Generated 12 pipelines

Adam API

RESTful API for integrating pipeline generation into your existing tools and platforms

POST /api/v1/generate { "source": "unity-catalog", "catalog": "main", "schema": "bronze" }
Get Started

Ready to Deploy Your Agent Team?

Join forward-thinking data platforms using AI agents to automate pipeline generation.

Free 30-minute consultation
Personalized demo with your data
No commitment required

Schedule Your Demo

Fill out the form below and we'll be in touch within 24 hours.

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