Agentic AI & Data Engineering Solutions

What We Do

Build AI Agents for Any Task — Repetitive or Complex

We design and deploy autonomous agents that eliminate repetitive busywork — daily anomaly detection, automated reports, invoice processing — and tackle complex multi-step workflows that used to require a team. Your people focus on decisions; the agents handle everything else.

Connect Your Data Smartly to AI Agents

Implement RAG pipelines, vector databases, and semantic search to give your AI agents accurate, up-to-date context from your own data sources. Stop hallucinations; start reliable answers.

High-Performance Data Engineering

Ingest massive data loads with resilient, scalable pipelines. From real-time streaming to batch ETL, we ensure your data infrastructure is always ready to feed your AI systems.

Agentic Solutions in Practice

AI agents aren't magic — they're well-designed systems with clear instructions, reliable data access, and the right tools. Here's how we build them.

MCP Configuration

Model Context Protocol (MCP) lets AI agents securely connect to your file systems, databases, and APIs — using a single open standard.

{
  "mcpServers": {
    "database": {
      "command": "uvx",
      "args": ["mcp-server-postgres"],
      "env": {
        "POSTGRES_CONNECTION_STRING": "${DB_URL}"
      }
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/data/reports"]
    },
    "slack": {
      "command": "uvx",
      "args": ["mcp-server-slack"],
      "env": {
        "SLACK_BOT_TOKEN": "${SLACK_TOKEN}"
      }
    }
  }
}

Example: Daily Report Agent

A real agentic workflow we ship for data teams:

Agent queries your data warehouse via Database MCP
Summarizes findings using Claude or OpenAI
Posts formatted report to Slack via API MCP
Flags anomalies and creates tickets via Jira MCP

No human in the loop for routine runs.

MCP Servers We Deploy

File System MCP — read/write structured outputs
Database MCP — direct SQL query access for agents
API MCP — connect agents to REST APIs (Salesforce, HubSpot, etc.)
Web Search MCP — real-time research capabilities

Any MCP-compatible model can use any MCP server — swap Claude for OpenAI in one config line.

AI + Data Engineering — Fully Integrated

We bridge the gap between your data infrastructure and the AI models your business wants to leverage.

LLM Integrations

Claude API Anthropic
Native MCP support, best for complex reasoning
OpenAI GPT-4o / o3
Broad ecosystem, via LangChain or Agents SDK
Ollama Self-hosted / On-prem
Llama, Mistral, Qwen — data stays on your infra
LangChain LangGraph Orchestration
Stateful multi-step agents and pipelines

Data Engineering for AI

ML Pipelines — feature stores, training data prep, model serving infrastructure
Vector Databases — embedding generation, similarity search, RAG setup
Streaming Ingestion — real-time AI context via Kafka, Kinesis, or Pub/Sub
Massive Load Ingestion — scalable batch ETL built to handle billions of records fast
Data Quality Agents — automated validation before LLM consumption to prevent garbage-in/garbage-out

About the Team

We are senior data engineers and AI architects with a track record of delivering production systems — not prototypes.

Metadata Morph was founded by practitioners who spent years inside high-scale data organizations — building pipelines that process billions of events, architecting lakehouses from the ground up, and deploying ML systems in production.

We started seeing the same pattern everywhere: companies had solid data infrastructure but no bridge to AI. The warehouse sat full of valuable data that no agent or LLM could reliably reach.

We built that bridge.

Today we work with data teams across industries to design and ship agentic AI systems grounded in production-grade data engineering — the kind that runs reliably at 3am without anyone watching it.

What we bring

20+ years production data engineering
End-to-end AI agent design and deployment
MCP configuration and tooling
Lakehouse architecture (Parquet, Iceberg, Delta, Hudi)
dbt, Airflow, Kafka, Spark at scale
Claude, OpenAI, and Ollama integrations
Data quality frameworks for AI pipelines

Get in Touch

Our Tech Stack

AI-first tools and proven data engineering technologies we deploy together

ClaudeOpenAIOllamaMCPLangChainLangGraphRAG / Vector DBsBedrockApachePythonSQLDBTAirflowSnowflakeGCPAWSTalendPostgreSQLVerticaRedshiftAnd More...

Seamless Integrations

Connect your AI agents and data pipelines to the tools your business already uses

Claude (Anthropic)OpenAI APIOllamaLangSmithAPI RestSalesforceWorkdayZoomHubSpotSlackJiraCoupaNetSuiteAnd More...

We Built the Bridge Between Your Data and AI. Ready to Cross It?

Book a 30-minute strategy session. We'll map your most repetitive workflows to an agentic solution you can ship in weeks.

Book Your Strategy Session

AI That Pays for Itself

Real automations. Real labor replaced. Ordered by business impact.

The $250K Employee You Can Replace with an MCP Agent

Every company has highly paid people spending most of their day reading, routing, and copying data between systems. MCP agents eliminate that work entirely — here's where the ROI is highest, and how we build the DBA case from the ground up.

Stop Guessing: How to Migrate Presto to BigQuery Without Breaking Your Analytics

SQL migrations fail in the last 20% — the subtle dialect differences that pass code review but silently return wrong results. Here's how to automate Presto-to-BigQuery migration with proof of correctness before a single query touches production.

Building an AI Data Layer on Top of Your Existing Data Lake and Warehouse

Learn how to build an AI Data Layer on your existing Snowflake, Redshift, or BigQuery warehouse using dbt as the semantic manifest and multiple specialized AI agents connected via MCP.

Building Your First AI Agent with MCP: A Practical Guide

Learn how to configure MCP servers and build an AI agent that automates daily data reporting using Claude, OpenAI, or Ollama — with real JSON config examples.

High-Speed Network Security Log Analysis with msgspec and AI Agents

How to use msgspec for ultra-fast network security log parsing and feed the results to an AI agent that detects threats, correlates events, and generates incident reports.

AI Resume Screening Agent: Ranked Shortlists Without the Manual Review Hours

Build an AI agent that scores applicants against a structured job rubric, produces a ranked shortlist with reasoning, and flags edge cases for human review.

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