Give Your AI Agents Access to Real Data
We build MCP servers that connect Claude, Cursor, and your custom AI tools directly to Snowflake, Databricks, Airflow, and the rest of your stack. No glue code. No security shortcuts. Production-ready in weeks.
Your AI Tools Are Isolated From Your Data
Most teams start with RAG. Feed documents to the model, get answers. That covers static knowledge. But when someone asks "Did last night's pipeline run?" or "What's our revenue this quarter?" the AI has nothing to say. It cannot see your live systems.
Building those connections yourself means custom code for every integration, security reviews for each one, and ongoing maintenance that pulls engineers away from product work. Most teams stall here or ship something half-baked.
MCP Servers Built Right, Once
Model Context Protocol is the standard that makes this work. We build MCP servers that expose your data infrastructure to AI tools through a clean, secure interface. Build it once, and Claude, Cursor, or any MCP-compatible client can use it.
What We Build For You
Custom MCP servers shaped around your stack and how your teams actually work
Natural Language Querying
Your teams ask questions in plain English, get answers from Snowflake, BigQuery, or Redshift in seconds. No SQL required, no waiting on analysts.
Pipeline & Orchestration Access
Monitor pipeline health, trigger runs, and troubleshoot DAG issues across Airflow, Prefect, or Dagster—all through natural conversation with your AI tools.
Catalog & Governance Integration
Agents discover schemas, look up metadata, and trace lineage through Atlan, Alation, or AWS Glue. Finding the right table takes seconds, not hours.
Not sure which capabilities fit your use case?
Get a Free AssessmentHow It Fits Together
Every MCP server we build exposes three types of primitives that AI models understand natively:
Tools (Actions)
Execute SQL queries, run dbt models. The operations that change state or fetch live data.
Resources (Context)
Data catalogs, database schemas, pipeline logs, semantic metrics. Reference material the model loads once and keeps in context.
Transport Layer
stdio for local developer tools, HTTP with Server-Sent Events for cloud deployments. We match what your environment needs.
Security Your Compliance Team Will Approve
Giving AI access to production systems raises questions. We build the controls that turn those questions into checkboxes.
Role-Based Permissions
Read-only for analysts, write access for ops, admin only where needed. Every action stays within bounds you define.
Data Masking & PII Handling
Sensitive fields get stripped before context hits the model. Your compliance team signs off, and you move forward.
Full Audit Trails
Every query, tool call, and response logged in real time. Answer security reviews in minutes, not weeks.
Sandboxing & Rate Limits
Prevent runaway queries and token blowouts. No surprise bills, no production incidents from AI overreach.
Need to pass a security review before you can move forward?
We provide documentation, architecture diagrams, and direct support for your infosec team.
Request Security DocumentationClear Process
From First Call to Production
A straightforward path with defined milestones. No scope creep, no surprises.
Strategy & Feasibility
We map your architecture, identify high-value use cases, and give you a realistic assessment before any code gets written.
Server Development
Custom MCP servers built in Python or TypeScript, shaped around how your teams actually work. Not generic, not off-the-shelf.
Production Deployment
Kubernetes, Docker, or serverless. OAuth 2.1 auth, gateway setup, monitoring dashboards. Production-ready, not demo-ready.
Managed Operations
Schema drift, new tools, performance tuning. We handle the maintenance so your team stays focused on what matters.
Strategy & Feasibility
We map your architecture, identify high-value use cases, and give you a realistic assessment before any code gets written.
Clear roadmap & ROI estimateServer Development
Custom MCP servers built in Python or TypeScript, shaped around how your teams actually work. Not generic, not off-the-shelf.
Working servers in stagingProduction Deployment
Kubernetes, Docker, or serverless. OAuth 2.1 auth, gateway setup, monitoring dashboards. Production-ready, not demo-ready.
Live, monitored systemsManaged Operations
Schema drift, new tools, performance tuning. We handle the maintenance so your team stays focused on what matters.
Zero maintenance burden30 minutes. No pitch deck. Just an honest assessment of your use case.
Built For Your Stack
We work with the tools you already run
- Claude Desktop
- Cursor
- Visual Studio Code
- Snowflake
- Databricks
- PostgreSQL
- dbt
- Azure
- AWS
- Docker
- Kubernetes
Using something else? We likely support it. Just ask.
Data Engineers Who Build MCP Servers
We are a data engineering company that builds MCP servers, not the other way around. When you work with us, you get people who understand warehouse performance, pipeline orchestration, and data governance deeply. The MCP part is how we expose that infrastructure to AI.
That matters because the hard part is not writing MCP code. It is knowing which tools to expose, how to structure context so models stay on track, and where to draw the line on permissions. Those decisions need data engineering judgment.
Teams typically come to us when:
- They need AI agents to query live production data, not just static documents
- Security and compliance requirements rule out quick-and-dirty solutions
- Their engineering team is stretched and cannot take on another integration project
- They tried building MCP servers internally but hit walls on design or security
Sound familiar?
Let's Talk About Your SituationOften Paired With
Get Started
Ready to Connect Your AI to Real Data?
Book a 30-minute call. We'll assess your use case, tell you honestly if MCP is the right fit, and outline what it would take to get there. No slides, no pressure.
