More AI Agents, Less Consulting Hours

AI Accelerators that Deliver Data & AI Solutions Faster

Migrate that legacy data to the cloud in weeks not months. Build Agents to analyze your data in days not weeks. New DAIS accelerates your AI journey.

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Insight Agent Example
Our Solutions

We architect the systems that transform stagnant data marts into active intelligence hubs, empowering your organization to act with unprecedented speed.

AI Ready Lake Houses Built Fast

Use AI Agents to migrate your local data marts and operational data into a medallion architecture on a modern data platform (Fabric, Databricks, Snowflake)

Intelligence on Demand

Deploy language models specific to your business that interface directly with your lake house, delivering precise analytical insights in real-time.

The Agentic Workforce

Custom autonomous agents capable of multi-step reasoning and API execution, designed to offload complex, repetitive enterprise workflows.
Harness Engineered Long-Run Autonomous Development
Step 01

Planning Agent

Agent development tasks broken into multiple sequential steps with each step achieving specific goals.

Step 02

Build Agent

Each tasks has its own context window where it gathers information generates work while taking notes allowing context window refresh and avoiding context anxiety.

Step 03

QA Agent

An independent agent to evaluate the task with a critical eye. Work loops through redevelopment until the task executes with high quality.

SOC2 Type II

CERTIFIED

Private Cloud

DEPLOYMENT

Zero-Retention

AI MODELS

Common Inquiries
How is New DAIS different from generative AI wrappers?
We build operational agents, not just conversational interfaces. Our systems are engineered to securely connect to your databases, perform complex multi-step reasoning, and execute authorized actions in your core systems.
How do you ensure data security?
We utilize isolated private cloud environments or on-premise deployments. All models run with zero-retention policies, meaning your enterprise data is never used to train generalized models outside your perimeter.
What is the typical deployment timeline?
Initial proof-of-concept deployments targeting a single workflow take 4–6 weeks. Full enterprise integration scales depending on the complexity of legacy infrastructure, typically ranging from 3 to 6 months.
Build Your Agentic Future.

Locations

Atlanta | Charlottesville | Charleston

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