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NuvantLabs

Data, AI & agentic transformation

Enterprise AI runs on data most companies haven’t built yet.

We build the foundations, then the models and agents on top, then turn what works into products.

Start a conversationSee how we work
  • Founded by Chicago Booth alumni
  • Founders led data & AI at Fortune 10 companies
  • Headquartered in Dallas, Texas

01Capabilities

What we build

Five lines of work. Ideas are tested before they are funded, and the data is fixed before anything is built on it.

  1. 01

    Rapid Prototyping

    Working prototypes on your own data, so the business decides what to fund on evidence instead of a slide.

    • Idea screening
    • Working prototypes
    • Value measurement
    • Go / no-go
  2. 02

    Data Foundations

    Pipelines, models, and governance that make enterprise data usable by someone other than the team that built it.

    • Platform architecture
    • Pipelines
    • Data modeling
    • Governance
  3. 03

    Applied AI & Analytics

    Forecasting, pricing, and decision models tied to a specific P&L line, with reporting that shows whether it moved.

    • Forecasting
    • Optimization
    • ML in production
    • Decision reporting
  4. 04

    Agentic Systems

    Agents that do defined work inside your systems, with narrow permissions, audit trails, and a person where one belongs.

    • Workflow agents
    • Tool integration
    • Evaluation
    • Controls
  5. 05

    Products

    Software built from patterns we have delivered more than once, licensed to enterprises that need the same thing.

    • Domain models
    • Connectors
    • Agent templates
    • Evaluation tooling
Scope, deliverables, and where each leads

02Method

How we work

Four stages, each built on the last. We stay through all of them; the work compounds instead of ending.

  1. Stage 01Data

    Foundation

    Get the data right.

    Reconcile sources, model the core entities, and put an owner on every table that matters.

  2. Stage 02Software

    Products

    Ship what repeats.

    Package the components that recur across the business into maintained software instead of one-off code.

  3. Stage 03Automation

    Agents

    Automate defined work.

    Put agents on top of clean data and stable products, where the error rate can actually be measured.

  4. Stage 04Operations

    Platform

    Run it as a system.

    Consolidate data, products, and agents into one platform your teams operate and extend.

The transformation model

03In practice

Where it usually startsData Foundations → Applied AI

From systems that disagree to a model the executive team plans against.

Most transformations start the same way, whatever the industry: several source systems that don’t reconcile, reporting held together by spreadsheets, and a forecast nobody fully trusts. We bring the sources into one governed model, then build the forecasting and planning models on top of it.

Talk through your situation
Where every model and agent ends up, not in a pilot
Production
Governed model for the core business entities
One
Agreed before any model is built, and measured against after
Baseline

If your data isn’t ready for AI, that is the right place to start.

Tell us what you’re working with. We’ll tell you where we would start and what it would take.

Start a conversation