AI that works inside the enterprise.

AI that works inside the enterprise.

We take on the AI problem your team hasn't been able to ship. Then we build it — inside your environment, on your data.

AI engineering for complex enterprises

Trusted across complex enterprise environments

AI Applications

AI applied to real enterprise problems

Examples of how we apply AI to complex data, workflows and decision processes

Defense
Data Fusion
Intelligence Data Fusion
INPUTS
Disparate Sources
AUTO
Fuse
Correlate
Contexualize
RESULT
Defense
Predictive AI
Decision Support
INPUTS
Operational Data
AUTO
Model
Predict
Evaluate
RESULT
MEDIA
AUDIENCE
Audience Intelligence
INPUTS
Audience Data
AUTO
Analyze
Segment
Understand
RESULT
MEDIA
CAMPAIGNS
Campaign Intelligence
INPUTS
Campaign Data
AUTO
Analyze
Predict
Optimize
RESULT
Life SCIENCES
NLP
Patient Intelligence
INPUTS
Patient Data
AUTO
Extract
Structure
Evaluate
RESULT
LIFE SCIENCES
RWE
Cohort Discovery
INPUTS
Clinical Data
AUTO
Phenotype
Cluster
Stratify
RESULT

How we work

From complex problems to production systems

We take ownership of high-value AI problems — from understanding the workflows, data, building, validating, and deploying systems inside your environment

Step 01
Discover

We understand the workflow, data, constraints, and business objective. Together, we define what success needs to look like.

Step 02
Architect

We design the AI approach, system architecture, data flows, and evaluation framework — based on what the problem actually requires.

Step 03
Build & Validate

We build against real enterprise data, test performance, evaluate the system, and address security and reliability requirements.

Step 04
Deploy & Evolve

We deploy into your environment and continue to improve the system based on real-world performance and feedback.

Have an AI problem your team hasn't been able to ship?

Industries

AI built for complex enterprises

Deep domain experience where data, workflows, and security requirements make AI harder to deploy

* From domain-specific AI to secure production systems, we work where enterprise complexity matters.

PROOF & CREDIBILITY

Proven results

Real results from real engagements. We measure success by outcomes, not activity.

01
Clinical Trial Intelligence
Case study
LIFE SCIENCES
52%

potential

reduction

in manual research effort

Problem
Clinical development teams work across protocols, study documents, clinical data, and scientific literature.
Approach:
Secure AI intelligence layer that connects clinical and scientific information, enabling evidence-based synthesis.
Outcome:
Faster information discovery, clearer connections across audience and campaign signals, and stronger decision support.
02
Intelligence Data Fusion
CASE STUDY
DEFENSE
35%

faster
triage

AI-assisted data fusion and retrieval

Problem
Critical information is spread across reports, data, and sensor feeds, making connections difficult to identify.
Approach:
AI-assisted data fusion combines structured and unstructured sources into contextual, traceable intelligence.
Outcome:
Faster information discovery, clearer connections across audience and campaign signals, and stronger decision support.
03
Audience & Campaign Intelligence
Case study
MEDIA
360°

audience
intelligence

Unified audience and campaign signals.

Problem
Campaign, and engagement data is fragmented across channels, making it difficult to understand performance.
Approach:
Combine machine learning and AI to unify audience signals, analyze campaign performance, and automate workflows.
Outcome:
Continuously updated intelligence layer that helps teams identify campaign patterns and make faster data-informed decisions.

What clients say

Our stack

GET STARTED

Let's talk about the AI problem you need to ship

Tell us about the workflow, data, or AI challenge your team is trying to solve.

We’ll explore whether 38 Labs can help take it from idea to production.

A focused conversation to understand your workflow, constraints, data and objectives — and determine whether there is a viable path to production.

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