Embedded with the work
We sit with the people closest to the workflow, map the real constraints, and find the narrow places where AI can change speed, margin, or decision quality.
Deployment
AI that leaves the demo room.
We move from discovery to production with the controls, monitoring, and operating support needed for real teams to depend on the system.
Why This Exists
The bottleneck is no longer model capability. It is deployment inside the business.
Enterprise AI fails when it is treated like a tool purchase. The hard part is the surrounding harness: data access, governance, workflow design, feedback loops, exception handling, and the judgment of the people who know the business.
Our deployment model brings those pieces into one room. We pair Levy Street engineering with your operators and domain experts, then build until the system is creating measurable value in production.
Built around your systems
Models are only useful when connected to permissions, data, tools, approvals, reporting, and the legacy software your team already depends on.
Measured in production
Every deployment is instrumented against an operating metric: cycle time, support volume, conversion, cost-to-serve, throughput, or revenue lift.
Deployment Model
A focused path from executive intent to working infrastructure.
Discover
Find the valuable workflow
We identify the work where high judgment, repetitive context gathering, or slow handoffs are creating measurable drag.
Prove
Ship a working system
We build the first production-grade version with real data, real users, evals, guardrails, and observability from the start.
Scale
Turn usage into infrastructure
We harden integrations, document operating ownership, train teams, and generalize the patterns that can become durable company capability.
What We Bring
Product judgment, software depth, and AI systems experience in one deployed team.
Levy Street is deliberately lab agnostic. We choose OpenAI, Anthropic, Google, open models, or traditional software based on what your deployment needs to do reliably.
Agentic workflow design
Evaluation harnesses
Tool and API integration
Data access patterns
Human approval loops
Security and permissions
Model selection
Production observability
Team enablement