EXEMPLARExemplar is part of the NVIDIA Inception program.
NVIDIA InceptionExemplar is part of the Google for Startups program.
Google for StartupsApplied by Exemplar
Enterprise AI, engineered end to end.
We design, build, and run AI transformations for mid-market and enterprise engineering teams.
Applied by Exemplar
The engineering practice behind your transformation.
Marshal, Relay, and DevX Assist power every Applied engagement — and each also ships as a product you can run on your own.
Strategy that ships to production
We define the operating model, then build and ship it ourselves, using the same control plane below.
Embedded with your team
Our engineers work inside your stack, alongside your engineers, from day one.
Governed from day one
Permissions, audit trails, and evaluation are built in from the start.
Live from the console
controls.evaluated.today
0
actions.denied.today
0
actions.ask.pending
0
behavior_guards.active
0
How it works
Every agent call passes through
the same policy surface.
The same visuals that power Exemplar Console — Relay's decision wire, the living MCP gateway, and guardrails — live here so you can see the control plane before you sign in.
Relay
Every agent action, cleared before it runs.
Relay sits on the wire between Claude Code, Cursor, Codex, OpenCode, and your company. Every command, file, and prompt gets allow, ask, or deny — under rules your org writes once.
The platform behind Applied
Marshal, Relay, and the rest of the control plane.
Each one also ships standalone — adopt it on its own, without hiring us to run it.
Marshal
Agent Primitives enabling harness
Tools, prompts, skills, gateway, evals, guardrails, and HITL — the primitives production agents need.
Relay
Hooks that govern Cursor and Claude Code
Control plane for Cursor and Claude Code hooks — allow, ask, or deny before risky actions land.
DevX Assist
Governed MCP for Dev connectors & integrations
Connect Dev integrations and expose them as governed MCP — same policy and audit as the console.
The problem
Your agents are running in production.
Nobody knows what they're spending.
No budget limits. No access controls. No audit trail. Most teams discover the problem after it becomes an incident.
3.2×
average token overspend when agents run without model routing or budget enforcement
74%
of agent failures traced to unrestricted tool access — agents calling APIs they should never reach
0
audit records kept by most teams — no log of what ran, what it touched, or who approved it
Before vs. after
The same agent. Two very different outcomes.
One runs ungoverned — expensive, unrestricted, unaccountable. The other runs through Exemplar.
Without Exemplar
With Exemplar
The numbers
What governed agents actually look like.
Data from engineering teams running AI agents through Exemplar — before and after governance was enabled.
Weekly token spend
before vs. after Exemplar
Model routing distribution
tasks auto-matched to right model
Blocked calls by category
last 30 days across all agents
Agent run outcomes — daily
passed / approved / blocked
Guardrails
Define exactly what your agents
can and cannot touch.
Set access policies once. Every agent — on every framework — operates within them.
Tool access control
agent-code-review · last run 4m ago
Cost governance
budget tracking · all agents · this week
Build the next generation of your enterprise.
Hire Applied to run it end to end, or adopt the platform on your own.
Explore Applied by ExemplarBlog
From the team
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A Software Factory Is Configurable Autonomy — Not Faster Coding
A software factory is how an organization decides, bounds, and proves agent work. The constraint is not generation. It is remaining able to say no — path by path, for engineering and product.
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Enterprise AI Strategy Isn't a Model Choice. It's an Operating Problem.
Most companies don't fail because they picked the wrong model. They fail because enablement, security, and governance show up late. A lot of the rails are open source — the hard part is assembling them into a path engineers will use.
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What Is Loop Engineering? The Complete Guide
Loop engineering is the discipline of designing the plan-act-observe cycle that lets an AI agent complete multi-step work: termination conditions, state, retries, and cost bounds. What it is, how it differs from harness engineering, and how to build one.
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Loop Engineering vs Harness Engineering: What's the Difference?
Loop engineering designs the plan-act-observe cycle an agent runs. Harness engineering governs what that loop is allowed to do. Clear definitions, a side-by-side comparison, and which to build first.
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Loop Engineering: 25 Questions Answered
Every question engineering teams ask about loop engineering — answered directly. What it is, how to design termination and retries, how it differs from harness engineering, cost control, and safety.
ReadAI & platform
The Loop Engineering Checklist: 12 Things Before You Ship a Standing Agent Loop
A practical checklist for teams shipping standing AI agent loops: termination conditions, retry design, cost bounds, safety gates, and monitoring — the 12 things to put in place before a loop runs unattended.
Read