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Alli for Enterprise AI
Take AI past the pilot. Keep control as it spreads.
Most companies have AI. A handful of people use it. Alli for Enterprise AI puts Alli4u on every desktop, grounds answers in shared definitions, connects agents to your data through the Alli MCP Server under each person’s own permissions, and sends what people build through review in Alli Apps before it goes live. Your people decide what gets switched on, shared, and approved.
- Available to PMG clients
- SOC 2 Type II
- Built by PMG
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Customer impact
What does AI look like when it runs in production?
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Read the Nike story The challenge
Why does enterprise AI stall after the pilot?
Getting a model to answer a question is the easy part now. Getting AI used by the whole company, on company data, without a security review stopping it, is where most programs stall.
The pattern repeats. A small team gets access. Costs are hard to see, so nobody can defend a bigger budget. Security teams spot API keys pasted into chats and say no. The prompt that worked for one person stays on that person’s laptop, and the next person starts from zero. Meanwhile other people sign up for their own AI tools, and nobody knows what data is going where.
None of that is a model problem. It’s a rollout problem. People need access they can be trusted with, answers that match the numbers finance already uses, a safe way for agents to reach real systems, and a route from one good idea to a tool the whole team can open. Without those, AI stays a specialist’s experiment.
Only specialists use it. Access stays with a small expert team, so only that team benefits
Alli4u enables models user by user and gives each person a workspace with pre-built skills
Costs nobody can see. Spend is scattered across subscriptions, and no one can justify scaling
Alli4u tracks token usage for each person, so cost is visible person by person
Security keeps saying no. Loose API keys and unclear data boundaries stall approvals
Alli4u locks down keys, tokens, and secrets, and separates configurations by customer, business unit, or product line
Answers that don’t match the dashboards. An AI answer about revenue disagrees with the number finance uses
The Semantic Layer defines each metric once, and Alli’s agents answer against those definitions
Agents with no safe way in. Custom integrations are slow to build, and ad hoc scripts can see too much
The Alli MCP Server turns Alli’s APIs into tools and secures each call with OAuth 2.1
Good work stays on one laptop. A useful automation never reaches the next person
Skills are published to a shared library, then wrapped in an interface in Alli Apps
Personal tools nobody manages. Individual subscriptions spread with no shared controls
One workspace with enabled models, tracked usage, and shared skills replaces scattered tools
Nothing reviewed before it spreads. More builders means more risk
Alli Apps runs code verification, reviews, and audit trails on every deploy
The system
How does Alli move AI from a pilot to everyday work?
Alli for Enterprise AI is a path with five steps. Each step reuses the one before it, and all of them run inside the same permissions. People get AI. The AI gets grounded in shared definitions. Agents get a safe way into Alli’s data and services. Builders turn what works into skills and workflows. Reviewed tools reach teams and clients through Alli Apps.
Nobody starts over at each step. A prompt that works becomes a skill. Skills that need to run together become a workflow in Generative Workflows. A workflow people need to see and act on becomes an app. The MCP Server sits underneath all of it, so every step reaches data and systems the same way.
AI reaches the whole organization, and you can see who uses what and what it costs
Each person gets a workspace with an AI coding assistant and the models they’re enabled for. Token usage is tracked individually. API keys and secrets stay locked down.
An AI answer about revenue matches the dashboard finance already trusts
Metrics and business rules are defined once. Agents resolve questions against those definitions, and every answer traces to a source.
Agents work inside each person’s access, without a custom integration for every use case
Alli’s APIs become tools an AI client can call. Each request carries the signed-in user’s authorization, and requests are stateless.
One person’s good idea becomes everyone’s tool
The assistant helps turn a described task into a working automation. Good ones are published as skills anyone can install in one click.
Alli4u, Generative Workflows
Tools reach the people who need them, and nobody else
A workflow gets an interface. The app goes through code verification and review, then goes to the teams or clients you choose, with an audit trail.
Explore Alli4u Explore Alli MCP Server Explore Alli Apps Explore Alli Semantic Layer
In action
See enterprise AI in action
Five moments from the path, each shown by the decision it improves.
Put AI in everyone’s hands · Alli4u
Give every team AI, not just the specialists.
Alli4u gives each person a workspace with an AI coding assistant. Admins decide which models each user can use, and token usage is tracked person by person, so teams match the model to the task and leaders see what it costs.
- AI becomes a default tool for the business teams that never had access, and leaders defend the budget with usage numbers instead of a guess.
Turn one idea into a team tool · Alli4u
Stop rebuilding the same automation.
Pre-built skills give teams a starting point on day one. When someone builds something that works, they publish it, and anyone on the team can install it.
- The organization’s AI capability grows each time one person solves a problem, instead of living on one laptop.
Let agents reach your data, inside the rules · Alli MCP Server
Say yes to AI agents without losing control of your data.
The MCP Server reads Alli’s API specifications and registers them as tools. An agent discovers only the tools the signed-in user can use, chains them in one request, and returns a result the person can review before acting.
- Engineers stop writing wrappers and authentication code for every new use case, and practitioners get answers without a ticket.
Ask, and see where the number came from · Alli Semantic Layer
Get an AI answer you can trace.
Ask Alli resolves each request against the metrics and business rules in the Semantic Layer. The answer arrives with the data, the logic, and the source, so a skeptical reader can check it.
- People act on AI answers because they can see where the numbers came from.
Ship a tool people can open · Alli Apps
Turn your best workflow into an app, with review built in.
A workflow gets a real interface, so people who don’t write prompts can run it. Every app goes through code verification and review before it goes live, and access is set by team or client.
- More people can build without adding risk, and nothing reaches users that nobody reviewed.
People in charge
Where do your people stay in charge of AI?
Built to scale your team’s expertise, not replace it.
Alli handles the access, the tracking, the grounding, and the checks. Your IT and security teams decide who gets which models and which tools. Data and finance owners set the definitions. Builders decide what to publish. Reviewers approve what goes live. Nothing that commits budget, launches a campaign, or ships creative goes out without a person’s sign-off.
Enable. Turns models on user by user, tracks token usage, and locks down keys and secrets
IT and AI leads decide who gets which models, and where to expand next
Ground. Resolves each request against shared definitions and shows the source
Data and finance owners agree on the definitions and own any change to them
Build. Helps turn a described task into a working automation
Builders test it, correct it, and decide whether it’s ready to share
Share. Runs code verification, routes the app to review, and records an audit trail
Reviewers approve each app, and owners decide which teams and clients can use it
Act. Drafts the output and queues the change, the launch, or the delivery
The responsible person approves every decision that commits budget, launches a campaign, or ships creative
The foundation
Why does safe AI start with trusted data?
An agent is only as safe as what it reads and what it’s allowed to touch. In Alli, the data layer is built to give the same answer to the same question every time, and every number traces back to its source. The AI works on top of that foundation, not inside it, so what agents say stays explainable. The Semantic Layer defines each metric once, and the MCP Server carries those same definitions to AI assistants outside Alli. Access follows the person, not the tool.
Who it’s for
Who is Alli for Enterprise AI built for?
By role
IT and security teams
Lock down keys and secrets, enable models user by user, and let agents in without losing control of the data.
CIOs and heads of data
Move AI from a specialist project to a daily tool, with usage and cost visible.
Marketing and media leaders
Get working automations and answers without waiting in an engineering queue.
Finance and analytics leaders
See who uses AI, what it costs, and what it produces, and trust that its answers match your reports.
Engineering and platform teams
Prototype an API call in conversation, and stop maintaining one-off wrappers.
By industry
Retail and consumer goods
Many teams and markets using the same shared skills and the same definitions.
Technology and B2B
Engineering teams prototyping against Alli’s APIs without custom integration code.
Your options
How does Alli compare to other ways of rolling out AI?
Most companies roll out AI in one of three ways: individual subscriptions, a pilot run by specialists, or custom integrations built by engineering. Each solves part of the problem.
| Individual AI subscriptions | Specialist pilots | Custom integrations | Alli for Enterprise AI | |
|---|---|---|---|---|
| Who uses AI | Whoever signs up | A small expert team | Whoever engineering builds for | Every enabled user, across the organization |
| Cost visibility | Scattered subscriptions | A pilot budget | Hard to attribute | Token usage tracked per person |
| What the AI reads | Whatever file someone uploads | A walled-off copy | Whatever the script queries | Alli’s trusted data, resolved against shared definitions |
| Connecting to systems | Limited | Built per project | Wrappers and auth code for each case | API specifications become tools automatically |
| Access control | Personal accounts | Project by project | Varies by build | The signed-in user’s permissions, with OAuth 2.1 |
| Review before launch | None | Varies | Depends on the project | Code verification, reviews, and audit trails on every app |
| Role of people | Do it all alone | Run the experiment | Wait for the build | Lead every step, with AI scaling their expertise |
Getting started
Where should an AI rollout start?
Start where the friction is. Each entry point connects to the rest of the path later.
| If your biggest problem is... | Start with | First step |
|---|---|---|
| Only a few people use AI | Alli4u | Enable one or two teams beyond your specialists, give them a handful of pre-built skills, and turn on usage tracking from the first day |
| AI answers disagree with reports | Semantic Layer | Pick the KPI that causes the most arguments, agree one definition, and set it |
| Good work stays on one laptop | Alli Apps | Take a workflow people keep asking you to run, turn it into an app, and share it with one team |
| Teams rebuild the same automation | Alli4u skills | Publish the best version as a skill so everyone installs one copy |
Learn the concepts
Key concepts in enterprise AI
- What is governed AI?AI that runs on governed data, within defined permissions, with audit trails and human approval, so its answers and actions can be checked and trusted.
- What is enterprise AI adoption?How widely and how well people across a company use AI in their daily work, measured by who uses it, what it costs, and what it delivers.
- What is enterprise AI integration?Connecting AI models and agents to the systems, data, and tools a company already runs on, so AI works on real business information.
- What is a forward deployed engineer?An engineer who works inside a customer's business to connect AI to their real systems, data, and workflows, and ships it to production.
- What does it take to move AI from prototype to production?The work of turning an AI demo that impresses in a meeting into a tool people use every day, with security, review, and support.
- What is an AI coding assistant?An AI tool that helps people write, explain, test, and fix code by describing what they want in plain language.
Alli for Enterprise AI FAQ
How Alli helps companies use AI every day, with security and people in control.
What is Alli for Enterprise AI?
Alli for Enterprise AI is a connected set of Alli products that gets AI used across a company. Alli4u gives people a workspace, the Semantic Layer grounds answers in shared definitions, the MCP Server connects agents to Alli under each user’s permissions, and Alli Apps turns reviewed workflows into shared tools.
Why do enterprise AI pilots stall?
Usually because of the rollout, not the model. Access stays with a small group, costs aren’t tracked well enough to justify expansion, security concerns slow approval, and useful work never gets shared. Alli addresses each: per-user access, per-person usage tracking, locked-down credentials, and a shared library of skills and apps.
How does Alli keep AI answers grounded in company data?
Ask Alli resolves each request against the metrics, dimensions, and business rules in the Semantic Layer, the same definitions that power Alli’s dashboards. Answers show the data, the logic, and the source behind them. If a number looks wrong, someone can trace exactly where it came from.
How does Alli control what AI agents can see and do?
The MCP Server uses OAuth 2.1 with PKCE and forwards the signed-in user’s authorization with each request, so agents act within that person’s access. Requests are stateless, which means no tokens are stored between calls.
Can agents take actions, or only read data?
Both, depending on permissions. Agents can inspect configurations and query data, and they can trigger workflows or build dashboards when the user has rights to do so. Teams decide which tools to enable, so read-only setups are possible.
Who approves what AI builds or does?
People do, at defined points. Every app in Alli Apps goes through code verification and review before it goes live, with an audit trail. Drafts from Ask Alli and workflows arrive for review. Decisions that commit budget, launch a campaign, or ship creative need approval from the responsible person.
How do you track AI usage and cost?
Alli4u tracks token usage at the individual level, and admins enable models user by user. That makes spend visible person by person and team by team. Leaders can use it to justify expanding access and to see where AI is producing the most value.
Who can use Alli for Enterprise AI?
Alli is available to PMG clients. Marketing, media, analytics, IT, and security teams use the same platform PMG’s experts use, so both sides work from the same data and controls. PMG’s Forward Deployed Engineering team also builds with Alli for clients, using Alli Data, the Semantic Layer, Alli4u, and Alli Apps. Book a walkthrough to see how it fits your teams, tools, and security requirements.
AI on every desktop. Control in your hands.
Book an enterprise AI walkthrough and bring the pilot you want to move. We’ll show you how it rolls out to a team, what they can build in the first week, and how your security team sees it. Alli is available to PMG clients.