Managed AI Assistant: Consumer AI Superpowers, With Enterprise Control
Your employees already use AI assistants at home. Metacto brings the same capability into your company, inside your cloud, with control over spend, data access, model training, and every action taken.
Personal AI assistants are all the rage with consumers. They read your inbox, prepare you for meetings, research anything, draft anything, and remember what you care about. For an individual, it feels like a superpower.
For a company, the same thing raises four questions nobody in the consumer app has to answer:
- What is it costing us?
- What data can it reach?
- Is our data training someone else’s model?
- What did it actually do?
Your leadership team wants the superpower. Your security, finance, and legal teams want answers. A Managed AI Assistant gives both.
The offer in one line
Your company’s AI assistant, running inside your cloud. Connected securely to your data and systems, using your approved models, with control over access, activity, security, and spend. Deployed and continuously managed by Metacto.
Don’t Send Your Company Into Another AI SaaS
The default path today is to buy another assistant subscription. Each one asks you to move company context into a vendor’s environment, accept that vendor’s model choices, and trust that vendor’s dashboard for what happened.
We think that is backward. The assistant should come to your environment, not the other way around.
With a Managed AI Assistant:
- The assistant runs in your cloud account. Conversations, memory, and files stay in infrastructure you own.
- It connects to your systems as the user. Email, calendar, documents, CRM, finance, and project tools, with the same permissions each person already has.
- It uses the models you approve. Including models served from your existing cloud provider agreements.
- Metacto runs it. Deployment, integrations, security, updates, monitoring, cost tuning, and continuous improvement.
We run the same architecture inside Metacto today. Every US employee has a private assistant built this way. Here is how we deployed it.
The Controls Leadership Gets
The assistant is only as trustworthy as the controls around it. These are the controls that ship with every deployment.
Managed AI Assistant controls
Controls are configured with your security and finance leads during deployment and reviewed with them on a regular cadence.
Control: Spending
- What you decide
- Budgets per person, team, and model, with alerts and hard limits. Expensive models reserved for the work that needs them.
- What you can see
- Cost by user, team, model, and task, updated continuously.
Control: Data access
- What you decide
- Which systems the assistant can connect to, by role. It inherits each user's existing permissions and never exceeds them.
- What you can see
- Which connectors were used, by whom, and which records or documents were touched.
Control: Model training
- What you decide
- Which model providers and endpoints are approved. Only endpoints with contractual no-training terms are allowed.
- What you can see
- Every model call and where it went. Calls to unapproved endpoints are blocked and logged.
Control: Actions
- What you decide
- Which actions need confirmation, such as sending email, updating records, or sharing files.
- What you can see
- A full audit trail of each tool call, approval, and result.
Control: Identity
- What you decide
- Who gets access, through your existing SSO and groups. Offboarding removes access immediately.
- What you can see
- Adoption and usage by person and team.
Control: Retention
- What you decide
- How long conversations and logs are kept, under your data policy.
- What you can see
- Logs in your own logging stack, not a vendor's console.
Why Start With the Leadership Team
A company-wide chatbot spreads thin. It gets broad access to little context, and usage fades.
The leadership team is different. A CEO, CFO, COO, and their direct reports spend their days on research, analysis, meetings, documents, communications, and decisions that pull from many systems at once. Give them an assistant with real company context and the right access, and the value is immediate:
- Meeting prep that pulls account history, open issues, and recent email.
- Board and investor materials drafted from current numbers.
- Research and analysis grounded in your own documents, not just the open web.
- Follow-ups drafted in your voice, waiting for approval.
Once leadership trusts the assistant, expanding it to the rest of the organization is a configuration change, not a new project.
What Metacto Actually Sells
We do not sell the assistant software, and we do not charge per bot. The open-source runtime underneath is replaceable, and it will keep improving without us.
We sell the managed layer around it:
Open-source assistant runtime → your cloud → your data → your models → Metacto-managed deployment, integrations, context, security, governance, observability, cost controls, and continuous improvement.
That design choice is the core of the value:
You are never locked into this quarter’s assistant. Assistants, agent runtimes, and models leapfrog each other every few months. Because connectors use open protocols, configuration lives in your repository, history lives in your database, and models sit behind your gateway, we can move you to a better runtime or model when one ships. Improvements in the market make your assistant better instead of obsolete.
Your data stays your data. Nothing about the service requires moving company context into a third-party SaaS.
Someone owns it. Most internal AI tools fail after launch because nobody is responsible for keeping them connected, current, and safe. Metacto is responsible. We monitor it, update it, tune cost and quality, add connectors as needs change, and report to you on adoption, spend, and value.
Our rule
Own the management layer, not the commodity layer. The assistant is this quarter’s best engine. The managed service around it is what lasts.
Where This Fits in Managed AI
The Managed AI Assistant is one entry point into Metacto’s managed AI services:
- Managed AI Assistant gives individuals leverage.
- Managed AI Workforce gives functions and teams persistent agent capacity.
- Managed Software Factory gives companies AI-native software development capacity.
They are different front doors into the same capability: we deploy and operate AI inside your company, rather than selling you another isolated AI tool.
How a Pilot Works
- Week 1: Deploy. We stand up the assistant in your cloud, connect SSO, approve models, and set budgets and data-access policies with your security and finance leads.
- Week 2: Connect. We connect the highest-value systems for your leadership team and configure which actions require approval.
- Weeks 3 to 4: Operate. Your leadership team uses it daily. We tune context, cost, and quality, and review the dashboard with you.
At the end, you decide whether to expand, with real adoption, cost, and value data in hand.