A managed AI employee for your service business, installed and run by us.
Agenvanta installs a dedicated AI employee (default name: Mia) inside your business. It learns your company, connects to approved tools, handles recurring admin, research, and follow-up work, and improves every week. You do not manage models, prompts, tokens, infrastructure, debugging, or memory. We run all of it.
One named employee, a real job description, and someone running it for you.
A managed AI employee is not a chatbot, not a workflow installer, and not a SaaS dashboard you log into. It is a dedicated agent with a name, a role, a memory vault, scoped tool access, recurring rituals, and human approval gates on anything customer-facing. Mia shows up every day, handles the jobs she has been assigned, surfaces open loops before they slip, and sends you a weekly digest of what she carried.
The point is that the operating layer (models, prompts, tools, memory, monitoring, debugging, weekly improvements) lives with Agenvanta. Your team gets the output. We carry the rest.
Concrete recurring jobs, not generic "AI assistant" promises.
These are the jobs we install first, before anything vertical-specific. They are designed to remove the recurring back-office work that quietly costs SMBs revenue every week.
Meeting summaries and action extraction
Mia turns call notes (or transcripts, if approved) into decisions, open loops, owners, and due dates. Each summary lands in the channel you choose, ready for a quick human review.
Follow-up draft generation
Client, vendor, and internal follow-ups get drafted on a schedule (or after a trigger like a meeting). Drafts go to a human approval queue. Nothing customer-facing sends without you approving it on day one.
Open-loop tracker
A live list of people, projects, due dates, and blockers. Mia keeps it current from meeting notes, email, and your project board, and flags items that have gone quiet.
Owner digest
One short report on a schedule you choose (daily, weekly, or both) covering what Mia handled, what is stalling, and what needs your attention. No new app to log into.
Account and prospect research
Mia pulls public context on prospects, clients, and accounts (company background, recent news, decision-makers, relevant context for the next meeting) so your team is not researching from scratch.
Internal status updates
Recurring status reports, project rollups, and "where things stand" summaries for the partners or team leads, on the cadence you set.
Everything the customer should not have to think about.
This is the work that usually kills self-built AI rollouts. We carry it so the AI employee keeps getting more useful instead of slowly going stale.
Company memory vault
We build and maintain the operating brain: company facts, services, team, clients, SOPs, tone, rules, projects, preferences, and recurring decisions. The vault is updated continuously as the business changes.
Tool access and permissions
We wire up approved tool access with scoped permissions (email, calendar, docs, CRM, project boards, accounting, Slack or Teams). No broad scopes on day one. Every connection is reviewed and logged.
Approval gates and human review
Customer-facing actions and sensitive operations go to a human approval queue. We set up the queues, the review owners, and the rules. Your team approves; we run the plumbing.
Monitoring, watchdogs, debugging
Uptime checks, error alerts, output quality monitoring, and a watchdog on every recurring job. When something breaks, we get the alert and we fix it. You do not debug the system.
Weekly improvement loop
Every week we ship improvements: better memory, tuned behavior, new skills, fixed failure modes. The AI employee gets more useful over time instead of flat-lining after the launch.
Progress updates and ROI narrative
A weekly progress note and a running "what the AI employee now handles" doc, so you can see exactly what you are paying for and where the next improvement is going.
Honest guardrails, in writing.
Day-one behavior is read, draft, suggest, and report. Autonomous customer-facing sends are not the default; they only get unlocked job by job, after you have seen Mia handle the drafting well and you have explicitly approved automation for that job.
- No broad account scopes on day one. Tool access is scoped to the specific data and actions the job needs, not all-or-nothing.
- Human review on customer-facing actions. Outbound emails, client messages, and external sends go to an approval queue. You approve, then the action runs.
- No regulated delivery work. No healthcare or finance compliance workflows on day one. No legal, tax, medical, or binding insurance advice, ever.
- No replace-your-staff claims. The AI employee carries specific recurring jobs. Your team still runs the business.
- No guaranteed-revenue promises. We are honest about what an AI employee can and cannot move.
- Clean offboarding. If we part ways, tool access is revoked, the memory vault is returned, and credentials are rotated. Documented up front.
From blank slate to operating presence in a month.
The playbook is the same whether you are an agency, a law firm, an insurance shop, a real estate team, or another high-touch service business. Verticalization happens in weeks three and four.
Days 0 to 2 · Install the employee
Name and role the AI employee, define permissions and approval rules, create the initial company memory vault, set up the communication channel, and ship the first useful output.
Days 3 to 7 · Make it useful
Stand up two or three recurring jobs: meeting summaries with action extraction, follow-up draft generation with human approval, and the open-loop tracker or weekly status digest.
Days 8 to 14 · Connect tools and stabilize
Wire up approved tool access (email, calendar, docs), launch the customer request board, add uptime and watchdog checks, route error alerts to us, and turn on the human review workflow.
Days 15 to 21 · Verticalize
Add business-specific jobs. Agency: client status updates, content and proposal research, deliverable tracking. Law firm: intake and admin summary, case open-loop tracker, client follow-up drafts. Real estate: lead follow-up drafts, showing and appointment admin, market and listing research. Insurance: renewal follow-up drafts, document request drafts, policy and admin summaries.
Days 22 to 30 · Make it feel like an employee
Ship the weekly progress update, the "what the AI employee now handles" doc, the next five-item improvement backlog, the ROI narrative, and the renewal and expansion path.
Beyond day 30
Weekly improvements continue. New jobs get added. The memory vault deepens. The AI employee becomes a real recurring presence inside the business, not a launch you have to keep alive.
One retainer for the whole AI employee.
The retainer covers the AI employee plus every recurring job it carries. Not a per-workflow fee. Not a usage meter. We do not bill for tokens, models, or infrastructure; that is our cost to carry, not yours.
Standard managed AI employee retainer. Includes install, company memory vault, approved tool access, recurring jobs, monitoring, weekly improvements, and the owner digest.
For the first 1 to 2 pilot members, in exchange for fast feedback and case study rights. Same service, reduced retainer while we build the first public case studies.
Common questions about the managed AI employee.
Is this just a chatbot?
No. A chatbot waits for someone to type at it. A managed AI employee has a job description, a company memory vault, approved tool access, and recurring rituals it runs on a schedule (meeting summaries, follow-up drafts, open-loop tracker, owner digest). It takes initiative inside the boundaries you approve, and a human reviews customer-facing output before it goes out.
What tools can it use?
Only the tools you scope and approve. Common day-one access includes Google Workspace or Microsoft 365 (email, calendar, docs), Slack or Teams, your CRM (HubSpot, Pipedrive, Close, and similar), project boards, accounting (QuickBooks, Xero), and shared drives. We do not take broad account access on day one and we do not connect anything you have not explicitly approved.
Can you replace my staff?
No, and we will not pitch it that way. The AI employee carries specific recurring jobs (summaries, follow-up drafts, tracking, research, digests). Your team still runs the business. The goal is to take low-creativity recurring work off your team so they can do the work only humans can do.
What if it makes a mistake?
Day-one behavior is read, draft, suggest, and report. Customer-facing actions go to a human approval queue before sending. Agenvanta monitors output, runs error alerts and watchdogs, and we own debugging. If a recurring job is producing bad output we pause it, fix it, and ship an improvement that same week.
How is this different from a ChatGPT Team seat?
A ChatGPT Team seat is a blank chat each person on your team has to prompt themselves. There is no shared company memory, no scheduled jobs, no approval gates, no tool integration beyond what each user wires up, and no one improving it for you. A managed AI employee has a persistent role, a company memory vault, scheduled rituals, tool access, and a team (us) running monitoring and weekly improvements.
How long until it is actually useful?
Days 0 to 2 we install the employee (identity, memory vault, approval rules, communication channel). Days 3 to 7 we ship the first useful recurring jobs (meeting summaries, follow-up drafts, open-loop tracker). By day 14 approved tool access is connected and you are getting a weekly owner digest. By day 30 the AI employee is verticalized for your business and operating as a real recurring presence.
What will you not do?
No legal, tax, medical, or binding insurance advice. No regulated delivery work day one (healthcare and finance compliance are not where we start). No autonomous customer-facing sends in week one. No broad account scopes. No guaranteed-revenue or replace-staff claims. No fake case studies.