ZenSavvy puts AI agents to work
Agents that answer, book, quote and write into your systems, following your business rules, with a person who can always take over.
33%
of enterprise software applications will include agentic AI by 2028, up from under 1% in 2024
Gartner, 2024
40%+
of agentic AI projects will be cancelled before the end of 2027, usually over cost or badly defined scope
Gartner, 2025
78%
of organizations already use AI in at least one business function
McKinsey, State of AI 2025
15%
of day-to-day work decisions will be made autonomously by 2028
Gartner, 2024
95%
of enterprise generative AI pilots never move a single business number
MIT Media Lab, NANDA project, 2025
25 → 50%
of companies already using generative AI will launch agents: a quarter in 2025, half by 2027
Deloitte, 2024
100%
of our agents ship with written limits and a handoff to a human
ZenSavvy, our standard
The customer does not leave because you said no.
They leave because they asked at nine at night and you answered at eleven the next morning.
How many messages go unanswered at your business every week?
Quoting the same price twenty times, booking appointments by hand, retyping into the system what arrived by message, chasing whoever did not reply. These tasks burn hours, need no judgment, and get done worse as the day wears on.
An AI agent reads the message in plain language, works out what is being asked, queries your systems to answer with real data and then executes: books, quotes, records or escalates to a person. It does not walk a menu of options; it follows your business rules.
The first thing you notice is the hours your team stops spending on repetition. The second, and usually the bigger one, is the conversations that used to go cold: the late-night ones, the weekend ones and the rush-hour ones nobody could get to.
The cost of not doing it
Every unanswered message is a sale that went to whoever replied first, and it leaves no trace anywhere. The cost of repetitive work does show up, but disguised: it appears as salary for capable people doing tasks that need nobody capable.
Together we pick the task that burns the most hours or loses the most sales, and we build the agent for that one. Once it is running and you can measure the result, you decide whether the next one is worth it. That is how you stay out of Gartner’s 40% of cancelled agentic AI projects.
If your message volume is low or every conversation is different, an agent will not pay for itself and we will say so. Agentic AI pays where there is repetition: with no repetition, there is nothing to automate.
| Task | Volume | Savings |
|---|---|---|
| Answer pricing questions | ~180 / month | 85 |
| Book and confirm appointments | ~140 / month | 78 |
| Appointment reminders | ~140 / month | 70 |
| Follow up with non-responders | ~90 / month | 52 |
| Write into the patient record | ~140 / month | 40 |
Free includes the task we recommend starting with
Get my automation map →The volumes shown are an example. Your map comes from your real conversations and operation.
Week 1
We go through what your customers actually ask, not what we assume they ask. That is where we learn what the agent can safely answer and what has to reach a person no matter what.
Week 2
We define what the agent can do, what it must never do and when it escalates. An agent with no written limits is the single most common reason an AI project ends up cancelled.
Week 3
We wire it to the calendar, catalog, inventory or CRM so it answers with real data instead of guessing. An agent that cannot query your systems can only improvise.
Week 4
It starts handling conversations with your team watching every one, and gets tuned on real cases. Only once the behavior is right does it run on its own, and the dashboard stays for review.
We answer any request in under 24 hours.
Wired to your catalog, calendar or inventory, the agent answers with the real information at that moment. Without that connection any agent ends up improvising, which is exactly what you do not want in front of a customer.
We hand over, in writing, what it can do, what it must never do and when it escalates. Discounts, date commitments and sensitive cases go to a person, every time.
Your team sees every conversation and can take over at any moment without the customer noticing the switch. The agent handles the volume; the person handles what matters.
How many conversations it handled, how many it resolved alone, how many it escalated and how much time it freed. If the numbers do not justify the agent, we turn it off. It is a service, not a bet.
This comes first by a wide margin. The agent answers with data it queries from your systems, and when it does not have the data it says so and escalates. One invented price costs more than every hour it saved.
A complaint, an exception, an upset customer or a price negotiation is not an agent’s job. Getting that boundary right is what leaves the customer satisfied instead of trapped.
Calendar, catalog, inventory, CRM, billing. Without those connections the agent can only converse, and conversing without executing is the same old chatbot with better prose.
The tone, the manner and the words your business actually uses. An agent that talks like a corporate manual gives itself away in the first line and takes away from the brand instead of adding to it.
Every conversation carries a model cost. We choose the model and the design so the savings are several times the spend, and we show you that number before you start.
Which data the agent touches, where it is stored and for how long. Your customers’ data is nobody’s training material, and that is settled in the contract.
How long does it take? An agent on a single, concrete task is usually handling conversations in 3 to 4 weeks, with a supervised pilot before it runs on its own. Agents that touch several systems take longer, and that number comes out of the automation map.
An agent pays off when there is volume and the questions resemble one another. If every conversation is different or the volume is low, the savings will not cover the cost of building it, and we would rather tell you first.
Businesses with a lot of similar inbound messages, a calendar to fill, or follow-up that is quietly not happening.
Where there is no repetition, or where the personal touch IS the product, an agent subtracts instead of adding.
If your case sits on the right, we say so and there is no proposal. And if the real problem is that nobody is finding you, the work is not an agent: it is search visibility.
Pricing
We do not publish a flat rate because it would be made up: an agent that answers questions and one that operates inside your systems do not cost the same.
An agent is not sold in tiers: the price depends on how many processes it handles, which systems it connects to and how much of your business knowledge has to be loaded into it. We define the scope in writing before talking numbers, and the proposal comes out of that.
Tell us which task is eating the most hours, or where your leads are going cold. We come back with a map of what can be automated, how much time each item frees, and which one to start with.
Get my automation map →We reply in under 24 hours.
What people always ask us
A chatbot follows a decision tree somebody wrote: the moment the customer steps off the script, it stalls. An AI agent understands the message in plain language, queries your systems to answer with real data, and executes actions: booking an appointment, building a quote, recording the customer or escalating to a person. The practical difference is that an agent decides and acts, it does not just reply.
That is the real risk with AI, which is why we treat it as criterion number one. Our agents answer with data queried from your systems rather than from memory, and when they do not have the data they say so and hand the conversation to a person. Prices, dates and commitments always come from a source of yours.
Website chat, WhatsApp, Instagram, Facebook and email. The agent answers on your business account at any hour, books or quotes by querying your systems, and hands the conversation to your team when it should.
There are two parts: building the agent, quoted per project depending on how many systems it touches, and the per-conversation cost of the AI model, which is variable. The automation map shows you both numbers along with the estimated savings, so you can see whether it adds up before you start.
No. Your team sees every conversation on a dashboard and can take over at any moment without the customer noticing the switch. On top of that, the cases we define as sensitive (complaints, discounts, exceptions) escalate to a person on their own.
We define in writing which data the agent touches, where it is stored and for how long, and that data is not used to train anyone’s models. It is one of the first things settled, before any system gets connected.
Yes, as long as they expose an API or we can integrate with them. Normally we wire it to the calendar, catalog, inventory or CRM, because an agent that cannot query your systems can only improvise, and that is exactly what has to be avoided.
An agent on one concrete task is usually handling conversations between week 3 and week 4, with a supervised pilot where your team reviews every conversation before it runs on its own. We always start with a single task: that is what keeps the project from ballooning and getting cancelled.
