microserv.io

The framework

Five layers. Tailored to your business.

Most working AI systems we have helped operate share the same shape underneath: five layers, in a particular order. The shape stays the same. What goes inside each layer is built around your team, your tools, and your margin structure.
Think of it like a kitchen. The interface is the pass; the logic is the recipe; the guardrails are the food-safety rules; the agents are the cooks; the data is the pantry. None of those are optional in a working kitchen.

Every Microserv.io engagement builds the same five layers, in the same order, inside the tools you already use. We do not bring a platform, and we do not ask you to migrate anything. The framework is a discipline, not a product.

Each layer below is described in plain language. If anything is unclear, the FAQ covers the questions we hear most often, or you can read how we scope the first engagement.

The layers

Each layer in plain language, with what it is for.

  1. 01
    Interface
    Where your team and the AI meet.

    A dashboard, an inbox label, an approval queue in the tool you already use. Anywhere a person can say "yes," "no," or "look at this." We design this layer for the people who will actually use it day-to-day, in the tools they already have open.

  2. 02
    Logic
    Your rules, written down, in plain order.

    Not a long prompt. A small, readable recipe: when X happens, do Y, but only if Z, otherwise hand it to a human. Anyone on your team should be able to read this layer and understand exactly what the system will do next.

  3. 03
    Guardrails
    What the AI can and cannot do — by design.

    Spending caps per task and per month. Tool permissions narrow enough that the worst case is "annoying email," not "deleted records." Approval thresholds for anything sensitive. And a record of every action taken, so nothing happens in the dark.

  4. 04
    Agents
    AI workers that do one job at a time.

    A triage agent. A drafting agent. A reconciliation agent. Each one is small, focused, and runs on the cheapest model that does the job well. Five small workers, each easy to inspect, replace, or turn off — instead of one giant black box.

  5. 05
    Data
    Read live from the tools that already own it.

    The agents read your CRM, your inbox, your tickets, your contracts directly through the live API. We don't build a parallel copy of your data for the AI to fall behind on. If a teammate updates a customer record, the agent sees the change the next time it looks.

A worked example

Refund triage, traced through all five layers.

A small, real-world example. A support team gets around 120 refund-related tickets a day. Roughly half are clearly eligible, low-value, and routine. The agent handles those. The rest go to a human, with a one-line summary and the relevant context already pulled.

  1. 01 · Interface

    A new ticket lands in your support tool. Within 30 seconds, the on-call engineer sees a one-line summary, a confidence score, and a button: "Approve draft" or "Take over."

  2. 02 · Logic

    The recipe in plain language: "If the ticket looks like a refund and the amount is under €50, prepare a draft reply. Otherwise, route to a human with the customer history attached."

  3. 03 · Guardrails

    Per-ticket cost ceiling: €0.04. Daily ceiling: €40. Anything above €50 in refund value needs a human signature. Every model call logged with its cost. The agent can read the customer record but only write to the ticket reply.

  4. 04 · Agents

    Three small workers. A classifier (cheap model, handles 95% of the load). A drafter (mid-tier model, only on the harder 5%). A cost-watcher (no model, just code) that pauses things if the day is unusually expensive.

  5. 05 · Data

    Read directly from your support tool's API and the customer record in your CRM. The only data we write back is the draft reply on the ticket. Nothing is mirrored elsewhere.

What you get when we leave

No platform. No license. A working setup, in writing.

The hand-over is short and concrete. The configuration of each layer in your repo, a runbook your team uses to operate it, and an audit log of every action taken since the system went live, with cost. If we walk away tomorrow, the system keeps running on your accounts and your cloud bill — exactly as it did the day before.

Common questions

The questions that come up before signing.

Do we need to rebuild our stack to use this?
No. The framework installs on top of the tools you already run — your CRM, support tool, inbox, knowledge base. We add coordination; we do not replace what is working.
How long until something is actually working?
The first checkpoint — one process working end-to-end — takes between three and six weeks for most engagements. We commit to the deliverable, not a fixed calendar. If your situation is unusually tangled, we will flag it before you sign anything.
Who owns the code, the prompts, the models, and the data?
You do — all of it. The framework lives in your cloud, runs on your accounts, and we hand over a how-to guide and a record of every action taken. If we walk away tomorrow, the system keeps running.
Why five layers, and not four or six?
Because in practice this is what working systems have: how the team interacts with the work, what rules govern it, what limits keep it sane, who actually does the work, and where the truth lives. Fewer layers tend to skip guardrails. More tend to invent a "platform" tier that nobody asked for.
Is this a product or a platform we install?
Neither. This is a consulting framework. We install configuration, code, and a runbook in your accounts. There is no Microserv.io tenant, no dashboard you log into, no per-seat license.
How does this fit alongside an AI tool we already use?
It sits next to it. If your team already uses one of the big copilots, the framework treats it as one more tool the agents can talk to. We do not ask you to remove things that are working.

30-minute call · no pitch deck

Bring one process. We will install the five layers around it.

A 30-minute call. We listen, we ask, we tell you whether the framework fits your situation. If it does not, we will say so on the call.