Agentic AI implementation
Built to be you. Built for you to own.
Agents that carry the work you repeat: the answering, the chasing, the retyping between systems. Built from your brand first, and standing up in your own accounts.
Why it's hard to say yes
The hard part was never the technology. It's trusting software to act in your name.
Most teams already know the repeat work is worth automating. What stops the yes is what comes after: letting something act on real records, in front of real customers, with your name on every reply. That is the part this page is about.
The same work, every day, by hand: what is it quietly costing us?
Is this actually worth automating, or are we chasing the hype?
Can we trust software to do the work, not only answer a question?
What happens the first day it gets one wrong?
Why brand-first
Your agent is your most repeated voice.
It talks to customers more often than any ad you run. On a default persona, that is thousands of conversations a year sounding like everyone else. Built from your brand first, every reply is unmistakably yours.
Hi! Do you deliver to Lugano, and roughly how long?
Hi! Yes, we deliver to Lugano, usually in two days. Want me to confirm the exact date for your postcode?
Sounds like you.
What it solves
The work it takes off your team
Not the judgment calls. The repetitive middle of the day: read, decide, type, repeat.
Questions that repeat
The same twenty questions arrive every day, on every channel you have. The agent answers them at once, in your voice, and hands the hard ones to a person by name.
Leads that go cold
An enquiry answered in a minute books; one answered tomorrow shops around. The agent replies at once, asks the qualifying questions, and puts the meeting in your calendar.
Retyping between systems
The order lives in one tool and the invoice in another, so someone copies it across by hand. The agent moves the record itself, and the errors retyping causes stop arriving.
Process
How we build it
Five stages, and the spec names every tool it picks and why, so the build stays about your business, not our habits.
- 01
The spec: your brand, its jobs, its brain
Before anyone opens an editor, you approve one page: which jobs the agent takes, what it may read and write, when it hands off to a person, and first, how it speaks, in your voice. Then we pick its brain per job: Anthropic's Claude for deep reasoning, OpenAI's GPT for fast structured logic, Google's Gemini for reading thousand-page archives. The spec says which one, and why.
- 02
The build stands up in your cloud
We build inside the cloud you already run: AWS Bedrock, Azure AI, or Google Cloud. The steps live in n8n when the path is drawable, in LangGraph when the job holds state and waits on an approval, in agent code on the OpenAI Agents SDK or Google's ADK when the deciding is the work itself. Nothing is staged on our servers, because that copy is where ownership quietly goes missing.
- 03
It connects over MCP, not glue code
Your agents reach your tools through MCP, the open protocol Anthropic published: your CRM, your Postgres database, GitHub, Slack, your calendar. n8n's catalogue covers several hundred services beyond that, and UiPath drives the legacy screens that never got an API. Connecting a tool is a scoping row in the spec, not a rebuild.
- 04
It rehearses, traced, and we break it
The agent runs against your live records with writing off until you stop disagreeing with its replies. Every decision is traced in LangSmith or Langfuse, so you can read its reasoning step by step; risky actions wait at a human approval checkpoint, personal data is scrubbed, spend is capped. Then we cut a live connection while you watch, and watch it recover. A failure path that has never run is one you meet on a Saturday.
- 05
Handover is the last day
Your team operates the system while we watch, not the other way round. The traces feed the dashboards your IT already runs, Datadog or New Relic, so oversight stays in-house. Then our access comes off and the project is over. You keep:
- The approved specification, including how it speaks.
- The workflow and agent code, as files in your repository.
- Every prompt, as plain text you can read and edit.
- The runbook: how to turn it on, off, and who to call.
Who it's for
Built for the person who has to live with it.
You want the repeat work gone, and you want to own what replaces it, not rent it back a month at a time.
Your team retypes between systems all day, and you already know which hours you would take back first.
You bought a bot that sounded like everyone else, or a tool you could never actually change. Not twice.
What it's worth
Take back the week.
Not a saving we promise you; a number you can check against your own week. Count the hours your team spends on the work that repeats, and that is the load an agent is built to carry, every week of the year.
Hours a week, by hand
960hrs
hours a year off your team's hands
That is about CHF 48’000 a year in loaded labour, at CHF 50 an hour.
One agent carries the work you would otherwise retype every week.
Built once and owned by you: the hours come back, the salary line holds.
Your people spend the reclaimed hours on judgment and on customers, the work an agent is not for. Taking the rest off them is the whole point.
Bring us the job you repeat.
Twenty minutes, no pitch. We tell you what we would build, and what a spreadsheet would fix for free.
FAQ
About the build
What people ask once the thing is worth building.
Like your business, by design. Before the build we write how the agent speaks, what it will and will not say, and how it hands a conversation to a person, and you approve it as part of the specification. That voice lives in the prompts your agent runs on, which ship to you as plain text files you can read and change. A default corporate persona is the first thing we remove, not the fallback you are left with.
A program that receives a trigger, reads a defined set of sources, chooses between actions using written rules and a language model, and writes the result back into systems your team already uses. Agentic means it acts rather than only answers. In practice it holds credentials, has scoped permissions, and follows a written escalation rule, and those three things are what most of the build is about.
Weeks for a first system, and the specification stage is what decides. Once the rows are agreed the build is mostly wiring, and wiring is quick. When a project runs long it is almost always because the process underneath was never written down, which the specification stage surfaces in week one instead of week six.
Yes, and that is why PlainAI ships the workflow and the prompts as files. Editing a prompt is editing a text file in your repository. Changing an escalation rule is changing one step in a workflow you own. Larger changes are a piece of work you can hand to us, to your own developer, or to anyone who can read the files.
It is built to fail in one direction: when a question has no documented answer the agent escalates to a person instead of guessing, and its write permissions are scoped tightly enough that the worst case is a row you can delete. Every decision it makes is logged with a timestamp and the input that produced it, so a wrong answer is something you can find, read, and correct in the rule that caused it.
Your team, for most of it. The runbook and the training session are built for exactly that: the error log names what failed, and the failures worth planning for have a documented response written before launch. When you would rather hand a change to us, you ask, and we quote it as its own piece of work. Nothing about the system depends on a standing contract with PlainAI.
Generative AI answers; agentic AI executes. A generative tool waits for a question and produces text. An agentic system receives a trigger, reads your records, decides against written rules, and writes the result back into your CRM, your calendar, or your database, without a person retyping it. That difference is why the build is mostly permissions, escalation rules, and traces rather than prompts, and it is the difference you are paying for.
The one the job calls for. We pick from LangGraph for stateful, auditable graphs, the OpenAI Agents SDK and Google's Agent Development Kit for code-first agents, Microsoft's Agent Framework inside Azure estates, and CrewAI where a role-based team of agents fits the work. If your business runs on Salesforce or Microsoft 365, we build with what is native there, Agentforce or Copilot Studio, rather than beside it. The specification names the choice and the reason before anyone opens an editor.
Guardrails are written into the specification, not promised afterwards. High-risk actions wait at a human approval checkpoint. Personal data is scrubbed before it reaches a model. Spend is capped per agent, per day, in numbers you set. Every decision is traced in LangSmith or Langfuse so you can audit the reasoning step by step, and those metrics feed the dashboards your IT already watches, Datadog or New Relic. Where a compliance framework applies, the same traces are what your auditor reads.
No, and it is built not to. The agent takes the repeat work, the reading and typing and chasing, and hands every judgment call to a person by name. What your team gains is the hours back, and those go to the customers and the decisions an agent is not for. You decide how that time gets spent before anything ships, so nobody meets the change as a surprise.
You change the system, because you own it. The workflow and the prompts are files in your repository, so swapping a CRM or rewriting an escalation rule is an edit, not a rebuild. You can make a small change yourself, hand a larger one to your own developer, or ask us and we quote it as its own job. Nothing switches off the day your stack moves, and nothing depends on a standing contract with PlainAI.