Discuss a workflow
AI for business

AI ANDAUTOMATIONFOR SMALLTEAMS.

I set up AI assistants and agents that work inside your team’s own tools, with your data kept inside your organisation. First we check where AI helps at all, and people keep control of every result.

Discuss your workflow See an assistant at work
The approach

The work
comes first.

Many processes need no AI agent. A better checklist, a simple integration or clearer ownership often solves the problem.

First we look at where information gets stuck, where time goes and which decisions need a person. Then we pick a realistic pilot, agree on what success looks like and decide whether AI is the right tool.

Hands-on setup

AI assistants that stay
inside your workspace.

Beyond advice, I set up working AI assistants and agents for teams and for individual managers. They read the documents your team already produces, sum them up and turn them into tasks inside the tools you use every day.

When your data must not leave the company network or your organisation’s own cloud workspace, the assistant runs there too. I connect it to your files, mail and task tracker, set what it may see and do, and tune it until it saves real time.

Example

Reports that analyse themselves

You want every weekly report read, summed up and turned into tasks, and the data must stay inside your organisation. This is how such an assistant could work.

  1. A report arrives

    A new sales, project or finance report lands in a shared folder or an inbox the assistant watches.

  2. The assistant reads it

    A language model on your own server or in your organisation’s cloud account pulls out key figures, changes since the last report and open risks.

  3. Summary and tasks

    You get a short summary, and suggested tasks appear in your task tracker with an owner and a due date.

  4. A person confirms

    Tasks go out only after someone on the team approves them, and every step is logged.

Your data stays with you

  • The model runs on your own hardware or inside your organisation’s cloud account.
  • The assistant sees only the folders and tools you allow, with the access rights your team already has.
  • Every report it reads and every summary and task it suggests is logged, so you can check its work.
  • Reports never go into public AI chat services.

The example shows a setup I can build and describes no finished client project. Whether a fully local model fits depends on your hardware, the volume of documents and the quality you need, and a short pilot answers that.

Discuss an assistant for your team
What I can help explore

What I can
help with.

Independent support for teams that are trying AI automation. What I can build depends on system access, available integrations, security requirements and the agreed scope.

Workflow analysis

Mapping a repetitive process, finding bottlenecks and deciding what is worth automating.

Process mapping / feasibility

Automation setup

Setting up a small pilot for document processing, request routing or recurring reports when the needed integrations exist.

Workflow configuration / integrations

Assistants & agents

Personal and team assistants, agent workflows and internal knowledge search, set up inside your own environment with clear tool permissions.

Private assistants / RAG / agents

Quality & oversight

Adding validation, independent checks, human approval and exception handling before a wider rollout.

Evaluation / monitoring / safeguards
Explore a workflow

Example
workflows.

These examples show how a team could organise the work. They are concepts. None of them describes a finished client project or a live integration.

INBOX & REQUEST TRIAGE
New request
AI classification
Human review

The system sorts each new request by topic and urgency, and an employee reviews the exceptions before anything goes out or changes.

CONCEPT ONLY. NO AGENT RUNS HERE.
How an engagement could work

How a project
could run.

Understanding the process

A focused conversation about the task, current tools, volume, risks and what a useful result would look like.

A small pilot

We define inputs, outputs and approval steps, then build a small working prototype with suitable tools.

Testing before a wider rollout

We test real examples, review the failures and decide what needs more work, monitoring or human review.

Let's talk through it

What slows
your team down?

You don't need a technical brief. Tell me what your team repeats every day, which tools you use and what you'd like to improve. First we check whether automation makes sense at all.

Please don't share passwords, customer records, proprietary files or other confidential information in a first message. I can't guarantee an implementation or any savings.

Back to FascinateAI Hub

Start with a short message.

I'll open the form here once I set up secure sending. Until then, describe your workflow on LinkedIn.

  1. What does your team repeat every day?
  2. Which tools do you use?
  3. What would a useful result look like?
Discuss on LinkedIn

Please don't share passwords, customer records or confidential files in a first message. LinkedIn's own terms apply there.