Done-for-you implementation

Internal AI agents that reduce busywork without creating new chaos

I design and build internal AI systems around real workflows. The goal is not to mimic autonomy for its own sake. The goal is to help your team move faster with better preparation, stronger first drafts, and clear review where trust matters.

See workflow examples →

Starting at €8,000 depending on workflow complexity, integrations, and review UX.

Example internal agent systems

Support prep agent

Prepares a reply draft, likely next action, and relevant context before a human sends anything customer-facing.

Inputs: Ticket, account data, docs, previous conversation history

Output: Structured summary, reply draft, escalation cue

Review: Support rep approves or edits

Internal research agent

Pulls information from docs, notes, transcripts, and links to create a cited internal brief for a team member.

Inputs: Question, docs, wiki, notes, transcripts

Output: Cited brief, open questions, suggested next step

Review: Operator checks synthesis before acting on it

Account briefing agent

Assembles the current state of an account before calls, renewals, or escalations so the team is not rebuilding context from scratch.

Inputs: CRM, support history, usage data, billing flags

Output: Meeting brief, risks, recommended actions

Review: CSM or account owner reviews

Content workflow agent

Turns a brief into a structured first draft, missing-info checklist, and production notes instead of asking humans to start from a blank page.

Inputs: Brief, notes, source links, brand constraints

Output: Draft, checklist, open questions

Review: Editor approves and tightens

What makes an internal AI workflow a good fit?

The workflow already exists and somebody owns it.

A human can quickly tell whether the output is useful, weak, or unsafe.

The work repeats often enough that a stronger first pass saves time every week.

The system can improve preparation, drafting, classification, or recommendation without taking final control too early.

Assist before autonomy

Summarize and structure

Retrieve and assemble context

Draft or recommend

Escalate low-confidence cases

Only trigger actions when the boundary is explicit and approval is designed in

What I actually build

Workflow trigger and owner
Context sources and retrieval plan
Prompting, tools, and business rules
Output schema and review UX
Logging, evals, and fallback behavior

What I optimize for

Useful outputs over flashy demos.

Human review where trust matters.

Integration with your current tools and systems.

Clear failure handling and iteration based on observed usage.

Have a workflow in mind?

Tell me the workflow, the inputs, what a good output looks like, and who should review it. I'll help you assess whether it's a strong fit for an internal AI system.