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AI internal tools
AI internal tools that take repetitive work off your team
We build AI tools that read documents, answer questions from your own data and prepare reports, connected to the systems you already use. It starts with a working prototype on your real data, so you see what AI can do before you invest more.
- Prototype first
- tested on your real data
- Your systems
- connected to the tools you use today
- People decide
- AI prepares, your team approves
What can an AI internal tool do?
Take over repetitive work with text, documents and data. These are the tasks where it usually pays off first.
Read documents
Pull data from invoices, orders, spec sheets and contracts into your systems.
Answer questions
Search across manuals, policies and past projects, and answer with sources.
Prepare reports
Turn data from several systems into a weekly report, ready to review.
Sort requests
Read incoming emails and requests, route them and draft replies.
Fill in data
Move information between systems without manual typing.
Draft documents
Prepare quotes, summaries and first drafts from your templates.
What do you get?
A tool your team uses every day, not a demo that stays in a drawer.
- 01
Process review
We map the task with the people who do it today and pick the steps AI can take over.
- 02
Working prototype
A first version running on your real documents and data, so the results speak for themselves.
- 03
Model choice
OpenAI, Claude or Gemini, picked for the task, the accuracy needed and the cost.
- 04
Integration
Connected to your email, documents, CRM, ERP or database, so nobody copies data by hand.
- 05
Human review
Your team sees, corrects and approves what the AI prepares before it is used.
- 06
Data protection
Business API accounts, which by default do not use your data to train models, and access only to the data the tool needs.
- 07
Rollout and training
The tool introduced to your team, with a short guide and support.
How does an AI tool project work?
Small first: a prototype on one task, then a full tool once it proves itself.
- 1
Pick a task
We choose one repetitive task with clear rules and enough examples.
You: a short call
- 2
Prototype
A working version on your real data, measured against how your team does it today.
You: share sample documents
- 3
Build
The tool connected to your systems, with review screens for your team.
You: test it on real work
- 4
Rollout
Your team starts using it; we watch the results and fix what comes up.
You: give feedback
- 5
Improve
More tasks added once the first one pays off.
You: pick the next task
How much does an AI internal tool cost?
The prototype is a fixed price, so you can test the idea at a known cost. If it works, the full tool is priced as a fixed-price project or by the hour, plus the AI provider's usage fees, which we estimate upfront.
What the price depends on
- How many systems the tool connects to
- The type and volume of documents or data
- How accurate the results need to be, and how they are reviewed
- Where the tool runs and who can use it
Which AI models do you use?
The one that fits the task. We test more than one on your data and choose on results and cost.
OpenAI
GPT models for text, documents and data extraction.
Claude
Anthropic's models, strong with long documents and careful writing.
Gemini
Google's models, a natural fit when your work lives in Google Workspace.
AI tool questions
We use business API accounts from OpenAI, Anthropic or Google, which by default do not use your data to train their models. The tool only gets access to the data it needs, and it can run in your own cloud account.
No. The tools take over repetitive steps, so your people spend their time on work that needs judgement. Your team reviews and approves what the AI prepares.
It depends on the task, which is why we start with a prototype and measure it against your team's results before anything goes live.
Usually, yes. If your system has an API or a database we can reach, the tool can read from it and write to it.
OpenAI, Claude or Gemini, depending on the task. We test on your data, choose on accuracy and cost, and can switch models later.
You do. The code and the accounts are in your name, including the AI provider account, so you see and control the usage costs.
