Work with AI without explaining and writing things down over and over
AIGrip is a framework for working with AI: a way of working in which decisions, current status, and handover materials are recorded as you go, not as an extra task. You do not have to prepare them separately. The brief and the decisions stay with you.
Leaders in automotive, banking, and telecommunications rely on our solutions
You do not have to write decisions down separately
Just tell the AI, and it captures them as you work.
You decide
The AI points out weaknesses in a proposal; the final decision stays with you.
Records and the procedures you create stay available to you
In a readable, open format.
What takes up unnecessary time when you work with AI
AI tools help with individual tasks. But when work runs over several days, more people get involved, and materials pile up, the time spent recalling context, tracking down decisions, and preparing handovers grows too.
These activities take time you could otherwise spend on the task itself.
What AIGrip is and what it is for
AIGrip is a framework for working with AI that we built from practical experience. It helps keep track of how tasks, decisions, and outputs connect, even in long-running work. Within it, the AI helps you think through the solution, keeps track of context, and reminds you of earlier decisions; lessons from completed work are used to build further procedures and tools. The AI draws on information created during the work and that it has access to. You continue to decide on goals and decisions.
Today we apply AIGrip in Claude Code with various models, depending on what each is best suited for. The way of working itself is not tied to any tool or model; it is designed so that the environment and the models can be replaced. You can adopt and develop the AIGrip way of working on your own. We help with introducing it, the initial setup, and providing the environment it needs. More than a hundred people work with AI in this framework today.
AIGrip is our solution in the Framework for work area. You do not need to introduce it if the tools and practices you already use cover the same needs. It does not replace office or process systems; it adds to them. AI adoption → It can optionally be combined with the AIGrip Workspace work environment; that is a choice, not a condition. Tools for work →
How it works
AIGrip helps keep track of context throughout a task, even when it runs over several days, materials change, or several people work on it. What the way of working provides and what a specific work environment provides differ; the environment belongs to tools for work. Tools for work →
We start with tasks you are already working on
Existing materials and decisions are read as input; nothing is deleted or overwritten.
The AI helps you think and decide
It develops your idea, says when it disagrees, and points out when you are drifting from the goal. Before a decision, it points out risks or alternatives; once a decision is confirmed, it builds on it in further work. Anything you have not confirmed stays a proposal, always with the original wording from the work it is based on.
Decisions are recorded as you go
You only need to tell the AI a decision; it repeats it and records it. A new decision replaces the old one without deleting it. For every record, it is defined where it applies and who may see it.
The current status of the work is always available
When you come back, you see what happened last, what is open, and what comes next, including what the AI is doing right now. What happened outside the AI, in a meeting or a call, you add in one sentence.
Errors found are used to improve procedures and checks
A discovered error becomes a procedure or a check that speaks up when the same thing is about to happen again.
You create the templates and procedures you need while you work
A matrix, template, checklist, or script is created during the work and stays with it; a colleague who takes over the work finds it too. Whatever stops being used is retired.
Decisions with wider impact require your confirmation
A small step that can be undone, the AI carries out and shows you; if you do not object, it stands. It asks for confirmation before a step that cannot easily be undone, affects people outside the task, is meant to apply to everyone, or concerns a specific person. This is a rule of the way of working; it is enforced technically where the work environment allows it. The same applies to agents, that is, AI that does a piece of work even when you are not there.
What AIGrip changes in everyday work
AIGrip helps with everyday work as well as longer tasks that involve several people.
You think and decide with support
When someone asks a month later "why this?", the reasons and the options considered are at hand.
Decisions and materials stay available without keeping separate notes
Materials, decisions, and outputs are with the work, not scattered across emails and drives. You pick up where you left off, after a day or after months.
You keep the overview even with a large volume of materials
Whatever came out of them stays with the work as a record, and at the end you can see what the conclusion was based on. When something changes, you can see what it affects; a person has to review and confirm it.
You have an overview and stay in control
You see the status at a glance. The system does not slow you down with trivia; you can undo a change, and nothing irreversible happens without you.
You can reuse the results of your work
The result is not text in a chat: the work leaves outputs that can be used, sent, and built on. What you have built once does not need to be built again.
When several of you work on it
A colleague opens the work and knows where it stands, what applies, and why. When handing over a task or standing in for a colleague, the necessary information and earlier decisions are available right with the work. Several people and agents can work on the same work at once; nobody overwrites anyone's records, and the status shows who is working on what. The company keeps information about decisions, options considered, and work procedures even when people on the team change, and the overview of work is based on the continuously recorded status, not on reports. What is measured is the system, not people: AIGrip does not produce individual performance metrics. It does not switch off the metrics of your environment; those stay in your tools, and you decide about them.
What working with AIGrip requires
Similar approaches often fail when they ask people for extra administration: notes, forms, reports. People stop doing it, and the system dies.
One sentence when you make a decision
The AI repeats it, and that records it.
Confirmation of decisions with wider impact
For small reversible steps, it is enough not to object to the AI's proposal; the AI asks for an explicit yes only when something has a wide impact.
Nothing more
No ongoing note-taking and no separate forms are needed.
Additional administrative tasks put people off a new way of working. That is why records are a by-product of the work, a lesson is put into practice instead of being written down, and a tool is created instead of being ordered.
How AIGrip differs from everyday work with AI
AI assistants today can remember and take notes. These are building blocks, not a way of working: they do not tell you what should remain from the work, what of it applies, and what it is based on. We only list differences we can describe specifically, including what is not an advantage of ours.
One shared record of decisions, insights, and created procedures
Intent, decisions, insights, and procedures arise from the work as proposals with the original wording they are based on; each one defines where it applies, who may see it, and what replaced it. Other tools have individual elements; a shared record of all four types as a ready-made element is something we did not find when comparing available tools.
Confirming important decisions is part of the way of working
Elsewhere, an administrator sets it as a policy that the system enforces; with us, it is the default behavior during work. At the same time, this confirmation is not enforced by the system; a technical lock has to come from the environment.
Lessons reach the company with the consent of those concerned and without names
Tool vendors also offer turning lessons into changed behavior. The difference lies in the conditions under which an insight is passed on to other parts of the company: only after it has been confirmed in several pieces of work, with the consent of those it concerns, and without names. We do not yet measure whether recorded insights actually improved procedures.
What comes out of the work is yours and portable
Portability alone is not an advantage; the difference is in how much stays in an open format: the record with its original wording, where and until when it applies, and what replaced it, not just the tool. Content and links are transferred, not automation; a new environment has to provide that itself.
Built and refined from practical experience
It grew out of our own work and customer projects; what did not hold up in practice, we dropped. We do not yet have a public overview of the practices we dropped after testing them. The first evidence is a trial on your task: we record which steps changed or were no longer needed.
What AIGrip does not cover
- Replacing human judgment. The AI is a partner and a challenger, not a decision-maker; responsibility stays with people.
- Monitoring employees' work discipline. Extra administration puts people off; that is a brief for designing the way of working, not for pressuring people.
- Processing all materials of a large agenda at once. How much can be covered at once is assessed for the specific agenda.
- Taking in what arrives from outside. Emails, exports, and attachments are not filed automatically.
- Assessing people's performance. The framework does not measure people.
- Operating the environment. Access, identities, models, and costs are not part of the framework; your company or AIGrip Workspace provides them. Tools for work →
Frequently asked questions
Isn't this just a well-written set of instructions and a few automatic checks?
Those are building blocks, and AI assistants have them today. What they lack is a way of working that connects them into one approach: what should remain from the work, what of it applies, and what it is based on. And above all: similar attempts fail because they ask people for extra work. Here, records are a by-product of the work.
Are the safeguards technically enforced, or do they rely only on an instruction the model might ignore?
These are not the same thing, and we do not merge them. The way of working builds in that every record defines where it applies and who may see it, and that a decision with wider impact waits for your yes. That confirmation is not enforced by the system: it is default behavior, not a lock. The lock has to come from the environment. A rule that is not checked is described as such and not presented as enforced.
Can we start with one team and stop without further commitment?
Yes. It starts with a real task, not an implementation project; one team and one real piece of work are enough. You will see the most on work that takes longer than a single prompt, involves several people, and carries decisions. What to monitor is the system, not people: whether the work is being captured, whether the safeguards run, and whether lessons are applied. When you stop, the records go with you.
How do we show management it was worth it if you do not measure people?
What is measured is whether the system works: whether the work is captured, whether the safeguards run, and whether lessons are applied. You do not show management a report but the real state: decisions with their reasons, handovers without briefings, lessons turned into procedures, and tools that colleagues use too. A quantified difference in specific people's work is what AI Buddy delivers. How people work →
How to get started
Not with an implementation project, but with work that is happening anyway. You pick one real task, ideally one where you repeatedly lose track of context or have to dig up earlier decisions. What you already have stays; you say what you want to achieve, not how to do it technically. We first test the way of working on this task and record which steps changed or were no longer needed. Together we look at what can be done differently, what will remain from it, and what needs to be added on your side. No commitment to continue.
Pick one piece of work that slows you down
One specific task is enough for the first meeting. No commitment.