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OSOM Labs · Sion, Switzerland
Understand what the web and AI are changing for your business.
Tools to try. Research to follow. Explore what we are building at OSOM Labs.
Explore the experiencesAn answer that brings together information from several pages.
AI proposes a summary. You check the sources.
A tool to use
One address. Observations about your page. Practical steps to make it easier to understand.
See how it worksChoose a step to read it in detail.
Your page. Enter the address of the page you want to examine. You then confirm the analysis on the audit screen.
An outside view. The audit reads the page’s code (headings, description, links, images) and, when available, its speed as measured by Google, to identify what makes it easier or harder for search engines to understand.
Practical next steps. Read the observations and suggested improvements without having to leave your email address.
An initial look at one specific page of your website, with suggestions to consider.
Enter an address to prepare an analysis of your page.
You will confirm the analysis on the next screen. It examines the supplied page, not the whole website. No email required to view the results.
A simulation to adjust
Expertise becomes useful when you can interact with it. Adjust the inputs in this calculation demonstration.
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Your scenario. Choose how long a task takes, how often it comes up over a period of your choice, and the share you think could be automated.
A simple calculation. The calculation multiplies the duration by the number of tasks and the share that could be automated. Minutes are then converted into hours.
A sense of scale. Move the sliders to see what changes. The result illustrates your assumptions; it does not measure savings already achieved.
A rough estimate that does not yet include setup and review time.
Move the sliders and see what changes.
Potentially automatable
2 hof time in your scenario
Minutes × tasks × share ÷ 60. An illustration based on your assumptions, before setup and review time.
A method to explore
Our measurement project: observe when a business appears in AI answers, and keep the sources.
See how it worksChoose a step to read it in detail.
A customer question. We start with business questions: finding a provider, comparing options or checking what a business does.
The answer and its sources. The protocol calls for keeping the original answer, its sources, the date and the interface used.
Facts to check. The protocol looks at whether the business is named, whether one of its pages is cited as a source, and whether what is said is accurate. Ambiguous cases need human review.
Here, you explore our research method; no results are measured live.
Choose a question to explore our method.
Explore the protocol · illustrative questions, no live results
« Who can help me make my website easier to understand? »
Does the business appear without being named in the question?
A defined set of business questions, repeated under a recorded protocol.
The original answer, its sources, the date and the interface used.
Mentions, citations and facts, with human review of ambiguous cases.
A principle to discover
An organised memory to retrieve a decision when it matters. An open-source integration in our workshop.
See how it worksChoose a step to read it in detail.
Decisions. A visual identity, a contact page, a Labs experience: every project carries decisions that need to be retrieved.
An organised memory. The principle is to organise this context so the relevant decision can be found when work resumes.
The right context. Choose a topic in the demonstration to reveal the corresponding decision. The examples are written into this page.
Examples written into the page illustrate the principle.
Open a folder to retrieve an example decision.
Use the yellow dot as the visual signature.
Illustration of context retrieval with local examples. This demonstration does not query the MemPalace engine.
A first-hand account to read
A system can look convincing and predict nothing. This project shows how we put its results to the test.
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An appealing idea. This internal project starts with an idea: build a score to help interpret stock-market signals.
The test of evidence. The score is compared with observations. A convincing presentation is not enough: what the system actually predicts needs to be checked.
An acknowledged limit. The story shows why a verdict that contradicts the original idea must be accepted: the score predicts no better than a coin toss. It shares a method for checking a system, and its limitations. Since then, the system no longer looks for the right moment to buy: it applies a written plan and refuses to advise on incomplete data.
An internal educational case, showing how to put a system to the test.
Follow the reasoning, then read the story of the experiment.
Build a score that helps interpret stock-market signals.
Compare its predictions with the observations. Accept a negative verdict.
Internal educational project. The full case explains the method and its limitations.
Read the full storyA precise need can become a clear, measurable and genuinely usable tool.