Featured field and capability
Linked Data
Model-Based Systems Engineering
Featured industry
Services
Featured case
Data Libraries
Technical documentation, like tenders, project plans, and bid documents, contains a lot of valuable knowledge. Turning that knowledge into reusable data takes more than a quick prompt to an AI chatbot.
AI-based tools can help, but they often bring inconsistency, missing data, or made-up answers. A structured, semantic approach can give your organization a way to extract knowledge from documents and make it reusable across systems.
Semmtech has grounded the core of its work on semantics since day one. In the following paragraphs, we will break down what this approach looks like and how you can get started right away.
If you are more of a listener, also check out our recent webinar on this topic here.
Nowadays, more and more project teams try AI tools on specifications or requirement documents and expect accurate results. In practice, several issues appear:
Inconsistency: Uploading the same document with the same question can lead to different outputs every time you run it.
Hallucinations: AI-based tools often hallucinate, extract, and interpret information that does not exist in the source or is incorrect altogether.
Weak traceability: Generic AI-based tools often cannot point to the exact place in the document where the information was found.
Disconnected output: Useful knowledge remains trapped in a chat window instead of moving into requirements management tools or other project management systems.
To counter these challenges, we developed an approach that gets rid of these inconsistencies and helps you reap the full benefits of AI with confidence!
A structured approach starts with building a domain model that represents the knowledge of the organization and the way the company works with its information. This model gives AI the context it needs to extract information consistently from technical documents.
A domain model helps define:
The model acts as an instruction set, helping AI classify and extract information consistently.
1. Define the domain model
Think of documents such as specification documents, requests for proposals, or standards. We sit together and review your processes and your documentation, and we work towards capturing your organization’s concepts, rules, and relationships in a way that is suitable for the model to interpret.
2. Use AI for extraction.
We customize our AI pipelines to work on your technical documents so they can identify relevant knowledge based on the model we defined in step 1.
3. Structure the data
Our AI pipelines will structure your data in a reusable format, so you are able to actually make use of it in your own tools.
4. Validate with experts
Human reviewers validate the extracted information
5. Store knowledge in a structured format
The validated output becomes reusable data for workflows, dashboards, knowledge graphs, and other downstream applications.
The process we described above pays itself off. For your team, it creates information that is:
At our core, at Semmtech, we focus on co-creation. Each new organization we work with brings different knowledge, different teams, and different priorities. That means we can adapt every solution to your organization’s needs and ambitions.
We aim to support pioneers who want to improve how they work, iterate on solutions, and build better ways to manage technical knowledge with AI. If you’d like to build something together, reach out here.
