validait combines several years of experience in service sales with the results of a master's thesis that was awarded the top grade. It shows that tenders can be assessed quickly, transparently and with evidence.
In the day-to-day business of a service provider, we experienced how much time finding, assessing and preparing tenders takes up and how strong the wish is for a clear basis for the decision. At the same time, a master's thesis was written that addresses exactly this point.
In conversations with bid managers from the DACH region, from large corporations to regional trade businesses, we heard the same wish again and again: decisions on a bid should be made faster and be easy to justify within the team. That became validait.
The thesis examines how the go/no-go decision on public tenders can be automated without losing transparency. Its title: ‘Ontology-Driven GraphRAG für die automatisierte Go/No-Go-Entscheidung von Bauausschreibungen, ein Vergleich lokaler und cloudbasierter Sprachmodelle’ (in English: Ontology-driven GraphRAG for automated go/no-go decisions on construction tenders: a comparison of local and cloud-based language models).
It is based on real contract notices from Germany, Austria and Switzerland and a survey of bid management professionals. The thesis was awarded the top grade (1.0, ‘excellent’). Its results still shape how validait reads documents, matches criteria and explains its recommendations.
A knowledge model of public procurement, known as an ontology, describes all types of requirements: suitability, grounds for exclusion, evidence, deadlines and award criteria. This allows the language models to identify the criteria in contract notices and tender documents in a targeted way and classify them consistently.
Each identified criterion is matched against the company's profile and classified as met, to be clarified or not met, each with a source reference in the document. Critical exclusion criteria are given particular weight.
The individual results add up to a clear go/no-go recommendation: bid, review more closely or decline to bid. The reasoning remains traceable criterion by criterion, so the team can stand behind the decision together.
14 language models were compared, from models running on in-house hardware to large cloud models. The strongest cloud model identified every critical exclusion criterion. The best local model came only slightly behind. This makes running without the cloud a realistic option for particularly confidential documents.
A knowledge graph checks in advance which tenders fundamentally match the profile. This saves around 60 percent of model calls. In the trial, the complete review of a contract notice cost around ten cents.
Binding the models to the knowledge model reduced the share of freely worded statements without a basis by a good third. That is why the product follows one rule: every assessment needs a source reference. Whatever the documents leave open appears as a point to clarify.
Source: master's thesis by Sebastian Löffler, 2026. Evaluation of nine active contract notices from the DACH region against an independently prepared reference assessment. The strongest cloud model was the only one to reach a critical recall of 1.0, meaning it identified every exclusion criterion.
A young company with experience in consulting, services and software development and a clear focus on sales.
Wrote the master's thesis on AI-assisted tender review from which validait emerged. Works in technical sales in intralogistics, previously spent several years in staffing and project services with direct involvement in tenders. Responsible for product direction, customer conversations and the content of this website.
Responsible for the architecture, development and operation of the platform. Makes sure analyses run reliably, data stays securely in Frankfurt and new features reach customers quickly.
For every criterion, validait shows the requirement, the source reference and the quote. This lets you trace every assessment yourself and present it convincingly in the bid meeting.
If a piece of information is not in the documents, validait flags it as a point to clarify. This keeps suitable tenders in play.
validait prepares, weighs and provides evidence. Whether to bid is decided by people who know their business. That is how the product is built.
Tender documents are stored encrypted in Frankfurt am Main, strictly separated by company, and are never used to train AI models. This is set out in the data processing agreement.
We review every registration personally. That way we know who is working with confidential tender documents, and we can talk with you while every conversation still moves the product directly forward.
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