Tender Filtering
Separating the few tenders worth reading in full from the many that are not.
- Classification
- Relevance ranking
- Decision support
Once tenders were being collected, the problem inverted. Instead of too little visibility there was too much: a long list of opportunities, most of them irrelevant, each needing to be opened and read before it could be dismissed.
The review starts with a short, ordered list rather than an undifferentiated one. Borderline items are visible as borderline instead of being silently dropped, which means the filter can be tuned over time rather than quietly losing work.
The problem
Information overload has the same practical effect as having no information. When reviewing the list becomes a job in itself, it gets postponed, and the genuinely relevant opportunity sits in a queue behind fifty that were never going to be pursued. Crude keyword filters are not a solution — they discard relevant work whose wording does not match, and keep irrelevant work that happens to use the right word.
What we built
We built a filtering layer on top of the monitoring, which classifies each tender against the criteria that actually determine whether the business would bid — the nature of the work, the eligibility conditions, the scale, the location. Each item carries the reasoning behind its classification, so the filter can be checked and corrected rather than trusted blindly.
Where AI enters
AI performs the relevance judgement, which is the part a keyword rule cannot do. It reads what the tender is actually asking for and compares it against the capability and eligibility profile of the business, including cases where the same requirement is described in unfamiliar terms. The filter ranks and explains; a person still decides.
Business value
Attention goes to the opportunities that could realistically be won. The review becomes short enough to actually happen on schedule, which is what determines whether a monitoring system produces bids or just produces a list.
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