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Our work / Procurement and Bidding Intelligence
Turning scattered procurement records into a market you can see
Most of what a company needs before it decides whether to bid already exists somewhere. It sits in different sources that use different formats, different identifiers and different update schedules. We build the pipeline that puts them together and answers the question behind every bid: where is the business, and who else is going for it.
The problem with the data
A company that wins its work by bidding lives on the list of what is open right now: which part, how many, by when. Reading that list tells you what is available today and stops there.
The questions that decide whether a bid is worth preparing sit elsewhere. Has this part been bought before, and how often? Who won it, at what unit price, and how long ago? Is the buyer a regular or a one-off? Are we qualified to supply it, and who else is? Those answers live in other records, each with its own format and its own idea of how to spell a company name.
People do reconcile this by hand. It takes a morning per part, which means it happens for the handful of opportunities somebody already had a hunch about, and the rest go unexamined.
What we built
An extraction pipeline that takes the data from different sources on a schedule, resolves those sources against each other, and loads the result into a single dataset that answers those questions in one query.
Records always carry an identifier, usually based on what is being built. Being able to see the past and similar contracts is key for determining future prices and delivery times, and that identifier is what makes the comparison possible.
The hard part is the ingestion
The hard part was never any single field. It was reconciling sources that disagree with each other, mining them at volume, and keeping the quality of what comes in consistent. That took months of review, trial and error before the system could be trusted to do it on its own.
If a system receives poor quality data, the insights it produces are equally poor. So we built it to go slowly and make sure everything it takes in is accurate, rather than to move fast and be wrong at scale.
Designed to run unattended
Long collection runs fail partway through, and a pipeline that starts over every time it hits a timeout will never finish. The extraction saves progress as it goes, so a run resumes where it stopped, and it paces its requests deliberately rather than leaning on infrastructure it does not own.
Raw pulls are written once and kept. Everything downstream reads from those stored files rather than re-fetching, which means an analysis can be re-run months later and produce the same numbers.
What it is used for
Three things, in practice.
Discovery: finding what is worth bidding. Open requests arrive already joined to their own history, so a buyer sees at a glance whether a part has been bought thirty times or once, and whether the prices have been climbing or falling.
Understanding the competition. History by part shows who has won it, how often, and who else is qualified to supply it. That turns a guess about the competitive field into a count.
Feeding the pricing model. A dataset like this is the training data for the price prediction model, which recommends a unit price for an open opportunity from the history of that exact part.
The value is not that the data was secret. It is that nobody had the hours to read all of it, in the several places it lives, before the bid was due.