Search that answers the question your users actually have.
Most search boxes make people translate their problem into the system's vocabulary. We build the opposite: engines that meet users where they are, measure how often they return the right answer, and improve until they do.
The questions your users actually ask.
The search box exists, but nobody trusts it
Your team types a query, gets nothing useful, and falls back to asking the one person who knows where things live. The search feature is technically there. In practice it is decoration.
Users have to know the answer to find the answer
Your system only responds to exact codes, official names, or internal jargon. Customers describe things in their own words, get the wrong result, and phone you instead.
Matching happens by hand, at scale
Somebody spends their day pairing one list against another — products to categories, requests to options, people to slots. It works until volume grows or that person is on leave.
Nobody can say how accurate it is
Ask "how often does our search return the right thing?" and the honest answer is a shrug. Without a measure, every improvement is a guess and every complaint is an anecdote.
Two engines, two very different questions.
National-scale classification search, made trustworthy
On a UK government tax & customs platform, search was returning the correct result 15% of the time. We built an in-house LLM evaluation framework to measure retrieval quality properly, then used it to drive the engine to 96.5% correct retrieval.
Outcome: the same search now handles roughly 280,000 classifications a month — and its accuracy is a measured number, not a hope.
Inverting the question in travel search
Standard travel search assumes you already know your dates and destination. We built a permutation engine that starts from "where could I go?" instead — flexible on time, place, or both — and searches the space of possible trips rather than one fixed route.
Status: live in private beta, running on its own infrastructure.
First we measure. Then we build. Then you decide.
The first slice is a measurement. We take one real search or matching problem, benchmark how often the current approach returns the right answer, then ship a working engine slice against that baseline — paid and scoped. You compare the two on real queries, not a demo set, and decide the next step with numbers in hand. And if you'd rather co-own than commission — you bring the domain, we bring the build — see how we partner.
If your users keep asking a question your system can't hear, we build engines that listen.
Tell us what your users type, and what they should have found. We reply within two business days — hello@understandata.com.
Send us a query it got wrong