Use Cases on HyperCrux.com

A Law Firm's Clause Library That Stays in the Office: Similar Clauses, Precedents and Client Links

Wed, 07 Oct 2026 00:00:00 +0000

Consider a commercial law firm of twenty lawyers. Over the years it has worked on thousands of contracts, and somewhere among them is the best indemnity clause it ever negotiated for a food distributor. A lawyer drafting a new one wants to find it, along with a few others like it under English law, and to know which of the firm’s templates each one started from.

The obvious tool would be a search service with AI built in, and that’s where the firm stops. Client contracts are confidential, and the partners won’t send their text to a cloud service to be indexed, however good the terms look. Whatever they use has to keep everything, embeddings included, on the firm’s own machines.

A PhD Student's Literature Review in One File: Citation Links, Abstract Search and SQL Together

Wed, 07 Oct 2026 00:00:00 +0000

Take a second-year PhD student in public health with 1,400 papers in Zotero, all about loneliness in older adults. Her supervisor points her to a 2024 review they both trust and asks for a reading list built around it. The list should hold the papers near that review in the citation graph from 2018 on, with the ones closest to her research question first.

Each half of that request is easy on its own. Citation tools follow references, and search engines rank by meaning. What she doesn’t have is one place that does both over her own library, and lets her rephrase the question next week and run it again.

A Small Bike Shop's Recommendations in One File: Similar Products, Parts That Fit and Stock Levels

Wed, 07 Oct 2026 00:00:00 +0000

Picture a bike shop with one store and a web shop selling about 3,000 products, from helmets to brake pads. The owner wants the product pages to suggest things worth adding to the basket. The web platform came with a “customers also bought” box, and it keeps suggesting lights that are out of stock and mudguards that don’t fit the customer’s bike.

Good suggestions mix two kinds of knowledge that usually live apart. Whether two products are alike is a question about meaning, which vectors are good at. Whether a part fits a bike is a plain fact from the supplier’s spreadsheet, and so is the stock count, except that it changes every time the till rings.

A Support Bot That Never Quotes a Retired Article: Help Centre Search on HyperCrux

Wed, 07 Oct 2026 00:00:00 +0000

Picture a small software company with about 400 help articles and a chat bot on its website that answers questions from them. The bot works the way most of them do. It turns the customer’s question into a vector with an embedding model and finds the articles closest to it. A language model then writes the answer from those.

The weak spot is what happens when an article changes. Say the refund policy changes in March. Someone writes a new article and retires the old one in the help centre, but the vector database keeps its own copy of every article, and the job that should delete the old copy fails without telling anyone. Now the bot quotes a refund window that no longer exists, and nobody finds out until a customer forwards the chat to support. Plans have the same problem. The search only knows which articles are for the pro plan if someone copies that over too, and keeps copying it.

An AI Assistant's Memory That Forgets Properly: People, Facts and Embeddings in One Transaction

Wed, 07 Oct 2026 00:00:00 +0000

Say you’re building an assistant for account managers. It listens to what they tell it and keeps the useful parts, like Dana preferring calls after ten or Acme renewing in March. Before it answers a message, it pulls up the memories that matter and puts them in front of the language model.

Two kinds of lookup hide in that. One is about meaning: which memories are closest to what was just said. The other is about people: which memories are about Dana. Then comes the part most memory setups handle badly. When Dana asks to be forgotten, every memory about her has to go, vectors included, and a copy left behind in a search index means the job isn’t done.

At 3 a.m., Find the Incident That Looks Like This One: On-Call Notes With Links and Similarity

Wed, 07 Oct 2026 00:00:00 +0000

Think of a platform team of six that keeps an online shop’s checkout running. Every incident gets a short write-up afterwards, saying what broke and what fixed it. After a few years that’s a few hundred pages in the team wiki, full of hard-won knowledge that nobody can find at 3 a.m.

The question at 3 a.m. is “has this happened before?” Wiki search matches words, and the words in an alert rarely match the words in a write-up. The alert says connection refused. The write-up from March says checkout errors after a database failover. They describe the same problem from opposite ends. And on a truly bad night the wiki may be down too, if it runs on the same infrastructure that’s failing.

Finding the Shot You Half Remember: A Photographer's Archive Searched by Image Embeddings

Wed, 07 Oct 2026 00:00:00 +0000

Imagine a commercial photographer with about 60,000 keepers from twelve years of shoots. A client calls. They want the shot of the red chairs on the stage from one of their events, or something like it, and wider if she has one. She can picture it. She can’t place which event it was, and her keywords from back then are patchy.

Image embeddings are good at this kind of memory. A CLIP model such as ViT-B/32 turns each photo into 512 numbers, and photos that look alike end up close together. It can turn a sentence into numbers in the same space, so “red chairs on a stage” lands near photos of red chairs on a stage. The rest of what she knows, which client it was for and that she only wants her four- and five-star picks, are facts, and facts belong in fields and links.

Matching Candidates to Jobs and Deleting Them Cleanly: A Small Recruiting Agency on HyperCrux

Wed, 07 Oct 2026 00:00:00 +0000

Say a four-person agency places data engineers and analysts, and holds about 8,000 CVs. When a client sends a new job, the first move is a shortlist: the people whose experience is closest to the job description, among those who are available and haven’t already applied. Pitching someone for a job they applied to last week is a quick way to look careless in front of a client.

The agency also holds personal data, and that comes with duties. Under data protection law a candidate can ask to be deleted, and the agency’s own policy says CVs go two years after the last contact. Either way the CV has to go along with everything made from it, the vector included, and every link that ties the person to jobs and clients.