Why write this up
Most writing about building software products is either a demo or a sales pitch. This is an account of the development process behind VetCaseIQ, a case-review platform built from concept through to deployment, and of the decisions that mattered.
Scope note before starting: this covers how the software was built, not what it concludes about any case. Nothing here should be read as clinical guidance, and no accuracy or outcome claims are made or implied.
The problem shape
The starting point was not a feature idea. It was a body of information that was written rather than tabulated — case documentation, notes, published literature — where the useful content sat in prose.
Conventional software is good at structured data. Give it rows and columns and it will filter, sort, and calculate reliably. Give it fifteen pages of narrative and it can store them and search for words, but it cannot work with what they say.
Closing that gap is the actual job. The input is unstructured, and the work requires turning it into something a system and a person can both check.
Structure first, summary second
The most consequential early decision was to treat the system as a document-processing pipeline rather than a single text box that returns an answer.
A text box over a pile of documents is easy to build and hard to trust. Whatever the system happened to pick out of the pile is invisible, and the quality of every answer depends on that invisible step.
The alternative is to do real work before anything is summarized: parse documents, identify their sections, extract the elements that matter, and convert prose into a structured representation the rest of the system can work with deliberately.
This is unglamorous engineering — parsing, normalizing, handling documents that do not follow the expected shape. It is also where reliability comes from. Every improvement here reduced how much later stages had to guess, and guessing is where errors enter.
A multi-stage pipeline
The second decision was to break the work into stages rather than producing a complete answer in one step.
Reading everything and producing a final output in a single pass gives something fluent, hard to check, and impossible to debug. When it is wrong, there is no way to see where it went wrong.
Splitting the work into discrete stages — each with a defined input, a narrow job, and an inspectable output — changes that. Each stage can be examined, tested, and improved on its own. When something is wrong, the stage responsible is identifiable rather than hypothetical.
It costs more to run and considerably more to design. It is the difference between a demo and something anyone should rely on.
Literature retrieval
Part of the system involves retrieving relevant published literature.
The engineering lesson was that retrieval quality dominates everything downstream. A stage given the wrong source material will produce a well-formed result built on the wrong foundation, and it will not signal that anything went wrong.
Consequently, more effort went into what gets retrieved and how it is selected than into the language that describes it — and the system is built to show its sources rather than to present conclusions as self-evident. A user needs to see what something was based on to judge it.
Where the humans are
The design assumption throughout: this is a tool for a professional, not a replacement for one.
Practically, that meant the output is reviewable rather than final, sources are visible, uncertainty is surfaced instead of smoothed over, and the interface is built for someone checking work rather than accepting it.
In a domain where a confident wrong answer carries real consequences, human review is a design requirement rather than a disclaimer.
The technical shape
The application is built with Next.js, React, and TypeScript, with API integrations handling the processing stages and literature retrieval, and runs in a cloud deployment.
TypeScript deserves a specific mention. When a system passes structured data between many stages, having the shape of that data checked before the code runs prevents a whole category of failure that is otherwise found at runtime, in production, on real input.
What transferred to other work
Four things generalize beyond this project:
Structure before summary. Effort spent making inputs structured reduces what later stages must guess, and guessing is where errors live.
Stages beat single passes. Decomposed work is debuggable work.
Retrieval quality sets the ceiling. No later stage recovers from being handed the wrong material.
Design for review. If a wrong answer matters, the interface has to make checking easy rather than making acceptance easy.
None of this is specific to one field. Any business with information in prose rather than in columns faces the same problems.
The VetCaseIQ case study covers the project itself; our custom application work is where similar problems get scoped.