General-purpose AI gets you started. It won't defend your results.
Chat assistants and closed platforms are both useful. Neither was built to answer the question a reviewer will actually ask you: where did this number come from, and how would I check?
No record of what happened
Upload a CSV to a chat assistant and ask for a differential expression analysis and you get a script. It may or may not run. There is no record of which package versions were used, no account of why one method was chosen over another, and no trace connecting the figure to the data. If a reviewer asks how you reached a finding, you are reconstructing the answer from a chat log.
No stable ground to stand on
Every conversation starts from scratch. The same prompt with the same data can produce different code, different statistical choices, different conclusions. Nothing pins the analysis to the environment that ran it, so six months later you cannot say with confidence what actually produced the number in your slide.
One method, presented as the answer
General-purpose models run one method and return one answer. They pick a single normalization strategy, a single test, a single set of thresholds, and present the output as if no other reasonable choice existed. They do not cross-validate against alternative approaches, test sensitivity to parameters, or flag when a finding rests on a single assumption. The analysis looks complete. It is shallow.
These tools are fast and flexible. That's exactly the problem. Speed without structure produces findings that don't hold up under scrutiny.
Every result carries its history
Each finding is linked to the dataset, the method, the parameters, and the model that reasoned about it, recorded at the moment it happened, not reconstructed afterwards, and signed so that a later edit is detectable.
Analyses run in a sandbox, not on your prompt
Code executes inside a container with the scientific stack already installed: R, Python, and the bioconda packages a real analysis needs. You are not pasting scripts into a terminal and hoping the dependencies resolve.
Multiple methods, compared
Instead of one pipeline, Inflexa runs several analytical approaches and compares them. You see where methods agree, where they diverge, and which findings survive the disagreement.
Grounded in the literature, with PMIDs
Inflexa runs targeted PubMed queries for each finding and assembles evidence chains traced to PMID. Disease associations, druggability, pathway context: cited, not asserted.
The three rows that matter are the ones nobody else can fill.
A chat assistant has nowhere to write a provenance record. A closed platform can write one, but you cannot audit the code that wrote it. Only an open tool can hand you a record and also hand you the means to check it.
| General-purpose AI | Closed platforms | Inflexa | |
|---|---|---|---|
| Runs analysis code for you | It writes a script. You run it. | Yes, on their infrastructure | Yes, in a sandbox on your machine |
| Knows your biology | From training data, unverifiably | Usually yes | Queries PubMed and 30+ biological databases live |
| Records how a file was produced | No, there is nothing to record it into | Sometimes, in a format only they can read | Yes, method, parameters, inputs, content hash, and model, aligned with W3C PROV |
| You can verify that record without the vendor | Not applicable | No | Yes, signed, and the verifier is open source |
| Records which model made each decision | No | Almost never | Yes, by resolved model name |
| Your data leaves your machine | Yes, into the chat | Yes, into their cloud | No |
| You can read the source | No | No | Yes, Apache 2.0 |
| Still works if the company disappears | No | No | Yes |
You don't have to take our word for any of this.
Install it, read the source, and check the claims on this page yourself. That is the entire argument.