That is not a flaw — it is the correct way to write it. Python ETL, notebooks, ML and reasoning layers, knowledge graphs, FastAPI services, Docker: the science is real and validated. What a paper never required is what a pilot demands on day one. The gaps concentrate in four places:
A research pipeline runs on the dataset it was written against — curated, cleaned, one-shaped. Production input is none of those things: malformed files, missing fields, encodings nobody chose. Research code meets its first malformed file and stops; production code logs it, skips it, and keeps going. That difference is error handling, and papers never require it.
A FastAPI layer that served you and your co-author will meet concurrent users, retries, and slow clients. Endpoints with no authentication were fine on localhost and are not fine on a contractor’s network. Queries that scanned everything were fine on the evaluation set and are not fine on a real project’s data. None of this shows up until the pilot.
Every research prototype has an install ritual only its author knows — the environment variables, the load order, the one script that must run first. A pilot partner cannot run rituals. Deployment means the system starts from a clean machine with documented configuration, secrets kept out of the repository, and a way to know it is up.
A paper is checked by peer review; a codebase is checked by tests — and research code almost never has them, because the deadline was the conference, not the customer. Before an industry partner, an investor, or an acquirer looks at the code, the difference between "it worked when we ran it" and "it is tested" is the difference between a demo and an asset.
Our standard $995 code audit with a standing 20% discount for university researchers, spinouts, and grant-funded projects. A senior engineer reads your full codebase — pipeline, models, API, deployment — and delivers a written report in five business days: what's solid, what's fragile, the security gaps, and a prioritized roadmap from prototype to deployable. The report is yours either way; hand it to us or to any engineer your university trusts.
University money comes with process: competing quotes, grant terms, approval rounds that pause for the summer. We treat that as a requirement, not an annoyance.
Often, yes. Several commercialization programs explicitly allow grant funds to be spent on external services — Germany’s EXIST Transfer of Research (up to €250,000 in material costs including external services), Enterprise Ireland’s Commercialisation Fund, Innovate UK’s ICURe follow-on funding, EIC Transition, and Sweden’s Vinnova verification grants. Check your grant’s terms or ask your grants office; we provide a formal fixed-price quote your administrator can file. One exception: US SBIR/STTR awards require the work to be performed in the United States, so those budgets cannot come to us.
It is a normal part of how universities buy, and we make it easy: a formal one-page quote within 48 hours — scope, deliverables, timeline, and a fixed price your procurement office can compare directly. Quotes stay valid for 90 days, so a summer break or a slow approval round does not cost you the price. Invoicing in EUR or GBP is available on request.
Working research code that needs to survive real users: Python ETL pipelines, Jupyter notebooks that became the product, ML models and reasoning layers, knowledge graphs and triplestores, FastAPI or Flask services, dashboards and viewers, Docker setups. Much of it is now AI-assisted — written with Copilot, Cursor, or ChatGPT by a domain expert who is not a software engineer. That is not a criticism; it is exactly the gap the audit is built for.
Usually not. Research code is written to produce results for a paper, so the gaps concentrate in predictable places — error handling, security, tests, structure, deployment — while the core science is real and validated. An audit tells you, in writing, what to fix and in what order, and whether any part genuinely needs rebuilding. Most prototypes are salvageable, and salvaging is far cheaper than rewriting validated work.
A fixed $796 — our standard $995 audit with a standing 20% academic discount for university researchers, spinouts, and grant-funded projects. A senior engineer reads the full codebase and delivers a written report in 5 business days: what is solid, what is fragile, security gaps, and a prioritized roadmap to deployable. If you continue to a Production-Hardening Sprint afterwards, the audit fee is credited against that sprint in full.