White paper
The anchor document in the B2B set. It argues that above a certain volume, language work stops being a sequence of editing jobs and becomes an operations problem, and it sets out what a controlled production layer has to do about it.
White paper · PDF, v3 · 12 sections
At the scale of a single manuscript, editing is a craft problem. Personal expertise and an email thread are enough.
At the scale of a journal issue, a departmental programme, a documentation set or a translation portfolio, it stops being a craft problem and becomes an operations one. Hundreds of files. Multiple contributors. Instructions that change mid-job. Terminology that has to hold across a set and across time. Tracked changes, references, deadlines, approvals, privacy obligations, and questions that arrive months after delivery. The cost is not only the editing. It is the coordination required to make every output dependable.
Where the time actually goes. The white paper is specific about this, because vague problem statements produce vague solutions. Institutional time is lost reconstructing requirements out of email threads and personal notes; repeating checks because the previous stage left no usable record; moving corrections between source files, clean copies, tracked copies, tables and supplementary files; resolving inconsistent terminology across a document set; waiting for a scarce specialist to finish routine preparation before reaching the questions that actually need judgment; and discovering document damage or unresolved references at final delivery, when it is most expensive.
The design response is document-state-first. Generic AI tools centre the conversation. This platform centres the document and its production state: what arrived, which instructions apply, what changed, what is still uncertain, who approved it, and which files make up the delivery package. In professional document operations that distinction decides whether a system is usable. A chat interface can improve a paragraph. It cannot tell you, four months later, which version was released and on whose authority.
AI does the repeatable work; people keep the authority. The suite runs first-pass editing, translation and reference work inside a defined job specification, and routes interpretation, specialist judgment, exceptions and release decisions to qualified people. That division is the product. It is not a compromise between automation and quality, it is what makes the automation safe to use on work that carries a name.
What a partner can buy. The white paper sets out four engagement models: run the software yourself with your own staff and release authority; hand the whole configured workflow to Uni-edit under your brand; keep your normal workflow and use Uni-edit as overflow capacity during peaks; or integrate selected capabilities into a portal you already operate. It also covers what may be branded and what should stay governed. White-labelling changes presentation and commercial ownership. It should not erase provenance, quality controls, or the evidence needed to operate responsibly.
On measurement, it refuses to flatter itself. The document says plainly that figures such as "30% faster" are pilot hypotheses until they are measured against the customer's own baseline. There is no benchmark table, because there is no benchmark. What it offers instead is a list of the things worth measuring during a pilot, and a recommendation to start with one genuinely awkward workflow rather than a demonstration chosen because it will go well.
Publishers, journals, universities, research institutions, editing companies, translation agencies, and specialist document teams.
A paid pilot on one difficult workflow.
Uni-edit holds no ISO certification, accreditation or independent conformity assessment. The document maps a customer's control requirements to recognised security, privacy and language-service frameworks so that procurement has something concrete to assess, and it says in terms that a mapping is not a certification.
Capabilities described as proposed or planned remain subject to implementation, validation and commercial agreement. Performance figures are pilot hypotheses until they are measured against your own baseline.
Where work runs in the controlled offline-first environment, the evidence describes what happened inside that defined run. It cannot describe what happened on another device, before the files reached us, or outside the defined environment. That boundary is the reason the evidence is worth anything, and it holds whatever the product's maturity.
