Regulated / Multi-Market Content
Translation Compliance Exposure
A compliance violation in a source document ships to every translated market it reaches. Model what that exposure costs each year, and what catching violations at source, before translation, saves.
Your Regulated Content
These are your numbers and your estimates, not published statistics. Results update instantly.
Regulated documents, claims, or product descriptions translated per year
Jurisdictions each document ships to, every one multiplies the exposure
Your estimate: share of source documents carrying at least one compliance violation (drug claim, prohibited concentration, unsubstantiated superlative, missing safety warning)
Detection rate (from the research)
The study measured 0% detection for unaided AI and 100% for the knowledge layer. Adjust either to match your own reality.
Share of source violations your current AI / manual process catches. The study measured 0% for both a plain LLM prompt and a glossary-pasted-in (RAG-lite) baseline — raise it if you catch some today
KGMT detected 100% of source violations on the study's controlled corpus. Lower it to be conservative about your own content; it never counts below the rate you already catch
Reviewer minutes per document when nothing is pre-flagged
Minutes per document when violations are flagged at source for confirmation
Fully-loaded hourly cost of a compliance reviewer
Knowledge-layer investment
$120K/yr
Built from the TextDistil price list: a platform license plus $20K per modality and $20K per Business Decision Context.
Annual platform license
Text, Video, Audio, Image, Network — one per content type you run through the layer
Each BDC is one decision the knowledge graph is built to serve; deployments start with one or two
Incident exposure (advanced, optional)
A probability-weighted estimate built from your own numbers - directional, not a prediction.
Your estimate of one compliance incident, fine, recall, or market rework. Leave at Excluded to skip
The Detection Gap
Unaided AI caught 0% of source-compliance violations. A knowledge-graph layer caught 100%.
A peer-reviewed study ran a controlled 30-error regulatory corpus (EN→Greek/French) through three conditions. The knowledge-graph pipeline caught 100% of source-compliance violations. Both unaided baselines caught none: a plain prompt, and one with the glossary and compliance tables pasted in (Gene & Sosoni, NeTTIT 2026). At your numbers, the gap looks like this:
Violations Missed Today
200 docs/yr
Docs with a source violation that go undetected at your current detection rate
Caught by the Knowledge Layer
200 docs/yr
Incremental source violations the knowledge layer catches that today's process misses
Market Exposures
600 /yr
Unflagged violation-market instances, each missed doc ships to every market
Review Cost Saved
$333K
3,700 hrs/yr saved, at your reviewer rate
The second risk: two runs, two answers
Peer-reviewedIn the study the same prompt produced different translations on different runs, so an unaided model can pass a check one day and fail it the next. Regulated workflows treat terminology consistency as a legal requirement. A knowledge layer is deterministic: the same source segment gets the same auditable terminology every time.
Before vs. After
Annual compliance-review cost, unflagged vs. pre-flagged at source
Projected annual savings of $333K
What this adds up to
Annual Benefit
$333K
Review savings (incident exposure not modeled)
Payback Period
5 mo
Months to cover the $120K/yr knowledge-layer investment
The 100%-vs-0% detection result is peer-reviewed on a controlled corpus (the enhanced KGMT pipeline); how it generalizes to your corpus is directional, which is why both detection rates are sliders you control. Your error rate, review times, and incident figures are your own estimates. Adjust everything until it matches what you actually see.
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Commonly Asked
Methodology & Sources
This tool multiplies your own operational estimates through deterministic formulas. The only external inputs are the peer-reviewed detection rates (0% baseline / 100% knowledge layer), seeded from two Gene & Sosoni papers and adjustable by you; every other input is yours.
Detection: 100% vs 0%
On a controlled 30-error regulatory corpus (a 500-word retinol product description, EN→Greek/French), the enhanced knowledge-graph-mediated translation pipeline detected 100% of source-compliance violations. Both unaided GPT-5 baselines detected 0%: a plain prompt, and one with the glossary and compliance tables pasted in (RAG-lite). Source: Gene & Sosoni, NeTTIT 2026. The controlled-corpus result is solid; how it generalizes is directional.
Source vs. translation-introduced (NCS)
The papers' Non-Compliance-in-Source (NCS) classification separates violations that start in the source, and propagate to every market, from those a translator introduces. That is the split this calculator models: it scores the source-propagated exposure a knowledge layer catches before translation.
Consistency across runs
The study found the same prompt yields different translations on different runs. Regulated work needs the same answer every time, and a knowledge layer returns the same auditable terminology on every run. We don't put a dollar figure on this, but it is a real second risk.
Not only cosmetics
The method applies wherever a claim legal in one market breaks the rules of another. The papers work through cosmetics (US FDA vs. EU) and point to financial services (US CFTC vs. EU MiFID II) as the same problem in a different sector.
Your numbers, not ours
Source error rate, content volume, markets, review time and cost, incident cost, incident probability, and both detection rates are yours to set. The model only does the multiplication; it never asserts an industry statistic on your behalf.
Full Source List
- Gene, V., & Sosoni, V. (2026). Knowledge-guided machine translation for regulatory compliance in high-risk industries. Proceedings of Convergence 2026: Human-AI Integration for Multilingual and Accessible Communication, pp. 67–82. Guildford, UK, June 17–19, 2026.
- Gene, V., & Sosoni, V. (2026). Dual-Metric Compliance and Quality Evaluation of Knowledge Graph Mediated Translation in Regulated Domains: An Enhanced Architectural Framework. Proceedings of the 3rd International Conference on New Trends in Translation and Interpreting Technology, pp. 109–118. June 24–27, 2026, Dubrovnik, Croatia. ©NeTTIT 2026. https://doi.org/10.26615/issn.2815-4711.2026_015
Read both papers → — download the full peer-reviewed research this calculator draws on.
This calculator is open source (Apache-2.0) — the model is transparent and yours to inspect. View the source on GitHub →
Go deeper
Catch violations before they ship to every market
Knowledge-graph-mediated translation checks compliance at source, so a violation never propagates through your language pipeline. See how KGMT makes regulated translation auditable.