How should label reviews arrive? Check label text, ingredient declarations and product claims against your approved regulatory checklist.
One label includes up to 30 approved checklist checks.
Review from exports Cited label issue report $1,296 – 3,600 setup Connect the artwork review queue Add an authorized system connection $1,584 – 4,440 setup
Which system should it connect to?Product information system API Internal artwork review queue A qualified regulatory reviewer approves every label. No legal clearance, inferred allergen safety, medical advice or automatic packaging release.
What is included Start with exported label text and product specifications for an agreed jurisdiction. Artwork geometry, minimum type size, laboratory verification and deriving nutrition values are outside the baseline.
Start with exported label text and product specifications for an agreed jurisdiction. Artwork geometry, minimum type size, laboratory verification and deriving nutrition values are outside the baseline.
Label text: up to 3,000 words.
Jurisdictions: up to 1 expert-approved jurisdiction.
Model input budget: up to 9,000 tokens across the reference run.
Model output budget: up to 1,600 tokens across the reference run.
Standard independent test set: at least 100 labels.
Strict independent test set: at least 200 labels.
Reference application CPU: 2 cores.
Reference application memory: 4 GB.
What standard do you need? Choose the acceptance targets your provider must demonstrate on your own test material.
Standard Test the defined workload and controls. Checklist issues found: ≥95% Flagged issues confirmed: ≥90% Included Strict Tighter targets and a larger independent test set. Checklist issues found: ≥98% Flagged issues confirmed: ≥95% Setup +$360 – 1,200 My own requirements Agree different targets and scope with your provider. Quote separately
How these standards are measured Hold out product families and artwork revisions. Include ingredient mismatches, unsupported health claims, missing fields, outdated checklists and compliant near-matches. Agree the holdout set before tuning. Report each metric with its numerator, denominator and exclusions; abstentions count against coverage. Test all inputs, including failures, rather than only accepted results.
Targets for your selected standard What is checked Target Checklist issues foundDetected in-scope issues divided by all expert-labeled issues; report allergens and unsupported claims separately. ≥95% Flagged issues confirmedExpert-confirmed issues divided by all flagged issues, so flagging every label cannot pass. ≥90% Correct rule and source referencesIssues with the correct current checklist rule and source text divided by all reported issues. ≥98% Approval and data controlsPassed control cases divided by all independently tested unauthorized-action, stale-input and sensitive-data cases; any critical failure rejects delivery. ≥100%
Targets apply to the agreed independent test set, not every possible future input. During the pilot, a person reviews every result. Any unauthorized action or sensitive-data exposure fails acceptance.
How quickly do you need each label? Choose the time from accepted input to a reviewable label. Human review and waiting for source access are separate.
Within 90 seconds Included Within 60 seconds Setup +$144 – 480 Within 30 seconds Setup +$288 – 840
Timing details Measure wall-clock time including queueing, model calls, validation and permitted retries. Report failures and deadline misses. Faster targets need pilot testing; they are not vendor benchmarks.
The target applies to at least 95% of agreed test runs, with 1 in progress at a time.
How much should each label cost? Choose a machine-cost target per label. A lower cap may limit model calls and iterations.
Up to $0.50 Included Up to $0.40 Setup +$144 – 480 Up to $0.30 Setup +$288 – 840
Cost details Includes reference model usage, a retry allowance and allocated hosting at the stated volume. Human review, source-system subscriptions and paid connector access are separate. A tighter target must preserve the selected standards in the pilot.
Reference machine cost: $0.21 – 0.36 per label at 200 labels a month. The selected cap is a target to test, not a replacement for this estimate.
Where do you want it to run? Run the workflow in your cloud or on your own machine. Choose separately whether it may use an external AI model.
Run it onYour cloud Local environment AI model accessApproved model API No external model calls
Data and access details Run in your controlled cloud environment with scoped access and retained review records.
Use an approved external model with the agreed data-processing terms and minimum necessary input.
Local hosting does not itself keep data offline. Connected business systems may still use the network. Private inference needs suitable hardware and a separate measured running cost; access and retention rules must be agreed before real data is used.