Product label compliance using applied AI

Do you work in an international, multi-product organisation that sells food, drink, medication or cleaning products? You are probably involved with the extremely important manual checking of product specifications, wording, formatting, logos and regulatory requirements. Getting a product label wrong can have disastrous consequences particularly if key product ingredients are omitted. But making sure the label has all the correct information can be tedious and difficult work - highly prone to human error. It is time to transition this process towards automated label checking tools.

Label compliance isn't just about checking boxes; it's a multi-dimensional process that involves accurate claims and ingredients, (in the case of food and drinks) nutritional information, avoiding confusing statements, regulatory rules, brand policies and guidelines.

The risks associated with incorrect labeling span health risks, legal consequences, financial impact and brand damage.

Given the complexities and high stakes, we encourage a migration to automated label compliance systems. This means a move away from manually compiling relevant requirements and then manually going through checklists. An automated approach means that compliance professionals can focus on verifying, ie confirming that automated checks have been performed correctly. This is made even easier if automated label checking tools can highlight areas on the label itself that have been checked and confirm that it is compliant or needs to be fixed.

We find that time saving from automated labels checking typically comes from:

1. Correctly complied check list - have the right specifications automatically complied based on the market the product is to be sold into and the details of particular product specifications.
2. Generating a list of non-compliance items – an automated review of each rule with a pass or fail outcome and details of why it failed, eg wheat missing from the ingredients list, incorrect volume amount, missing recycle label, etc.
3. Identifying the specific area on the label – being able to quickly see why the rule has failed (and where) or if it has passed, where is it on the label.
4. Automatic remedy comment generation - so that the comment can be easily relayed back to the label artwork producer to remedy the error. This could be a paragraph of all errors or detail on the specific error and where it appears (or does not appear where it should).

So how is automated checking more consistent and detail focused than human checking? Using advances in applied AI and document review there are several techniques that can be used to drive significant increases in review consistency and time savings in manual label review.

AI-powered computer vision can analyze label artwork to ensure that text size, positioning, and overall design adhere to both regulatory and brand guidelines. It can detect errors that might be missed by the human eye. Natural language processing techniques can review the language used in claims, ingredient lists, and nutritional information to ensure it meets regulatory standards. Large language models can cross-reference label contents with regulatory requirements across different countries. Other applied AI techniques can understand the context in which information is presented, ensuring that claims are not misleading and that essential warnings are prominently displayed.

In summary, using applied AI in label compliance processes can achieve significantly better standards of accuracy and efficiency. Automated labels compliance helps better safeguard consumers and improve the efficacy and efficiency of the label compliance process for organisations.

Algospark is an applied AI solutions provider that has built numerous compliance solutions. Read more and see our label checker demonstrator here: https://algospark.com/frameworks/label-checker/