An estimate shows that to process 20,000 items, you’ll need $21,000 and one year of time.

1. Initial data:

• 2 employees fully focused on the task: searching, analyzing, and entering data.
• One employee can process up to 50 cards per day (with an 8-hour workday; each card takes ~10 minutes to find, collate from different sources, check for duplicates, and format).

2. Work volume:

• Total: 20,000 cards.
• A team of two can fill: 2 × 50 = 100 cards per day.
• Total required: 20,000 / 100 = 200 working days.

3. Real-life adjustments:

• Considering inefficiencies (holidays, vacations, sick leaves), add 20% insurance time:
• 200 × 1.2 = 240 calendar days.
• Considering weekends: work only on weekdays, so 240 / 5 × 7 = 336 calendar days.

4. Financial estimate:

• Assume employee salary = $875/month
• For 12 months, two employees: 2 × $875 × 12 = $21,000
• Cost per card: $21,000 ÷ 20,000 = $1.05 per card

Conclusion:
Employees would do this work for $1.05 per card, taking about a year.
AI completes it in 5 to 10 days at a much lower cost ($0.44 per card), saving both money and time.

Note:
Logistics departments often describe the task as time-consuming and difficult to standardize manually. Experience shows that data enrichment can be automated, drastically reducing labor and speeding up your business processes.

For each 10,000 items:

• About 18,200 internet search queries are made.
• Online search time: 114 continuous hours.
• Average amount of collected information: 228,000 sentences (about 10 copies of "The Count of Monte Cristo" ).
• 16 AI agents gather, check, and rephrase information, generating the final answer.

Using AI significantly speeds up the collection and structuring of information from open sources (websites, online stores, manufacturer data) compared to manual processing.
This automates over 98% of routine work, reduces staff workload, and allows your team to focus on reviewing and clarifying only non-standard cases.
Possible edge cases where AI may provide incorrect data:

1. Errors in sources: If a manufacturer or marketplace website contains incorrect information, AI may reproduce these errors.
2. Incorrect SKU in the database: If your company’s system initially has the wrong SKU, AI may match data incorrectly.
3. Mixed modifications: If a product is presented in multiple variants and the source doesn’t distinguish features of each, incorrect aggregation of parameters is possible.

Despite these risks, practice shows the inaccuracy rate does not exceed 2% of the generated data.
Conclusion:
AI tools allow you to quickly enrich and structure the majority of your database, and any rare edge cases are addressed by specialists. This is an effective combination of automation and human expertise. For accurate and fast review, sources are provided for each data point.

You receive already parsed data, ready to be imported into your system:

1. JSON format with key attributes for database upload
2. Excel format for specialists to assess result quality