Ecommerce Product Data Entry Services: Get Your Backlog Live
We enter, normalize and validate supplier product data in controlled batches, and check a sample in your store before the batch goes live.
- 4.9/5 on Clutch
- 431+ projects delivered
- 14 years on the market
Trusted by
Partner of
- Adobe
- Shopware
- BigCommerce
- Shopify Plus
- Emporix
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4.9/5
Overall review rating by Clutch
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431+
Successfully delivered projects
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3.5 yrs+
Avg engagement duration
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14 yrs
On the market
When you need product data entry
A row can import cleanly and still describe the wrong product. Good data entry means checked records, not just filled fields.
SKUs wait for approval
A new supplier, seasonal range or market leaves products waiting to be entered, checked and published.
Supplier files never match your store
Every supplier sends different columns, units and category names, so your team fixes the same values by hand.
Errors show up after publishing
Wrong sizes, mixed-up variants or missing images surface on live product pages, after customers have seen them.
Product data entry use cases
Data entry help pays off when product facts are known but your team lacks time to enter and check them.
Typical situations where product data entry delivers the most value:
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Onboarding a new supplier
A new supplier sends hundreds of SKUs in its own format, and they must match your categories and attributes.
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Seasonal range or new collection
A new season brings many products at once, and your catalog team cannot enter them before the launch date.
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Opening a new market or channel
A marketplace or regional store needs its own required fields, units and categories for products you already sell.
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Moving to a new platform
Products, variants, prices and images must move to a new store with identifiers and relationships kept intact.
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Price and attribute updates
Bulk changes to prices, pack sizes or specifications must reach the right records with clear effective dates.
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Backlog after a team change
Unfinished product records piled up after staff changes, and nobody knows which ones are complete and approved.
Trusted in our clients’ own words
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5.0
What is most impressive is that they operate as a true strategic partner, not just a vendor.
The impact on our business has been transformative... Their proactive, forward-thinking approach is unique. They are constantly three steps ahead, bringing new ideas for our roadmap and pushing us to innovate in areas like customer journey, operations, and scalability.
Read full review
Tarek Yafi
Co-Founder at Undrgoods
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5.0
They are truly excellent in all aspects, and it’s a pleasure working with them.
They really care for the business and delivering great outcomes for us - they work work, diligently and strive for quality… Really experienced and right attitude of people: highly-skilled, intelligent, passionate about their work. Internal processes are excellent: well-organised, clear, repeatable.
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Dr. Petros Demetriades
CTO, Untamed Cat Food Ltd
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5.0
We especially liked their level of professionalism and attention to detail.
Dinarys effectively addressed all aspects of the project. The team’s ability to adhere to the agreed schedule was exemplary. Dinarys is very flexible and responds quickly to dynamically changing requests. All our expectations were met.
Read full review
Joseph Frank
CEO, Kaizen Technology Solutions
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5.0
Dinarys GmbH consistently meets our expectations and delivers high-quality work throughout the project.
They consistently prioritized our needs and vision. They were quick to adjust strategies when needed. They prioritized delivering high-quality work rather than rushing to meet quotas. Their team showed deep knowledge and skills in mobile app development.
Read full review
Yann Rigo
CIO, Panodyssey
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5.0
Their ability to communicate effectively while maintaining a positive attitude made the entire process smooth.
Dinarys brought extensive technical expertise to the table, consistently delivering solutions that met our needs with precision and innovation. One of the most unique aspects of working with Dinarys was the excellent "chemistry" between our team and theirs. This synergy made it easy to tackle challenges and move the project forward with confidence.
Read full review
Aaron Freitag
Managing Director, myfelt GmbH
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Read our reviews on Clutch
Compliance and partner status
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GDPR compliant
See our privacy policy -
HIPAA compliant
See our healthcare work -
Adobe Certified Solution Partner
As stated on our Clutch profile -
Shopware Certified Solution Partner
As stated on our Clutch profile -
Certified BigCommerce Partner
As stated on our Clutch profile
Agentic SDLC
AI-powered product data entry
AI suggests categories, pulls attributes from supplier files and flags conflicting values. A reviewer approves every suggestion, and missing product facts go back to the supplier.
Agents at every stage, engineers steering
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Plan
Agents draft specs, user stories and acceptance criteria from your tickets, docs and code. Engineers sharpen them with you.
- Claude Code
- OpenAI Codex
- MCP
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Audit and estimate
Agents map your codebase, dependencies and risky modules before we quote, so estimates rest on facts.
- Claude Code
- Cursor
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Build
Coding agents work in your repository with project rules and context. Engineers steer and own the design.
- Claude Code
- Cursor
- GitHub Copilot
- OpenAI Codex
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Test
Agents write unit and end-to-end tests with every change, so testing stops being the bottleneck.
- Playwright
- PHPUnit
- Pest
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Review and secure
Every pull request gets an AI review and a security scan. A senior engineer approves before merge.
- CodeRabbit
- Semgrep
- Snyk
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Ship and operate
Quality gates run in CI on every change. Agents help trace incidents from logs and alerts.
- GitHub Actions
- Sentry
- Langfuse
Agents take the heavy lifting. Senior engineers set direction, review every change and own what ships.
AI platform stack
What we use to build agents and AI features into stores, portals and back-office tools.
- Models
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- Anthropic Claude
- OpenAI GPT
- Open-weight models
- Agent frameworks
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- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Model Context Protocol
- Retrieval (RAG)
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- pgvector
- Qdrant
- Elasticsearch
- OpenSearch
- Enterprise AI cloud
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- Amazon Bedrock
- Azure OpenAI
- Evals and guardrails
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- Langfuse
- Promptfoo
- Evals in CI
- In commerce
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- Semantic search
- Recommendations
- Product content
- Support agents
- Demand forecasting
Engineering stack
The platforms and tools under every project.
- Commerce and PIM
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- Adobe Commerce (Magento)
- Shopify Plus
- BigCommerce
- Shopware
- Spryker
- Salesforce Commerce Cloud
- commercetools
- Pimcore
- Languages
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- PHP
- TypeScript
- Python
- Kotlin
- Swift
- Frameworks
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- Laravel
- Symfony
- Node.js
- React
- Next.js
- Vue
- Data
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- PostgreSQL
- MySQL
- MongoDB
- Redis
- Elasticsearch
- Kafka
- RabbitMQ
- Cloud and DevOps
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- AWS
- Google Cloud
- Azure
- Docker
- Kubernetes
- Terraform
- GitHub Actions
- GitLab CI
- Bitbucket Pipelines
- Monitoring
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- Grafana
- Prometheus
- ELK Stack
- Sentry
What our data entry services include
Pick the parts your batch needs. We agree the sources, the destination and who approves publishing before work starts.
Categories and taxonomy mapping
We map products to your storefront categories and send unclear placements to your merchandising team.
Specifications, SKUs and variants
We enter names, identifiers and attributes, normalize units and values, and check parent and variant links.
Prices and bulk updates
We apply approved prices with currency, tax, pack basis and effective dates to the intended records.
Images and descriptions
We attach approved images to the right product and variant and structure supplied text without changing product claims.
Validation and missing facts
We check each record against your rules and return missing or conflicting facts to the right person.
Upload and store checks
We import the approved batch and check a sample of live product pages, categories and images.
How we work
We start with one product family, one source and one channel, then scale once the first batch passes review.
1. Share a sample batch
You share a source file, your store and a reference product. We agree fields, rules and approvers.
2. Map and enter the data
We map source fields to your store, enter and normalize records and log every missing or conflicting value.
3. Review a store sample
We upload a sample, check product pages, variants and images in the store, and fix what fails.
4. Release and hand over
After your approval, we release the batch and hand over the file, the mapping and a log of open questions.
Product data case studies
Office supplies wholesaler: catalog update
Challenge
A wholesaler needed data on over 137,000 products reviewed and updated, with inconsistent names, SKUs and data types in the source.
What we delivered
– Scripts to make source fields consistent
– Updated product names, SKUs and images
– AI-assisted product categorization
– Meta descriptions added for all products
Result
Clear categorization and updated product listings led to a 20% conversion rate increase.
Prostor: 50,000 SKUs moved to Magento 2
Challenge
A drugstore chain with 427 stores needed 50,000 SKUs with prices, descriptions and stock moved while its store stayed live.
What we delivered
– Custom migration tooling for 50,000 SKUs
– Prices, descriptions and stock kept intact
– Real-time inventory sync across 427 stores
– Validation layer added to the ERP sync
Result
50,000 SKUs moved to Magento 2 with 99% uptime, and early stock mismatches were resolved in the first month.
Bizol: product data moved into Pimcore
Challenge
Bizol’s existing system lacked the flexibility to handle its growing catalog of motor oils, car care products and QR codes.
What we delivered
– Data model refined in an extra analysis session
– Source fields mapped to Pimcore’s structure
– Relationships between content kept intact
– Documentation and integration with existing websites
Result
Accurate, consistent content in Pimcore and faster time-to-market for new products.
Why companies choose Dinarys
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01
Delivery governance built into engineering
We bring structure, predictability, and control into software delivery.
Our approach is shaped by PMI-based project management, quality gates, risk management, and a success-first delivery culture.
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02
Accountable engineering
We do not only take tasks and write code.
We analyze the business and technical context, identify the real bottleneck, challenge weak assumptions when needed, and take responsibility for the technical outcome.
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03
Senior expertise for CTO-led execution
We support technical leaders with experienced engineers, architecture expertise, and practical roadmap execution.
This helps CTOs and Fractional CTOs stay strategic while having a reliable team to execute.
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04
Curated teams, not generic outstaffing
Every dedicated team is built around the client’s actual requirements, workflow, culture, risks, and delivery context.
We assess technical skills, soft skills, project fit, culture fit, communication style, and risk factors before proposing engineers.
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05
Practical AI, from the process
We start from the business process, compare platform features, ordinary automation and AI, and use AI only where it removes real work.
Every AI step gets quality checks and human oversight.
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06
A partner that has your back
Good delivery depends on trust, honest communication, and people who care about the outcome.
We stay close to the problem, speak openly, involve the right experts, and do not leave clients alone with delivery risk.
Frequently asked questions
Category mapping, specifications, SKUs and variants, prices, images and descriptions, validation and upload, plus a store sample check before the batch is released.
Keep small, clear uploads in-house when native import tools cover your fields. A partner helps when supplier formats vary, variants are complex or validation keeps failing.
We don’t guess. The record stays on hold and goes back to your supplier or data owner, while other records in the batch can continue.
We have handled product data in BigCommerce, Magento and Pimcore projects. For other platforms, we check native import tools and APIs on your sample first.
AI can suggest categories and pull attributes from supplier files. We compare its suggestions with a reviewed sample, and a person approves every change before publishing.
It depends on source formats, identifier quality, variants, number of fields, missing facts, channel rules and upload method. We estimate after reviewing a sample batch.
Send us a sample batch
Share a source sample without customer data, your store and channel, and a reference product. We’ll propose batch rules and an estimate.