Manually writing alt-texts for thousands of images is an unrealistic task. A media library of 50,000 images demands hundreds of human-hours, and the quality often suffers due to editor fatigue. Our AI system processes such volumes in one hour with 94% accuracy—10 times faster and 20% cheaper than manual labor. We automate this process using vision-language models, delivering quality close to editorial standards at a fraction of the time.
According to internal tests, the system achieves 94% alignment with editorial standards on a sample of 1000 images.
Why automating alt-texts is a necessity
Without alt-texts, your content remains invisible to screen readers and ranks poorly in search results. Manually handling even 10,000 images requires weeks of a copywriter's work, and consistency suffers. Our system solves both problems: it generates accurate, SEO-optimized descriptions automatically, taking into account the page context and brand guidelines.
How we achieve 94% accuracy
Accuracy is the result of contextual prompting: the system receives the page title, category, and surrounding text. For each client, we configure description rules—for example, mandatory brand mentions or exclusion of certain objects. Regular A/B tests compare machine-generated descriptions against editorial ones, and we adjust the model when deviations occur.
Want to see how it works on your data? Get a test run for 100 images—just contact us.
How we build the system: stack and approach
For alt-text generation, we use a combination of vision-language models:
- GPT-4V / GPT-4o — maximum accuracy, context understanding, support for complex scenes.
- LLaVA 1.6 / InternVL2 — self-hosted option for strict confidentiality requirements.
- BLIP-2 — lightweight model for high-frequency generation (batch up to 500 images/min).
Integration is done via REST API with CMS (WordPress, Contentful, Strapi) or S3/GCS buckets. Operation modes: scheduled bulk processing or real-time hook on image upload. Prompts are customized to the brand style—what to include (objects, colors, actions) and what to ignore. To optimize quality, we apply fine-tuning with LoRA and control p99 latency at <150ms.
Comparison: automated generation vs. manual work
| Criteria | Manual generation (copywriter) | Our AI system |
|---|---|---|
| Speed | 1–2 minutes per image | 100–500 images/min (batch) |
| Description accuracy | ~95% (average) | ~94% (vs human benchmark) |
| Consistency | Depends on the writer | Uniform style across all descriptions |
| Scalability | Linear cost growth | Nearly constant cost at large volumes |
| Language support | Depends on linguist | 50+ languages out of the box |
Model comparison: when to use what
| Model | When to choose |
|---|---|
| GPT-4V/GPT-4o | Maximum accuracy, complex scenes, no data transfer restrictions |
| LLaVA 1.6 / InternVL2 | Self-hosted, confidentiality, control over infrastructure |
| BLIP-2 | High throughput, bulk processing, low cost per million tokens |
How to set up prompts for your brand?
The system supports templates with variables: brand name, color palette, mandatory objects. For an online clothing store, you could specify: "Mention the brand at the beginning, describe the color, style, and material. Avoid personal opinions." Ready prompts are tested on a sample of 200 images before launch.Work process: from audit to deployment
- Analytics: we study your media library, page structure, and description requirements.
- Design: select the model, design the pipeline, configure prompts.
- Implementation: integrate with CMS via API, configure batch and real-time modes.
- Testing: compare against reference descriptions, adjust prompts until target quality is achieved.
- Deployment: roll out to production, monitor quality via A/B tests.
What's included
- Documentation: solution architecture, operation instructions, API description.
- Access: to models (cloud or self-hosted), to the pipeline, to the monitoring dashboard.
- Training: 1–2 hour workshop for content managers and developers.
- Support: 2 weeks post-launch support for tuning and optimization.
Timeline and cost
A typical project takes 1 to 2 weeks from the moment we receive access. The cost is calculated individually, based on media library size and integration complexity. We don't hide pricing: contact us to get a preliminary estimate within one day.
Our team has completed 12 content automation projects for major retailers—that's 5 years of AI experience and hundreds of thousands of images processed. We guarantee data confidentiality when using self-hosted models and compliance with WCAG 2.1 AA. All system components are tested and validated in production.
Contact us to discuss your project. Get a consultation on implementation today.