News
Key Takeaways
- Document automation. Vertesia launches Semantic DocPrep to automate PDF-to-XML conversion.
- Accuracy boost. Service designed to improve GenAI model reliability and output quality.
- Enterprise developer impact. Reduces manual prep time enabling faster, more accurate GenAI app deployment.
Vertesia announced the launch of its Semantic DocPrep service on June 3, 2025. The cloud-based API service converts complex PDF documents into semantically tagged XML for use in generative AI applications.
According to company officials, the service aims to address two persistent challenges in enterprise GenAI adoption: ensuring output accuracy and reducing data preparation time. Vertesia research suggests document preparation consumes up to 50% of GenAI development time.
The service is designed for developers building custom GenAI applications and retrieval-augmented generation systems, with a focus on improving accuracy and reliability of model outputs.
Stakeholders in the Crosshairs of AI Accuracy
- Enterprise developers building custom GenAI applications
- Semantic DocPrep’s PDF‑to‑XML conversion offers clean, schema‑ready inputs for LLM fine‑tuning, RAG pipelines and prompt engineering, slashing rework cycles and boosting first‑run accuracy.
- IT leaders managing document-intensive AI workflows
- CIOs and heads of data ops can standardize unstructured PDFs into machine‑readable XML at scale, tightening governance, reducing compliance risk and freeing infrastructure teams from manual ETL bottlenecks.
- Organizations seeking to reduce GenAI development time
- For healthcare systems, financial institutions, manufacturing giants and more, halving data‑prep hours means faster model deployment, quicker ROI and a competitive edge in AI‑driven customer experience.
Why Timing Is Right for PDF‑to‑XML Automation
Brooklyn-based Vertesia has entered the document preparation space with its Semantic DocPrep service. The solution targets a significant pain point in GenAI development: the processing of unstructured data, which makes up 80% of all data. According to Vertesia research, document preparation consumes up to 50% of GenAI development time.
The launch comes amid growing industry recognition that quality assurance is essential for AI applications. Enterprise leaders increasingly find that 95% accuracy is insufficient, according to company officials. This aligns with broader market trends where organizations are seeking ways to not just manage AI but to actively shape the narratives AI creates.
Vertesia asserts that its hybrid approach to document processing will eliminate hallucinations and improve reliability of LLM outputs. The service converts PDFs into semantically tagged XML, which the company claims will streamline integration with modern AI pipelines without additional setup or training.
As more companies develop AI applications, solutions that address document preparation challenges may prove valuable for organizations looking to reduce development time while improving output quality.
"The two concerns we hear most from enterprise leaders are consistent: 95% accuracy isn't good enough, and data preparation is a costly, time-consuming challenge."
- Chris McLaughlin
Chief Revenue Officer at Vertesia
Inside Semantic DocPrep: Features That Matter
According to Vertesia, the Semantic DocPrep service offers several technical advantages:
| Feature | Description |
|---|---|
| Document conversion | Transforms PDFs into structured XML without altering source content |
| Hybrid processing | Selects appropriate AI model based on page content type |
| Structure preservation | Maintains original document relationships and context |
| API integration | Fits into existing AI pipelines without additional setup |
| Multi-format support | Processes PDFs from Word, PowerPoint and other formats |
About Vertesia: Low‑Profile, High‑Impact
Founded in 2024 and headquartered in Brooklyn, NY, Vertesia is a privately held software company that bills itself as a “unified, low‑code platform” for rapidly building and operating generative‑AI applications and agents. Its mission is to help enterprises deploy GenAI at scale, and its vision is to enable agentic enterprises where AI is embedded across core and support processes.
Vertesia’s Market Position
Vertesia occupies the fast‑growing niche of unified, low‑code GenAI platforms aimed at large enterprises that need multi‑model flexibility, strong governance and rapid deployment without assembling piecemeal frameworks.
The company is still an emerging player — absent from most major analyst quadrants and facing stiff competition from hyperscaler stacks (like AWS Bedrock and Google Vertex AI) and specialist platforms (e.g., LangChain, Akkio). Its differentiation rests on agentic workflow orchestration, built‑in monitoring and deep document‑prep automation, which together promise faster ROI and lower operational risk for enterprises scaling GenAI.
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