Shakti-1B: A Vision-Language Model Built for Enterprise Excellence
At SandLogic, our mission is to push the boundaries of artificial intelligence by developing highly optimized language models that cater to enterprise applications. We believe that the future of AI lies in seamless multimodal understanding, where text, images, and structured data come together to provide actionable insights across industries.
With this vision in mind, we are proud to introduce Shakti-1B, our 1-billion parameter Vision-Language Model (VLM) designed to enhance enterprise AI applications with advanced document intelligence, multimodal reasoning, and OCR-based understanding.
Unlike traditional text-based language models, vision-language models like Shakti-1B are engineered to interpret both visual and textual data, making them exceptionally powerful for document processing, financial and legal AI, research analytics, and customer service automation.
Through rigorous testing and benchmarking, Shakti-1B has demonstrated superior performance against other leading VLMs across a wide range of industry-standard benchmarks, proving its ability to outperform larger models while maintaining efficiency and scalability for enterprise applications.
With its exceptional accuracy, speed, and domain adaptability, Shakti-1B is set to revolutionize how businesses process and understand multimodal data—enabling faster decision-making, reducing manual workload, and enhancing AI-driven automation across industries.
In the sections ahead, we dive into the benchmark results, real-world applications, and the technological innovations behind Shakti-1B that make it a game-changer in vision-language AI.
Benchmarking Shakti-1B Against Industry-Leading Models
We conducted extensive evaluations across 16+ industry-standard vision-language benchmarks, comparing Shakti-1B against other state-of-the-art models such as MoLmoE-1B, InternVL2-1B, SmolVLM-2.25B, MiniCPM-V-2.0-2.8B, Qwen-2VL-2B, and InternVL2-2B.
Here are some key highlights from our benchmarking:
#1 in Multiple Vision-Language Benchmarks
- MMMU_val: 42.5, outperforming MoLmoE-1B (34.9), InternVL2-1B (36.7), and MiniCPM-V-2.8B (38.2).
- DocVQA_test: 87.96, surpassing all except Qwen-2VL-2B (90.1).
- ChartVQA_test: 79.56, exceeding InternVL2-1B (72.9) and MiniCPM-V-2.8B (62.2).
- TextVQA_val: 80.75, better than MiniCPM-V-2.8B (72.7) and InternVL2-1B (70.5).
- OCRBench: 798, leading over InternVL2-1B (754) and MiniCPM-V-2.8B (701).
- MME_sum: 1910.62, higher than MoLmoE-1B (1782.2) and SmolVLM-2.25B (1801.9).
- MMStar: 50.13, exceeding MoLmoE-1B (40.2) and InternVL2-1B (39.4).
- RealWorldQA: 64.82, outperforming InternVL2-1B (50.3) and MiniCPM-V-2.8B (50.4).
🔍 Competitive Performance in Multimodal Benchmarks
- VQA_v2_val: 76.28, coming close to MoLmoE-1B (83.9).
- Ai2d: 77.29, second to MoLmoE-1B (86.4).
- MathVista (testmini): 46.2, leading against MoLmoE-1B (34) and SmolVLM-2.25B (37.7).
- HallusionBench: 40.07, showing strong capabilities in reducing hallucinations.
Shakti-1B vs. Qwen-2B: A Head-to-Head Comparison in Real-World Scenarios
When it comes to Vision-Language Models (VLMs), performance isn’t just about parameters—it’s about how effectively a model can address real-world challenges. Shakti-1B, with its enterprise-focused design, delivers superior accuracy and contextual understanding across critical tasks like OCR, floor plan analysis, and multimodal reasoning. Here’s how Shakti-1B stacks up against Qwen-2B in scenarios that truly matter to industries.
Shakti-1B vs. Qwen-2B: A Head-to-Head Comparison in UI Image Analysis
When evaluating UI elements, precision matters. Misidentifying an issue can lead to unnecessary design changes, while missing real errors can impact user experience. Our latest Shakti-1B vs. Qwen-2B comparison reveals how AI models interpret UI-based images—and which one gets it right.
Key Findings from the UI Image Comparison – Used ChatGPT as Judge here to validate Shakti 1B and Qwen 2B
Where Qwen-2B Went Wrong
❌ False Positive in UI Analysis
- Qwen-2B incorrectly flagged button placement issues, even though both images had identical button positioning.
- This suggests the model relied on generic UI rules instead of analyzing the actual layout.
❌ Overlooking Critical Issues
- It missed spelling errors (“costumers” instead of “customers”).
- It failed to detect missing translations, which directly affect usability for non-English speakers.
Why Shakti-1B is the Better Choice for UI Content Validation
✅ Accurate in spotting real content issues (spelling, translation, and context mismatches). ✅ No unnecessary UI fix recommendations, preventing wasted design revisions. ✅ Better at localization validation, ensuring seamless multilingual UI experiences.
Key Learning: AI-based UI analysis isn’t just about heuristics—it’s about context awareness. Shakti-1B proves that real intelligence in UI validation comes from accurately detecting meaningful errors, not just enforcing rigid design rules.
Why Shakti-1B Stands Out?
1️⃣ Unparalleled Performance in Vision-Language Understanding
Shakti-1B excels in multimodal reasoning, OCR tasks, chart-based comprehension, and document intelligence, making it ideal for enterprises dealing with financial reports, legal documents, and visual analytics.
2️⃣ Domain-Specific Adaptability
Unlike generic models, Shakti-1B has been optimized for finance, legal, and enterprise AI, ensuring better accuracy and adaptability for real-world business applications.
3️⃣ Efficient Training and Inference
Shakti-1B has been trained with cutting-edge techniques like VGQA, RoPE, and SwiGLU, allowing up to 50% faster inference speeds compared to other models of similar scale.
4️⃣ Optimized for Enterprise and Edge AI Applications
With low computational cost and high efficiency, Shakti-1B is tailored for real-time decision-making applications across industries like finance, healthcare, legal, and customer support.
Real-World Applications of Shakti-1B: Transforming Industries with Vision-Language Intelligence
Shakti-1B is not just another Vision-Language Model (VLM); it is tailored for enterprise-grade AI solutions, designed to understand, interpret, and generate insights from multimodal data. Below are some of the real-world applications where Shakti-1B is making a significant impact.
1️⃣ Financial Document Analysis: AI-Powered Financial Insights
Financial institutions and analysts deal with vast amounts of structured and unstructured financial data. Shakti-1B simplifies data extraction, interpretation, and analysis for:
🔹 Automated Report Analysis: Extracts and summarizes key insights from financial statements, earnings reports, balance sheets, and regulatory filings (e.g., SEC 10-K, 10-Q, and annual reports).
🔹 Fraud Detection: Identifies anomalies and irregular patterns in financial transactions and reports using multimodal reasoning.
🔹 Investment Research: Assists investment analysts in parsing through market reports, financial charts, and risk assessments with intelligent summaries.
🔹 Tax & Compliance Automation: Streamlines regulatory compliance checks by scanning legal tax documents and ensuring adherence to financial regulations.
Impact: Reduces manual workload, enhances financial accuracy, and speeds up data-driven decision-making for financial institutions.
2️⃣ Legal AI Assistants: Revolutionizing Legal Research & Compliance
Legal professionals often work with massive amounts of case law, contracts, and regulatory documents. Shakti-1B automates and accelerates legal research by:
🔹 Legal Document Summarization: Extracts key points from court cases, contracts, and legal filings to help lawyers quickly understand precedents and obligations.
🔹 Contract Analysis & Compliance: Identifies risks, obligations, and compliance gaps in contracts and agreements, ensuring regulatory adherence.
🔹 Legal Q&A & Case Law Search: Provides quick retrieval of relevant case law from legal databases based on text and document inputs.
🔹 Multilingual Legal Processing: Supports multilingual legal text understanding, making it useful for international law firms and corporate legal teams.
Impact: Reduces legal research time by 60%, enhances contract risk analysis, and improves legal decision-making with AI-powered insights.
3️⃣ OCR and Intelligent Document Processing: Advancing Text Extraction
Businesses and government agencies rely on accurate Optical Character Recognition (OCR) for digitization of physical documents, invoices, and historical records. Shakti-1B significantly improves:
🔹 High-Accuracy OCR for Multilingual Text: Extracts printed and handwritten text from scanned documents, contracts, receipts, invoices, and forms.
🔹 Intelligent Data Extraction from Forms & Tables: Analyzes structured data from financial reports, medical prescriptions, and insurance claims.
🔹 Image-to-Text Conversion for Digital Archiving: Converts old records, manuscripts, and government archives into searchable digital formats.
🔹 OCR for Multimodal AI: Enhances AI-powered chatbots and virtual assistants by recognizing and interpreting text from images.
Impact: Improves OCR accuracy by over 20%, reduces manual data entry costs, and enhances data accessibility for businesses.
4️⃣ Customer Service AI: Next-Gen Intelligent Support Bots
The customer support industry is evolving with AI-powered assistants that understand both text and images. Shakti-1B enhances the customer experience by:
🔹 AI Chatbots with Visual Understanding: Enables customer service bots to process and respond to both text-based and image-based customer queries.
🔹 Invoice & Order Processing: Automatically extracts details from scanned invoices, receipts, and warranty documents, reducing manual processing errors.
🔹 Multilingual Support AI: Supports global customer service interactions by recognizing and interpreting multilingual customer requests and documents.
🔹 Ticket Automation & Smart Responses: Enhances support ticket routing and resolution times by extracting key details from uploaded images and forms.
Impact: Reduces customer support resolution time by 40%, enhances CX automation, and improves accuracy in handling customer queries.
5️⃣ Scientific and Research Analytics: AI-Driven Insights for R&D
Shakti-1B is a game-changer for researchers and academics working with scientific papers, research datasets, and visual data analysis. Key applications include:
🔹 Research Paper Summarization: Extracts key insights, figures, and citations from scientific journals, medical research, and technical papers.
🔹 AI-Driven Data Extraction from Charts & Graphs: Analyzes scientific data, lab reports, and experimental results from visual charts and plots.
🔹 Biomedical Text & Image Processing: Assists in medical literature review, drug discovery, and genomic research through vision-language AI.
🔹 AI-Powered Patent Analysis: Extracts claims, prior art, and key innovations from patent filings and technical documents.
Impact: Accelerates scientific discoveries, reduces manual data analysis, and enhances research productivity with automated AI tools.
Why Enterprises Should Adopt Shakti-1B for Vision-Language AI?
✅ Optimized for Real-World Applications: Unlike generic VLMs, Shakti-1B is fine-tuned for enterprise use cases, making it a powerful tool for document-heavy industries.
✅ State-of-the-Art Performance: Outperforms leading Vision-Language models (VLMs) on benchmarks related to document comprehension, multimodal reasoning, and OCR accuracy.
✅ Scalable for Edge & Cloud AI: Designed to run efficiently on-premises and cloud environments, ensuring low latency and high throughput.
✅ Multilingual Support: Supports multiple languages, making it useful for global enterprises and cross-border applications.
✅ Enhanced Enterprise AI: Powers financial firms, legal teams, healthcare providers, customer support, and scientific research with unparalleled multimodal intelligence.
Shakti-1B is ready to drive the next wave of AI-powered enterprise transformation. We invite enterprises, AI leaders, and industry experts to explore how Shakti-1B can redefine AI-driven document processing, customer service, and research intelligence.
What’s Next?
Shakti-1B is just the beginning. At SandLogic, we are continuously innovating to push the boundaries of Vision-Language AI.
- We are scaling up the Shakti series with larger, more efficient multimodal models.
- Our upcoming enterprise-ready models will further reduce hallucinations and improve domain-specific accuracy.
- We are working on Edge AI compatibility, making Shakti-1B adaptable for low-power, real-time applications.
We invite AI researchers, enterprises, and industry leaders to collaborate and explore the potential of Shakti-1B.
Let’s connect and drive AI innovation together!
