11x.ai, a startup focused on automating workflows through AI bots, has raised $24 million in a Series A funding round led by Benchmark. The company, which specializes in creating “automated digital workers,” aims to replace repetitive tasks typically handled by human employees, allowing them to focus on more strategic responsibilities. Originally based in London, 11x.ai has now relocated its headquarters to San Francisco.

Founded in 2022, the company gained attention with its AI agent, Alice, designed for sales teams. It has since expanded its product lineup to include Jordan, an AI phone representative capable of holding intelligent conversations for up to 30 minutes. CEO Hasan Sukkar noted that these agents are a key part of 11x.ai’s strategy to create a suite of virtual employees tailored to different job categories. Sukkar also mentioned that the company is nearing $10 million in annual recurring revenue and counts companies such as Brex, DataStax, and Otter among its clients.

The new funding round comes just a year after 11x.ai raised a $2 million seed round, also led by Project A Ventures. With this latest investment, the company plans to accelerate product development and expand its team, currently numbering 27 employees. The funds will also support the continued growth of its AI worker suite, with additional virtual employees set to be launched in the coming months.

11x.ai’s digital workers are designed to automate tasks that traditionally require human labor, with agents like Jordan trained across 25 languages, including Swedish, Italian, German, and Hebrew. While competition in the AI automation space is fierce, with companies like UiPath, ServiceNow, and Salesforce already established, investors remain optimistic about 11x.ai’s potential. Sarah Tavel, general partner at Benchmark and a board member of 11x.ai, expressed confidence in the company’s future, noting that the demand for AI in workplace automation is rapidly increasing.

Sukkar emphasized that the development of AI agents capable of operating autonomously could transform industries on a scale similar to the internet or cloud computing. He envisions a future where AI agents handle highly skilled tasks without human involvement, drastically enhancing productivity.

While the company’s future plans include further expanding its product lineup and team, 11x.ai will maintain an office in London but relocate most of its key staff to San Francisco, positioning itself closer to the heart of tech innovation.

HyperWrite’s Reflection 70B, a new large language model (LLM) based on Meta’s Llama 3.1-70B Instruct, has been introduced by Matt Shumer, CEO of HyperWrite. Shumer claims that Reflection 70B is now the most powerful open-source AI model in the world, outperforming Meta’s Llama series and competing with top commercial models. What sets Reflection 70B apart is its innovative self-correction capability, allowing the model to recognize and fix its own mistakes during inference, enhancing its accuracy in various tasks.

Reflection 70B has been tested on multiple benchmarks, including MMLU and HumanEval, using LMSys’s LLM Decontaminator to ensure clean, reliable results. These benchmarks demonstrate the model’s ability to surpass its competitors in terms of performance. Users can interact with the model through a demo on a dedicated website, but the overwhelming demand has caused traffic issues, with Shumer’s team working to scale up GPU resources to meet the surge.

A key feature of Reflection 70B is “reflection tuning,” which allows the model to detect errors in its reasoning and correct them in real time. The system uses special tokens to mark reasoning steps, enabling more structured interaction and accurate outputs. This makes it particularly useful for tasks requiring precision, such as comparing numbers or counting characters in words, areas where many other models often fail.

Shumer also revealed plans for an even more powerful model, Reflection 405B, set to release soon, which is expected to outpace top closed-source models like OpenAI’s GPT-4. In addition, HyperWrite intends to integrate Reflection 70B into its main AI writing assistant, with details on this integration expected soon.

The rapid development of Reflection 70B was made possible by Glaive, a startup specializing in generating synthetic data for AI model training. Glaive’s technology allowed the HyperWrite team to quickly create high-quality datasets tailored to their needs, reducing the model’s training time to just three weeks. Shumer credited Glaive for this efficiency, highlighting how synthetic data can accelerate AI development.

HyperWrite, originally founded as Otherside AI in 2020, has grown significantly since its inception. Initially launched as a tool for crafting emails, HyperWrite has expanded to provide a range of AI-driven services, with two million users as of late 2023. The company’s cautious approach to accuracy and safety in AI development mirrors the reflection capabilities embedded in its latest models.

Looking forward, Shumer’s ambition is for Reflection models to set new standards for open-source AI, challenging established players in the industry like OpenAI, Anthropic, and Microsoft. With Reflection 405B on the horizon, the balance of power in generative AI might be shifting towards open-source innovations.

Concerns about an impending fourth AI winter are rising as doubts emerge over whether artificial intelligence will deliver enough practical benefits to justify its high costs. Recent reports from Goldman Sachs and other research bodies highlight this skepticism. However, a solution has been present for some time—engineered intelligence, a concept emphasizing the practical application of AI through engineering principles.

The Missing Link in AI Development

Traditionally, scientific breakthroughs in fields like chemistry and physics are first made in laboratories and then transferred to engineers to develop real-world applications. This process ensures that discoveries are turned into practical solutions. However, AI lacks a similar transition mechanism. Instead of a dedicated discipline for applied AI, organizations often hire data scientists—primarily researchers—to work on developing practical AI solutions. This mismatch has contributed to a significant failure rate, with 87% of AI projects not reaching successful outcomes.

What is Engineered Intelligence?

Engineered intelligence, or intelligence engineering, is emerging as a new field focusing on applying AI research in practical settings, much like how chemical or mechanical engineers utilize scientific discoveries. This discipline allows experts, scientists, and engineers to develop intelligent solutions without needing to become data scientists. By reestablishing research-to-engineering pipelines and forming partnerships with academic institutions and technology vendors, industrial organizations are setting the stage for engineered intelligence. This approach mirrors how breakthroughs in other scientific areas are handed off to specialized engineers.

The Benefits of Intelligence Engineering

With intelligence engineering, AI research can be transformed into breakthrough applications, yielding tangible value and producing outcomes that might not have been identified by data scientists alone. The process facilitates creating value-driven AI solutions that are feasible and safe for production use, contributing to meaningful advancements across various industries.

Implementing Intelligence Engineering: A Five-Step Approach

To introduce intelligence engineering into an organization, practical experience is crucial. Here’s a five-step guide that differs from traditional AI implementation methods:

1. Map Existing Expertise: Identify areas of expertise within current organizational processes.
2. Evaluate Expertise Value: Assess which expertise is most valuable and determine its scarcity or abundance.
3. Prioritize Expertise: Focus on the top five areas of valuable and scarce expertise.
4. Analyze for Feasibility and ROI: Examine these areas for potential return on investment, feasibility, cost, and timeline.
5. Invest in Execution: Select a subset of these valuable use cases and proceed with development.

A New Wave of AI-Driven Value Creation

By adopting intelligence engineering, organizations can expand their capabilities, moving beyond existing expertise to identify new opportunities. This approach enables safe and practical value creation, both within organizations and across broader ecosystems. As more industries and educational institutions develop programs focused on engineered intelligence, the resulting innovations will unlock unrealized economic and societal benefits, paving the way for new job categories and a surge in value creation.

Brian Evergreen, author of Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence, and Kence Anderson, author of Designing Autonomous AI, both emphasize the potential of engineered intelligence to redefine AI’s impact in practical, everyday applications.