Multimodal Is Pioneering Custom Generative AI Solutions For Businesses

Large language models (LLMs) have shattered a long-held belief that sophisticated, high-value tasks can only be done manually by human experts. However, most organizations are still far from experiencing the seismic shifts that these models promise to catalyze. 

While many are using LLMs to perform minuscule tasks or enhance their products in marginal ways, they’re missing a critical component that would drive true transformation: customization of the models for their specific use. 

Model customization would enable organizations to automate end-to-end workflows – instead of just insignificant parts of their processes – as well as develop truly breakthrough products and services. 

We’ve set out to help them unlock such radical transformations and drive the revolution forward. 

The problem with generic LLMs

Generic LLMs have several flaws that prevent organizations from fully harnessing the potential of this transformative tech:

• Hallucination. Most LLMs are trained on unvetted web data, which increases the likelihood of biased, unverified, and incorrect responses. This makes them unsuitable for tasks that require high accuracy, such as critical decision-making.
  
  
• Lack of data recency and relevancy. LLMs often lack access to up-to-date information, which makes them prone to providing outdated responses. Additionally, most LLMs also lack access to private organizational data, which limits their applicability.
  
  
• Lack of specialization. Off-the-shelf LLMs are usually trained for a myriad of different use cases, which often decreases their effectiveness in performing any one specific task.
  
  
• Lack of adaptability and control. LLMs don’t adapt to individual users nor grant them sufficient control that would allow for widescale automation. Most organizations try to bypass this by merely prompting the models to generate desired outputs, which falls short of true personalization, limits scalability, and requires too much manual effort.

Our goal is to solve these issues and allow organizations to automate their end-to-end operations, unleash human potential, and stand out on the market.

Our solution

We believe the key to making LLMs truly efficient and profitable lies in extensive customization and a personalized approach. We provide both through the following core services:

• Collaboration. We work one-on-one with organizations to ensure we understand their needs and requirements and design a suitable solution. That way, clients get full control over their models and how they work.

• Use case customization. In order to boost model performance and accuracy, we customize LLMs for organizations’ specific use cases. This includes training the models on organizations’ desired tasks using few-shot and in-context learning, as well as advanced instruction tuning.

• Custom data training. We help organizations curate and prepare their own documents for model training, so that the LLMs can learn from the most relevant data possible. This minimizes the risk of inaccurate responses and boosts the models’ domain-specific knowledge.

• Custom agent development. Complex use cases often require more advanced solutions. In such instances, we develop custom agents that act as intermediaries between organizations and their models and enable a more sophisticated use of LLMs. For example, agents can enable the model to fetch real-time data or retrieve it from dynamic data sources, solving the data recency and relevancy issues of generic LLMs.

• Custom integrations. LLMs can’t automate entire workflows or boost customer experience in isolation. To achieve that, we integrate them with organizations’ internal systems or products and ensure they work seamlessly in their intended environments.

• Continuous monitoring and optimization. We offer organizations ongoing support that includes regular evaluation, feedback loops, and iterative improvements of their models. This eliminates the necessity of forming expensive in-house AI teams.

By providing an all-in-one service and prioritizing a deep understanding of our clients’ unique needs, we’re able to take their efficiency, productivity, and competitiveness to the next level — a level they couldn’t achieve with even the most advanced off-the-shelf models.

Our qualifications

Extensive model customization requires a high level of technical expertise, so we made sure to assemble a global team of experts. Our team consists of experienced NLP, ML, data, and MLOps engineers and researchers, many of whom have prior experience in founding advanced tech startups.

Our founder, Ankur Patel, has already founded and led several successful AI startups, including Glean AI and R-Squared Macro. He also has extensive experience in natural language processing, unsupervised learning, data science, and finance.

Some of his accomplishments include working as the lead emerging markets sovereign credit trader for Bridgewater Associates, leading data science efforts for AI firm ThetaRay in NYC, and serving as the vice president of data science at 7Park Data, which was acquired by Vista Equity Partners.

Ankur has written two technical books in artificial intelligence with O’Reilly Media, Natural Language Processing in the Enterprise and Hands-on Unsupervised Learning Using Python, teaches AI at Open Data Science Conference (ODSC), and regularly speaks on applying AI in business at meetups and conferences.

Today, we’re dedicated to lending our expertise to organizations that want to transform how they work and how they position themselves on the market.

We’re already working on amazing projects, and we look forward to more

At the moment, we’re laser-focused on helping organizations work faster, unleash human potential, and drastically improve customer experience through groundbreaking services and products. 

With that in mind, we are currently working on three main types of projects.

• AI document processing. We train LLMs on clients’ custom schema, enabling them to automatically classify, store, and extract data from documents in exactly the fields they need. 
• Workflow AI. We build custom agents and integrate these custom APIs into clients’ internal systems to enable end-to-end workflow automation, including high-stakes processing and generation tasks.
• Product AI. We integrate custom APIs into clients’ products, empowering them to offer innovative features and services and optimize customer experience.

Our experts design highly customized solutions for each department, and our clients are already experiencing significant revenue increases and time and cost savings. We’ll present some of these results in upcoming case studies.

Interested in seeing how our models work in action in the meantime? Feel free to schedule a free 30-minute demo with our experts today.