We crawl the Web for brands and extract relevant information to understand various facets of a brand. We can also quickly gather details about thousands of brands in different verticals based on our clients' business needs. We use a proprietary crawler seeded with initial brands either provided by the client or from a well-known knowledge base like DBPedia. We use a combination of LLM guided crawls and searches to quickly extract troves of specific information to create brand data.

For instance, a luxury brand purchase (like Louis Vuitton) provides insights into a user's affluence, while shopping at stores like Costco or Walmart highlights a different facet of consumer behavior and a brand like Titleist or Gibson sheds light on entirely new segments. With this brand registry, you can better understand your customers and craft solutions tailored to their brand affinities.

Modern search is no longer just about text, it also encompasses audio, video and images. Search is not about finding mere keywords—it's about identifying the true intent of a query and finding relevant content whether or not we have the text. Our novel combination of traditional search and latent vector embedding of content and queries ensures that if someone searches for “Black Nike basketball shoes with red highlight,” the system prioritizes “Nike” while finding the best attribute matches for the rest in the images, description and reviews.

We pioneered a new ranking methodology based on our original research and combine different indices and allow our clients to tune search relevance accordingly. We use a combination of Solr, Vector Databases and Embedding models to achieve our results. We have shown significant uplift when compared to commercial-grade product search engines employed by large e-commerce players.

Generic LLMs excel at conversational tasks, but they aren't always optimal for highly specific tasks with strict output requirements. We built a specialized library that runs on local LLM instances and enforces precise outputs suitable for machine consumption. We make it stupidly simple to programmatically configure new tasks and set up LLM inference endpoints.

On top of LLM serving we enable features like Pydantic output, with task specific caching. These advances recently introduced by commercial LLMs have been part of our proprietary library from the start, enabling seamless integration and robust code interactions with LLMs.

A critical part of any user interaction with a user interface—be it on an app or a website—is to understand what works and what doesn't. Our response modelling engine perpetually learns from billions of data points each day, tailoring content to each individual's preferences and behaviors.

By showing content users are most likely to click or buy, we ensure higher engagement and conversion rates across the board.

We built an LLM-first entity extraction engine that can understand malforformed data like error-laden transcripts and noisy images and convert that into stored and queryable entities. By harnessing LLMs as a central knowledge base, instead of a conversational agent, we achieved multi-modal capabilities. Our system can process physical receipts or even audio data and extract relevant entities without needing text conversion.

This library is production-ready for image and text data and is currently being developed and tested for voice, offering seamless document comprehension regardless of input format.

Recommendations are about aligning the right content with the right users. Our library uses LLMs to incrementally embed a user’s past behavior into a vector space. We embed content into the same space to identify the content that is most relevant to the user's behaviour. Using this library one can also get goal specific recommendations like cross selling, up selling or attaching accessories.

We have deployed this approach to great effect, delivering outstanding results for our clients. By merging classic recommender system logic with modern LLM and embedding techniques, we boost relevance and user satisfaction.

We built a library that places real-world products into artificially generated environments, producing picture-perfect product photos. Unlike many unconstrained AI-generated images that may be eye-catching but lack business utility, our approach algorithmically constrains images using a variety of ControlNets for practical outcomes.

Whether you need consistent branding, contextual backgrounds, or stylized imagery, our system can deliver professional, on-brand visuals every time.

Translating complex business and operational requirements into mathematical constraints can be challenging. Our in-house translation layer software, integrated with popular solvers like CVXOPT, Mosek, and Gurobi, supports both open-source and commercial-grade solvers.

This flexibility allows you to handle everything from small-scale linear optimizations to large-scale number-crunching, enabling an operations framework that is both powerful and efficient.

Our high-performance forecasting engine predicts user access patterns up to 60 days into the future, a critical component of advertising systems that enable companies to book deals in advance.

By combining classical statistical methods with cutting-edge deep learning models, our forecasting library ensures reliability, scalability, and a competitive edge in planning and resource allocation.

Large LLMs can be expensive when we only need specific tasks to be performed. Our fine-tuning library built on LLM Finetuning creates smaller, task-specific LLMs that run efficiently while still delivering high-quality results.

This approach lets you focus on specialized tasks without the cost and complexity of a massive general-purpose model, offering faster insights and better ROI.