Generative AI Software Development

Generating realistic electronic medical records (EMRs)/medical health records (MHRs) for further medical simulations or personnel training without privacy concerns.

Processing vast amounts of information to extract meaningful or most essential parts. This help with healthcare automation, significantly decreases manual work for better and faster decision-making or monitoring and allows the automatic generation of medical reports.

Creating LLM-driven AI chatbot assistants for various fields, from patient-doctor collaboration to internal process helpers, to improve patient engagement and early/fast 24/7 assessments, reduce the administrative burden, and improve planning.

Improving data quality with signal enhancement (in noisy images/scans), removing noisy elements, or generating super-resolution objects for more precise decision-making and explanation.

Generating compounds or codon sequences for preclinical research (lab-in-the-loop) based on known elasticity, environment, and targets, focused on cost optimisation and broader variety coverage.

Speeding up the research process and providing safer testing possibilities by generating combinations of factors and exciters for further simulations, considering need, distributions, and value areas.

Optimising goods/service features by generating promotional marketing descriptions and compelling designs based on current trends to increase consumer utility, competitive advantage, and average basket size.

Enhancing customer segment features and controls for further simulations and more precise campaigns that increase overall customer engagement and SKU turnover.

AI in marketing can help generate messages for efficient ads and accelerate the promotional cycle for increased conversion rates and brand awareness.

Providing secure and tailored communications, helping customers make better choices by offering best-fit goods and services. These recommendations can be further improved according to collected conversations, resulting in increased customer loyalty.

Building a chain of LLM-based agents that automates internal processes from ordering and communication to hiring processes, which significantly decreases operating costs.

Matching data based on the semantic understanding of statements, reports, and receivables/payables structure and comparing consolidation with reported data. This helps finance automation, allowing you to speed up operations and streamline credit decision-making while minimising human errors.

Facilitating customer communication for better engagement and up/cross-selling possibilities while taking into account historical information and recent trends.

Automated Gen AI module that understands current policies and regulatory updates, with the possibility of creating reports. The result is a reduced risk of non-compliance and corresponding penalties.

Monitoring and evaluating markets with an LLM-based agent, which recommends where to invest and helps to achieve higher ESG scores and profitability, identifying new possibilities and innovative niches, like green finance.

Built on top of an analytical system, LLM-based agents can gather additional market data and expert opinions to generate advanced market overviews and recommendations, helping financial institutions improve their tactical and strategic vision.

Improving media and content generation based on the most popular topics or even terms, taking into account the user's preferences and promotion plans.

Reducing post-production time, based on multimodal (text2image, text2video) generative models, with lower skill barriers and the possibility to easily repurpose raw materials.

Realistic computer-generated imagery (CGI), text, sound, video, and AI-generated art enable fast prototyping and decrease time-to-market for new products, especially in marketing, gaming, etc.

Increasing localisation efficiency, with natural language processing (NLP) tools that understand contextual grammar, idioms, phrasemes, and other language richness techniques.

Using SOTA LLMs for dynamic changes in the marketing/website/app appearance, including built-in ads generation, which results in higher CTR and improvement of other essential metrics.

Reducing processing time and operational costs with automated information retrieval and accident assessment reports, automated information routing, and managers' assignments.

Instruct-based LLMs for insured object forms and data analysis, mapping inputs with policies to prevent risk exposure and decrease human errors (fraud) in risk evaluation.

Building an LLM-based recommender system with the functionality of mixing the possibilities and limitations from the underwriting policies with the recognised consumption and need patterns to improve customer retention.

Uncovering risk patterns from the insured features and adjusting premiums dynamically, based on customer conversation, and considering traditional risk parameters.

Improving customer engagement in product selling and claims management (reporting) processes, using secure and verified adapters on top of foundational LLMs to reduce customer service costs by providing 24/7 support.

Building conversational interfaces on top of existing ERP data, DWHs, dashboards, and predictive analytics modules for faster decision-making and effective weak signal management.

A WMS (warehouse management system) that accommodates information from multiple sources and facilitates process control, providing suggested actions for employees in real time. This tool won't require specific skills and can include a natural language interface (custom LLM) to provide additional automation and decrease human error.

Retrieving and summarising information from various customs forms, shipment documents, invoices and other unstructured documents to speed up operations and reduce manual work.

Based on the capacity planning system, automatically generating communication templates, emails, orders, and reports, while assisting customer support with shipment details. AI chatbots can decrease operating costs and response latency.

Automatically monitoring public data concerning weather, prices, trends, and contractors' features as well as analysing new competitors to suggest fast and efficient decisions or strategy adjustments.

Automatically generate alternatives for design, including materials or printing recommendations, that help to shorten the design cycle, including innovations for product lifespan and reliability improvements.

Using a configured chain of LLM-based agents to connect different operations (from communication to ordering or planning) and facilitate responses and decision-making to optimise operational cycles, prevent mistakes, and increase internal efficiency.

Extending existing datasets with AI-generated samples, generating artificial environments or combining any factors (elasticity-based), for faster in silico simulations, testing and further ML model training.

Generating appearances or dynamic UI for seamless remote manipulations or virtual workforce training, allowing you to reduce human mistakes, introduce automation, and improve knowledge retention.

Enriching existing prototype datasets with AI-generated objects and meshes based on existing requirements to reduce the time for modelling and prototyping and ensure 3D object compliance and standardisation.

Automatically generate multiple interior design concepts based on proposed sketches, brand strategies and ergonomic/utility principles to reduce design time and propose innovative, creative solutions.

Creating realistic and diverse data and scenarios to train and test autonomous driving models, improving model robustness and safety, and reducing real-world testing efforts.

Using LLM-based agents for automated contractors' information search, filtering and ranking based on usefulness and predefined conditions. This allows businesses to facilitate negotiations and RFQ preparation to reach higher internal efficiency.

Speeding up design and prototyping, considering exogenous risks (seismic, human mistakes) and cost efficiency (location, size, i/o, etc.) with modern multimodal approaches.

Automating the procurement process, including negotiations and invoicing, with SOTA-secured LLMs to speed up operational activities and decrease idle time and delays due to human mistakes.

Utilising fine-tuned LLMs for incident scenario generation, safety procedures, and responses to automate and enhance HAZOP studies.

Generating different reports based on analytical modules and regulatory sources to reduce SME involvement and enhance public relations.