Generative AI & Large Language Models (LLM)
Transform How You Create, Communicate, and Automate. We leverage open-source and proprietary models—including LLaMA, RAG, and Stable Diffusion—to build customized solutions that generate new designs, automate complex document processing, and provide human-like summarization and classification. Our expertise transforms unstructured data into actionable intelligence, empowering your organization to achieve unprecedented efficiency and innovation.
document automation.
with synthetic data.
Hear from Our Projects
Real-world LLM and Generative AI case studies across industries.
Diagnostic/Medical Summary (Healthcare)
Challenge: A Major Healthcare Provider struggled with time-consuming, fragmented documentation workflows across their EMR system, delaying care and increasing clinician cognitive load.
Solution: An intelligent, RAG-enabled documentation platform was deployed as a seamless EMR plug-in. It pulls patient data in real-time via FHIR APIs, generating concise, structured summaries of the patient’s journey with verified citations back to source documents.
Key Impact Metrics
80%
35%
99.9%
Generative AI – Fashion Design Generation (Retail)
Challenge: A Major Fashion Retailer needed a method to rapidly produce a high volume of new, trending, and customized fashion designs to accelerate the product design cycle and minimize pre-production costs.
Solution: A "virtual design studio" was created using Stable Diffusion and specialized LoRA (Low-Rank Adaptation) models, trained on the retailer's aesthetic. Designers use text prompts to generate high-fidelity images of new outfits and virtual models.
Key Impact Metrics
10x
60%
40%
Client Project - Resume Extraction (HR / Recruitment)
Challenge: A Large Nurse Practitioner Matching Firm faced a massive administrative bottleneck in manually extracting applicant data from resumes submitted in various formats (PDF, DOCX, scanned images).
Solution: An automated pipeline uses LLaMA 3.1 to intelligently parse text from diverse file formats. The LLM extracts required entities (skills, experience), automatically structuring the details into JSON for direct database record creation.
Key Impact Metrics
95%
5x
70%
Client Project - Medical Equipment Faults (Manufacturing)
Challenge: A Global Medical Equipment Manufacturer lacked sufficient historical, labeled data to train a traditional model to automate the classification of technical complaints into specific, predefined codes.
Solution: We utilized LLaMA 3.1 to generate a high-quality, synthetic training dataset, then fine-tuned the LLM specifically for the manufacturer's classification schema. The deployed model provides automated, real-time complaint code predictions.
Key Impact Metrics
95%
7x
40%
Neural Voice Synthesis (Media & Content)
Challenge: A Global Digital Content Provider faced high production costs and slow turnaround times for creating multilingual voice-over content that lacked the flexibility for instant updates.
Solution: A custom, multilingual Text-to-Speech (TTS) system was built using Tacotron 2 and the WaveNet vocoder. The model synthesizes expressive, human-like speech in four target languages from any written script.
Key Impact Metrics
90%
MOS
Related RAG & LLM Expertise
Custom RAG Implementation
We design and deploy Retrieval-Augmented Generation systems to ground LLM outputs in your private, internal documents (e.g., technical manuals, compliance records), ensuring factual accuracy and cite-ability.
Zero-Shot LLM Fine-Tuning
Specializing open-source models like LLaMA for high-accuracy, custom tasks such as text classification and entity extraction, even with limited historical data via synthetic data generation.
Secure API Integration
Expertise in connecting LLM solutions with secure enterprise systems like EMR (via FHIR APIs) and internal databases (MySQL) for seamless data retrieval and output.
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