Enterprise AI Systems &
Machine Learning Integration
We architect and deploy secure machine learning models and semantic pipelines designed to automate manual data analysis, lower processing latencies, and unlock internal knowledge bases.
AI Architecture Offerings
Retrieval-Augmented Generation (RAG)
Implement semantic database query systems linking LLMs directly to your secure internal knowledge bases, utilizing vector indexing models.
Custom Model Fine-Tuning
Fine-tune open-source models (Mistral, LLaMA) on domain-specific training data to secure proprietary logic and reduce API latency.
Agentic Document Parsing
Construct multi-agent document parsers that extract, structure, and validate terms from complex invoices, PDFs, and legal paperwork.
Automated Claim Policy Parsing for InsuTech Group
We built an AI claim parsing agent utilizing open-source LLMs fine-tuned on custom datasets. The solution reduced audit times from hours to seconds with 98% metadata extraction accuracy.
99.2% Faster
From 3 hours manual audit to 8 seconds query parsing.
Supported Machine Learning Tech Stacks
Python / PyTorch
For model training, pipeline design, and script scheduling.
Pinecone / pgvector
High-performance vector storage supporting semantic queries.
HuggingFace
Open-source foundation models and tokenizer configurations.
OpenAI / Claude APIs
Proprietary advanced LLM reasoning calls and RAG pipelines.