We fine-tune, deploy, and integrate large language models on your proprietary data via custom application development, enabling intelligent automation, contextual reasoning, and enterprise-grade reliability at scale.
60%
Faster Response Time
45%
Accuracy Improvement
80%
Query Automation Rate
From fine-tuning proprietary models to building production RAG pipelines through custom application development, we handle every layer of the LLM stack, designed for accuracy, speed, and scale.
By means of customized application development, our team fine-tunes foundation models on your proprietary datasets and delivers models which understand the language, tone, and logic of your industry without hallucinations.
We develop customized applications to create RAG models that will help anchor LLM-generated responses in your trusted knowledge base using live enterprise data sources.
With custom software development, we design multi-stage agent pipelines with LangChain and comparable agent orchestration tools to facilitate self-directed action within integrated systems and APIs.
Fine-tuned models with optimized inference, token efficiency, and latency are deployed with customized application development for reliable performance under heavy enterprise loads.
We offer to host LLMs through custom application development on your cloud infrastructure, keeping your sensitive data inside your own ecosystem while having full control over the model behavior and outputs.
We develop our own application to implement strong evaluations of LLM outputs, such as BLEU and ROUGE metrics, among others task-specific, and ensure continuous improvement of models over time.
Developing an LLM production system goes beyond the selection of a base model. This is because through custom application development, we build the whole pipeline which includes data curation and preprocessing, finetuning using either RLHF or supervised instruction tuning, vector database integration for retrieval, and inference with monitoring.
The systems we develop are built to cater to the unique requirements of your use case: be it legal document analysis, customer service automation, knowledge base lookup, or any other complicated reasoning task.
45%
Accuracy Improvement
60%
Faster Response Time
A research division at UCLA was spending more time looking than researching, manually cross-referencing thousands of academic papers and clinical records. We built a custom LLM trained on their research corpus with Enterprise LLM integration to answer multi-part queries, summarize findings across documents, and respect per-document access permissions for authorized researchers only.
Michael C.
Data Science Director, UCLA
Real deployments across legal, consulting, financial advisory, marketing, and software where LLM-powered systems replaced slow manual processes with accurate, fast, and consistent outputs.
A small US consulting firm was dangerously dependent on one senior consultant who held almost all institutional knowledge. When she was unavailable, junior staff could not answer client questions confidently. We built a RAG system trained on five years of her documented processes, proposals, and client communications that junior staff could query in plain English.
5 yrs
Documents Indexed
↑
Junior Staff Resolution Rate
Staff at a small US financial advisory firm were spending 20 to 30 minutes per query searching an unorganized shared drive for compliance documents and regulatory guidance. We built a RAG-powered search tool that answered compliance questions in plain English, cited the exact source document and version number, and flagged conflicting versions.
<2 min
Query Resolution Time
25 min
Previously Per Query
A US marketing agency was writing every new business proposal from scratch despite most proposals sharing 60 percent of their content. We fine-tuned a lightweight LLM on their best-performing historical proposals and built a simple interface where account managers input a client brief and receive a structured first draft within minutes.
30 min
Draft Time (From 5 hrs)
✓
Win Rate Maintained
A US software company's generic website chatbot was frustrating visitors by failing to answer specific product questions and redirecting them to documentation pages instead. We replaced it with a custom LLM trained on their product documentation, FAQs, and support history, with a confidence threshold that escalated edge cases to a human agent with full context attached.
↓
Support Escalations
↑
Website Conversion Rate
A small US law firm was spending 3 to 4 hours per commercial contract review as junior associates read every word searching for risk clauses, liability caps, and missing terms. We built a fine-tuned LLM trained on the firm's past reviewed contracts and senior attorney annotations that flagged high-risk sections automatically, letting associates focus on flagged areas only.A small US law firm was spending 3 to 4 hours per commercial contract review as junior associates read every word searching for risk clauses, liability caps, and missing terms. We built a fine-tuned LLM trained on the firm's past reviewed contracts and senior attorney annotations that flagged high-risk sections automatically, letting associates focus on flagged areas only.
3.5hr→45min
Review Time Per Contract
↑
Volume Handled Per Week
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