We build custom LLM products, enterprise copilots, knowledge companions, and AI automation systems on top of GPT-4o, Claude 3.5, Llama 3, and Gemini — with RAG, guardrails, and LLMOps baked in from day one.
The Problem
Teams build impressive demos — a ChatGPT wrapper, a basic RAG prototype — but they stall before enterprise deployment. The challenges are real:
Our Solution
We bridge the POC-to-production gap with enterprise engineering practices applied to LLM systems:
What We Build
Custom AI assistants branded to your organization — customer service bots, internal knowledge assistants, HR copilots, and sales enablement tools.
Turn your documents, databases, and APIs into queryable knowledge — with precise retrieval, source citations, and hallucination-resistant answers.
LLM-powered automation that replaces repetitive knowledge work — document processing, report generation, email drafting, and data extraction.
Customize open-source models (Llama 3, Mistral, Gemma) for your domain with LoRA, QLoRA, and supervised fine-tuning — with evaluation suites to validate improvements.
Systems that process text, images, audio, and documents together — medical reports, invoice extraction, product image analysis, and audio summarization.
Production-grade LLM infrastructure — prompt registries, automated testing, cost monitoring, canary deployments, and multi-model orchestration.
Architecture
A typical enterprise Generative AI deployment includes these layers:
Architecture Diagram Placeholder
User Interface → API Gateway → LLM Orchestration → Retrieval → Vector DB → LLM → Guardrails → Response
LLM Models
Frameworks
Vector Stores
Use Cases
Regulatory document summarization, AI-powered financial advisor assistants, automated report generation, and fraud narrative generation for compliance.
Clinical note summarization, medical literature Q&A, patient intake automation, drug information assistants, and EHR data extraction.
Contract review and clause extraction, legal research acceleration, case summarization, and due diligence automation with citation grounding.
Product description generation, AI-powered customer support, personalized recommendations with explanation, and review summarization.
Technical documentation Q&A, maintenance log analysis, supply chain risk narrative, and quality inspection report generation.
Internal knowledge management, HR policy assistants, IT helpdesk automation, executive briefing generation, and meeting intelligence.
Business Benefits
Every GenAI system we build is tied to concrete business KPIs from day one.
FAQ
We're model-agnostic and select based on your requirements. We work with OpenAI (GPT-4o, GPT-4-turbo), Anthropic (Claude 3.5 Sonnet/Haiku), Meta (Llama 3 70B/8B), Google (Gemini Pro), Mistral, and custom fine-tuned models on Hugging Face. For cost-sensitive workloads, we implement intelligent model routing.
We address hallucinations at multiple levels: (1) RAG grounding to tie responses to verified sources with citations, (2) automated evaluation pipelines using G-Eval and custom metrics, (3) structured output schemas to constrain LLM responses, (4) confidence scoring and abstention for low-confidence queries, and (5) human-in-the-loop review for high-stakes decisions.
We implement enterprise security standards: VPC-deployed models (no data leaves your cloud), PII detection and redaction before LLM calls, Azure OpenAI or AWS Bedrock for enterprise data privacy guarantees, audit logging of all LLM interactions, and RBAC for access control. We can work with air-gapped environments using local model deployments.
A focused POC is typically 2–3 weeks. A production-ready MVP (with RAG, evaluation, basic monitoring) takes 6–8 weeks. Full enterprise deployment with LLMOps, custom fine-tuning, and integrations is 10–14 weeks. We use sprints to deliver working software incrementally throughout.
Ready to build?
Book a free 30-minute consultation to discuss your use case, data assets, and what a production-ready GenAI system would look like for your business.