Should I use OpenAI's API or fine-tune an open-source model?

It depends on your data sensitivity and cost structure. OpenAI's GPT-4 is ideal for general-purpose tasks with fast time-to-market. For regulated industries (healthcare, finance) or high-volume use cases, we recommend fine-tuning open-source models like Llama 3 or Mistral on your own infrastructure for full data sovereignty and lower per-token costs.

How do you ensure LLM outputs are accurate and not hallucinated?

We implement multiple guardrails: RAG with citation verification, structured output validation, confidence scoring, and human-in-the-loop review for high-stakes decisions. Our systems include automated evaluation pipelines that continuously test output quality against golden datasets.

🧠 Large Language Models

LLM Integration Services

Q: What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that connects a Large Language Model to your proprietary data sources (documents, databases, wikis). When a user asks a question, the system first retrieves the most relevant information from your data, then feeds it to the LLM as context — producing answers that are grounded in your actual business knowledge, not hallucinated.

Connect the world's most powerful language models to your proprietary data. We build RAG pipelines, fine-tune open-source models, and deploy production-grade LLM systems that transform how your enterprise operates.

🔧 Core Capabilities

End-to-End LLM Engineering

📚

RAG Pipelines

Connect LLMs to your documents, databases, and knowledge bases. Our RAG systems use vector databases (Pinecone, Weaviate, pgvector) to retrieve the most relevant context before generating answers — eliminating hallucinations.

🔬

Model Fine-Tuning

Fine-tune Llama 3, Mistral, or GPT models on your proprietary data using techniques like LoRA and QLoRA. Achieve domain-specific accuracy at a fraction of the cost of larger general-purpose models.

🎯

Prompt Engineering

Systematic prompt design that maximizes output quality. We build prompt templates, chain-of-thought pipelines, and few-shot learning systems that consistently produce reliable, structured outputs.

🔌

API Integration

Seamless integration of OpenAI, Anthropic, Google Gemini, and Azure OpenAI APIs into your existing tech stack. Includes rate limiting, fallback chains, and cost monitoring dashboards.

💬

Custom Chatbots

Intelligent conversational interfaces trained on your data. From internal knowledge bots for HR and IT, to customer-facing support agents that resolve 85% of queries without human escalation.

🛡️

Guardrails & Safety

Content filtering, PII detection, output validation, and compliance frameworks. We ensure your LLM systems are safe, auditable, and aligned with industry regulations (HIPAA, SOC2, GDPR).

🏗️ Models We Deploy

From Proprietary to Open Source

We are model-agnostic. We select the best model for your use case based on accuracy, latency, cost, and data privacy requirements.

🔒 Proprietary Models

GPT-4o / GPT-4 Turbo — Best-in-class reasoning for complex tasks.
Claude 3.5 Sonnet — Exceptional at long-document analysis and coding.
Google Gemini 2.5 — Multimodal capabilities across text, image, and video.

🔓 Open-Source Models

Llama 3 (70B / 8B) — Meta's flagship model, ideal for on-premise deployments.
Mistral Large — Fast inference with strong multilingual support.
Phi-3 — Microsoft's compact model for edge and mobile deployments.

❓ FAQ

Frequently Asked Questions

What is RAG (Retrieval-Augmented Generation)?+

RAG connects a Large Language Model to your proprietary data sources. When a user asks a question, the system first retrieves the most relevant information from your data, then feeds it to the LLM as context — producing answers grounded in your actual business knowledge, not hallucinated.

Should I use OpenAI or fine-tune an open-source model?+

It depends on your data sensitivity and cost structure. OpenAI's GPT-4 is ideal for general-purpose tasks with fast time-to-market. For regulated industries or high-volume use cases, we recommend fine-tuning Llama 3 or Mistral on your own infrastructure for full data sovereignty and lower per-token costs.

How do you prevent hallucinations?+

We implement multiple guardrails: RAG with citation verification, structured output validation, confidence scoring, and human-in-the-loop review. Our systems include automated evaluation pipelines that continuously test output quality against golden datasets.

Can you integrate LLMs with our existing CRM / ERP?+

Yes. We specialize in connecting LLMs to existing enterprise systems — Salesforce, SAP, HubSpot, Jira, Confluence, and custom databases. The LLM acts as an intelligent interface layer that can read, summarize, and act on data across your entire stack.