Generative AI Development
We architect and develop production-grade generative AI applications powered by state-of-the-art foundation models (GPT-4o, Claude 3.5, Llama 3). We move beyond simple API wrappers to build robust, secure, and highly specific LLM systems that execute complex business logic securely.
Core Features
Advanced RAG Pipelines
Context-aware systems that securely retrieve your enterprise data to ground LLM responses and eliminate hallucinations.
LLM Guardrails & Security
Implementation of NeMo Guardrails and custom validation layers to ensure brand-safe, compliant, and deterministic AI outputs.
Multi-Model Orchestration
Routing queries dynamically between Claude for reasoning, GPT-4o for speed, and local open-source models for sensitive data.
Agentic Workflows
Developing AI agents capable of planning, utilizing tools (APIs, databases), and executing multi-step reasoning.
Our Process
Architecture & Model Selection
Week 1-2Evaluating foundation models, defining the data retrieval strategy (RAG vs Fine-tuning), and scoping security guardrails.
Data Pipeline & Vector DB Setup
Week 3-4Ingesting, chunking, and embedding unstructured enterprise data into high-performance vector databases.
LLM Orchestration & Prompting
Week 5-6Developing the core reasoning logic, tool calling capabilities, and system prompts using LangChain or LlamaIndex.
Evaluation & Red Teaming
Week 7Rigorous testing of the generative AI system against adversarial prompts and measuring output quality using LLM-as-a-judge.
Deployment & Observability
Week 8Deploying the system to production with comprehensive observability to monitor token usage, latency, and drift.
Technologies We Use
FAQ
How do you prevent the AI from hallucinating?
Is our company data used to train public AI models?
Do you use LangChain, or do you build custom orchestration?
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