Beyond Chatbots.
Intelligent Systems.
We move beyond simple "chat with PDF" demos to build production-grade RAG pipelines and multi-agent swarms that execute complex workflows autonomously.
From Novelty to Utility
Most GenAI projects fail because they lack structure. We engineer systems with memory, tools, and guardrails to ensure reliability and safety.
Advanced RAG
We implement hybrid search (vector + keyword), re-ranking, and knowledge graphs to ensure your agents always have the right context.
Multi-Agent Swarms
Decompose complex tasks into sub-problems handled by specialized agents (Researcher, Coder, Reviewer) that collaborate to solve them.
Tool Use & Actions
Our agents don't just talk; they act. We connect LLMs to your APIs, databases, and internal tools to automate real work.
Orchestration Architecture
We build on battle-tested frameworks to ensure your GenAI applications are scalable, observable, and maintainable.
Orchestration
LangChain, LangGraph, and AutoGen for complex agent workflows.
Vector Stores
Pinecone, Weaviate, or Qdrant for semantic memory.
Observability
LangSmith and Arize Phoenix for tracing and debugging agent thoughts.
Inference
vLLM and TGI for high-throughput token generation.
GenAI Stack
Common Questions
How do you prevent hallucinations?
We use a multi-layered approach: RAG for grounding in your data, self-reflection steps where the model critiques its own output, and deterministic guardrails to filter unsafe or incorrect responses.
Is my data safe with GenAI?
Yes. We can deploy entirely private instances of open-source models (like Llama 3) within your own cloud environment (AWS/Azure/GCP), ensuring no data ever leaves your control.
What is the difference between a chatbot and an agent?
A chatbot answers questions. An agent pursues goals. Agents can plan, use tools (like a calculator or API), and iterate until they solve a problem, whereas chatbots are passive responders.
Build Intelligent Systems
Don't settle for a demo. Build a GenAI system that drives real business value.