AI agents and voice AI
An AI agent uses a language model to decide which steps and tools to use for a goal. The hard part is not the model: it is grounding answers in your data, giving the agent memory, and putting guardrails around what it can do. We build agents with retrieval and knowledge-graph memory, and voice agents with memory and calendar sync.
What people ask us about this
Where we do this work
- ServiceAI voice agentsLow-latency voice agents with memory and calendar sync.Learn more
- ServiceConversational AI with memoryA GraphRAG assistant that remembers.Learn more
- Our productJustInterview.aiAI technical screening and video interviewingSee the product
- Our productAether VoiceConversational voice AI agentsSee the product
- Our productGeolyticsSEO + AI-search visibility platformSee the product
Where it has shipped
- Artificial Intelligence & Agentic SystemsA US Conversational AI VentureEngineering a production-grade multimodal conversational AI engine powered by n8n orchestration, LangChain, and GraphRAG knowledge graphs with Apache AGE and pgvector.$10KCommercial Sprint
- Electronics & OEM ManufacturingAn Electronics & OEM Hardware Manufacturing EnterpriseAutomating Purchase Order (PO) to Sales Order (SO) conversion, national GST e-Invoicing/e-Way bill generation, and intelligent tender document assistance.
Read more on this
RAG vs GraphRAG: when a knowledge graph is worth it
Plain RAG is enough for most question-answering over documents. GraphRAG earns its extra cost when answers depend on relationships between entities and multi-hop reasoning.
"Types of AI agents and when each one fits"
From rule-based bots to tool-using, retrieval-grounded, planning, multi-agent and voice agents, here is how each type works, what it costs you, and how to pick the simplest one that works.
Vector Database Evolution: Production RAG Beyond Embeddings
A technical look at scaling RAG beyond simple embeddings. We explore hybrid search, metadata filtering, and when pgvector suffices for production.
Terms explained
- AI agentSoftware that uses a language model to decide which steps and tools to use to reach a goal, instead of following a fixed script.
- Agentic AIAI systems that plan, act through tools and check the result over several steps with limited human input. The term covers single agents and teams of agents working together.
- LLM (large language model)A neural network trained on large amounts of text that can read and write language, answer questions and write code. GPT, Claude and Gemini are examples.
- RAG (retrieval-augmented generation)Gives a language model the most relevant documents at question time, so its answers are grounded in your own data and can cite it.
- GraphRAGA form of RAG that also stores the relationships between entities in a knowledge graph, so the system can answer questions that need several linked facts.
- Vector databaseStores embeddings and finds the closest matches by meaning rather than keyword. Many teams use a vector extension in the database they already run.
- EmbeddingsLists of numbers that represent the meaning of text, images or other data, so similar items sit close together and can be found by similarity search.
- Prompt engineeringWriting and testing the instructions, examples and context given to a language model so it returns reliable output for a task.
- HallucinationWhen a language model states something false with confidence. Grounding answers in sources and checking outputs reduces it, but does not remove it.
- Fine-tuningFurther training a pre-trained model on your own examples so it follows a style or task more consistently. Better prompting or retrieval is often cheaper and enough first.