Question 1

What LLMs does Hestur AI use?

Accepted Answer

Hestur AI uses GPT-4.1 and o3 (OpenAI), Claude Sonnet 4.6 and Claude Opus 4.7 (Anthropic), and Gemini 2.5 Pro (Google) depending on the use case. Model selection is driven by task requirements: GPT-4.1 for instruction-following and coding tasks, o3 for complex multi-step reasoning, Claude Sonnet 4.6 for long-context document processing, and Gemini 2.5 Pro for multimodal tasks. All builds are model-agnostic — models can be swapped without re-architecting the system.

Question 2

What is LangChain and why does Hestur AI use it?

Accepted Answer

LangChain and LangGraph are orchestration frameworks that let AI agents reason across multiple steps, call external tools, maintain state, and coordinate with other agents. Hestur AI uses LangGraph specifically for multi-agent workflows where different agents handle different tasks in sequence or in parallel — for example, one agent extracts data from a document while another validates it against a database.

Question 3

What vector databases does Hestur AI use for RAG systems?

Accepted Answer

Hestur AI builds RAG systems on Pinecone, Weaviate, Qdrant, and pgvector (Postgres extension) depending on scale, existing infrastructure, and cost requirements. Pinecone is preferred for high-scale managed deployments. Weaviate is used when multi-modal or graph-based retrieval is needed. pgvector suits teams already on Postgres who want to minimise infrastructure overhead.

Question 4

What platforms does Hestur AI use for voice AI?

Accepted Answer

Voice AI agents are built on VAPI and LiveKit. VAPI provides a managed telephony and LLM orchestration layer with low-latency voice pipelines. LiveKit is used for real-time audio applications requiring WebRTC-level control. Text-to-speech uses ElevenLabs or Azure Neural TTS. Speech-to-text uses Whisper (OpenAI) or Deepgram for production-grade transcription accuracy.

Question 5

What infrastructure does Hestur AI deploy on?

Accepted Answer

Hestur AI deploys on AWS, GCP, Azure, and Vercel depending on client requirements. Most production systems run on AWS (Lambda, ECS, or EC2 depending on workload). Hestur AI uses Supabase for real-time database needs and implements Infrastructure-as-Code (Terraform) for all production deployments. 99.9% uptime SLA is available.

Question 6

Does Hestur AI build with open-source or proprietary models?

Accepted Answer

Both. Hestur AI uses proprietary frontier models (GPT-4.1, Claude Sonnet 4.6, Gemini 2.5 Pro) for most production use cases due to performance and reliability. For clients with data residency, air-gap, or cost requirements, Hestur AI deploys open-source models (Llama 4 Scout/Maverick, Mistral, Phi-4) on private infrastructure. The system architecture is always model-agnostic to allow future switching.

Production-Ready AI Technology Stack.

Performance dashboard.

Our technology stack.

OpenAI GPT-4.1 / o3

Anthropic Claude Sonnet 4.6

Google Gemini 2.5 Pro

LangChain

LangGraph

LangSmith

Security & compliance.

SOC2 Type II

HIPAA Ready

GDPR Compliant

E2E Encryption

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