Hestur AIHestur
    Generative AI DevelopmentPoC in 2–4 weeks

    Custom Generative AI Built for Production.

    We build generative AI applications that work in your actual environment — connected to your data, your systems, and your workflows. RAG, agents, voice AI, workflow automation, and LLM integrations. Fixed-scope, production-grade.

    Start Your PoCNot sure what to build? See AI consulting →

    How it's built

    A stack built around your data — not a generic chatbot wrapper.

    Every generative AI system we build follows the same layered architecture. Your existing data and systems stay where they are. We build the ingestion, retrieval, orchestration, and application layers on top — model-agnostic, production-hardened.

    Generative AI solution architectureFive-layer stack from bottom to top: Your Data and Systems, Ingestion and Retrieval, LLM Model Layer, Orchestration and Tools, Application UI.Application / UIWeb app, chat widget, API endpoint, voice interfaceOrchestration & ToolsLangGraph, n8n, tool calling, memory, guardrailsLLM / Model LayerGPT-4o, Claude Sonnet, Gemini 2.5, Llama 4 (model-agnostic)Ingestion & RetrievalChunking, embeddings, hybrid search, reranking (Pinecone / Weaviate)Your Data & SystemsDocs, CRM, databases, APIs, ERP, knowledge base

    Capabilities

    Six generative AI development areas.

    Every project starts as a 2–4 week PoC on one of these capability areas. Full production builds follow after validation.

    Generative AI development capability mapSix capability areas: RAG Systems, AI Agents, Workflow Automations, LLM Integrations, Voice AI, and MCP Servers.RAG SystemsPrivate data Q&A, doc search, knowledge basesAI AgentsMulti-step reasoning, tool use, autonomous workflowsWorkflow AutomationsDocument processing, triage, data entry, routingLLM IntegrationsEmbed AI into existing products via API or pluginVoice AIInbound call handling, booking, CRM updatesMCP ServersGive AI agents access to your tools — RBAC & audit logs
    RAG Systems

    Knowledge base and document AI

    Private-data Q&A systems that answer questions from your documents with source citations. Hybrid retrieval, 90–95% accuracy, sub-200ms response. Supports PDFs, Word, Notion, Confluence, SharePoint, databases.

    Learn more →
    AI Agents

    Multi-step autonomous agents

    Agents that reason, use tools, and complete multi-step tasks without human input. Built on LangGraph. Connected to your CRM, databases, APIs, and email — not just a chat window.

    Workflow Automation

    Document processing and workflow AI

    Invoice extraction, contract review, support ticket triage, lead scoring, compliance checking. Structured data out of unstructured inputs, automated into your existing systems.

    Learn more →
    LLM Integration

    AI embedded in your product

    Add generative AI capabilities to your existing SaaS, mobile app, or enterprise platform via API. Streaming responses, function calling, tool use, user-level memory — production-grade, not a prototype.

    Voice AI

    Conversational voice agents

    AI that answers calls, understands natural language, books appointments, and updates your CRM — without hold music or scripted menus. Sub-400ms latency, 20+ languages.

    Learn more →
    MCP Servers

    Tool access for AI agents

    Model Context Protocol servers that expose your CRM, databases, Jira, and internal systems to AI agents — with RBAC, OAuth, and audit logging. Claude, ChatGPT, and Cursor-compatible.

    Learn more →

    Delivery

    Fixed-scope. No retainers. No billing surprises.

    01

    PoC (2–4 weeks)

    Working prototype on your real data. Accuracy metrics, integration validation, go/no-go recommendation. Fixed price before we start.

    02

    Production build (6–10 weeks)

    Full deployment: monitoring, error handling, documentation, handoff. Production-hardened — not MVP-grade code pushed to prod.

    03

    You own everything

    Code, infrastructure, and model configuration. No vendor lock-in to Hestur. We document everything so your team can maintain and extend it.

    FAQ

    Common questions.

    What is generative AI development?

    Generative AI development means building production applications on top of large language models (LLMs) like GPT-4o, Claude, or Gemini. This includes RAG systems, AI agents, workflow automations, voice agents, and LLM integrations — anything that uses foundation model capabilities to solve a real business problem.

    How long does a generative AI project take?

    A proof of concept on one use case: 2–4 weeks. A production-grade single-system deployment: 6–10 weeks. Multi-system enterprise deployments with compliance requirements: 10–16 weeks. Every engagement is fixed-scope with a firm quote before work starts.

    Do you work with open-source models or only OpenAI/Anthropic?

    Both. We build on GPT-4o, Claude, and Gemini for most production deployments. For use cases requiring data residency, self-hosted inference, or cost optimisation at scale, we use Llama 4, Mistral, or other open-source models deployed on your infrastructure.

    How much does generative AI development cost?

    PoC: $10K–$25K. Single-workflow production build: $25K–$80K. Multi-system enterprise deployment: $100K–$300K+. Running costs depend on LLM API usage, infrastructure, and scale. We model the full cost — build and running — before you commit.

    Do you build the AI or use off-the-shelf tools?

    Neither extreme. We don't fine-tune foundation models from scratch (not needed for 95% of business use cases). And we don't just wire together no-code tools. We build custom application logic — retrieval pipelines, agent orchestration, tool integrations — on top of foundation models using the right mix of frameworks.

    Start with a PoC — see results in 2–4 weeks.

    Pick one use case. We build a working prototype on your real data. Fixed scope, fixed price, go/no-go decision at the end.

    Book a PoC CallSee pricing →