Custom Generative AI Built for Production.
We build generative AI applications connected to your data, your systems, and your workflows — fixed-scope, production-grade, deployed in 2–4 weeks.
Looking for the business view? See How It Works or Solutions.
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.
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.
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 →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.
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 →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.
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 →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.
What do your custom generative AI development services include?
We design, build, and deploy the whole system for you. That covers six areas: RAG systems, AI agents, workflow automations, LLM integrations, voice AI, and MCP servers. Each build connects to your own data and systems, runs on GPT-4o, Claude, Gemini, or an open-source model on your infrastructure, and is fixed-scope with a firm quote before work starts. You'll see a PoC in 2 to 4 weeks and a production system in 6 to 10.
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.
What should you look for in a generative AI development services company?
Three things: production experience, not just demos; a fixed-scope process with a PoC before the full build; and model-agnostic engineering, so you are not locked into one LLM vendor. Ask any provider for a timeline and a firm quote before they touch your data. We give you both: a 2 to 4 week PoC, then 6 to 10 weeks to production, at a fixed price agreed upfront.
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.