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We build multi-step AI agents that use tools and APIs to finish real workflows, RAG assistants that answer from your private documents and databases, copilots embedded inside products you already run, and the test sets and monitoring that keep them reliable. A typical engagement starts with an AI audit to find where AI saves measurable time or money in your business. It then moves to agent design and a small pilot on real data, followed by human-in-the-loop review design and monitoring after launch, so the result does not turn into a one-off demo that quietly stops being used.

We build mainly on OpenAI, Anthropic Claude, and Google Gemini, and we choose per task by looking at response speed, cost, and how well the model reasons on your material, rather than defaulting to one vendor. LangChain and LangGraph handle the step-by-step logic and state of agents, LlamaIndex and pgvector cover retrieval and embeddings, and Python with Hugging Face comes in when a task needs a custom or open-source model. Because the pieces are separate, you can swap a model or provider later without rebuilding the whole system.

Yes, and most of our AI work is exactly this: putting copilots, assistants, and automation into products that are already live instead of building something new. We begin with a short audit of your product and data to find the use case with the clearest return, then ship that one first as a limited pilot before widening it. You get a working result to judge before committing to a larger plan.

Every system we ship has a test set that scores outputs against real examples before and after each change. RAG answers carry citations so they can be traced to a source document, risky or irreversible decisions go to a person for review, and monitoring tracks accuracy, response time, and cost as they move over time. We treat AI quality as ongoing engineering work, like uptime or security, rather than a checklist you tick once at launch.

It depends on scope. As a rough guide, a Proof of Concept for one agent or RAG use case takes 3-6 weeks from audit to a working demo on real data. Adding a copilot to an existing product, with limits and tests in place, usually takes 8-14 weeks. A full multi-agent system with several integrations, human review, and production monitoring can take 3-6 months. We always start with the smallest version that proves value, then grow it from something people are already using.

Cost follows scope and integration effort far more than model choice. API fees from OpenAI, Anthropic, or Gemini are usually a small part of the total next to engineering time. A focused Proof of Concept for one use case generally starts in the low six figures (THB). A production agent or RAG system connected to your stack, with testing and monitoring, typically costs several times that. We quote a fixed price per phase after a discovery call, and we include the expected ongoing token and API costs so nothing surprises you after launch.

Your data is not used to train public models. We use the enterprise API tiers from OpenAI, Anthropic, and Google, whose terms exclude your data from training. For RAG, sensitive documents stay in a database you control, typically pgvector inside a Postgres instance that is yours, and we set exactly what an agent may read, call, or write before it goes live. If your industry requires it, we can design a deployment that runs on your own servers (on-premise) or inside an isolated VPC.

You do. Custom code, agent configurations, prompt templates, evaluation datasets, and fine-tuned model weights where they apply all transfer to you on final payment, with no lock-in to tools that only Haliviq has. We recommend building inside your own cloud account and GitHub organisation for anything headed to production, so you can see and control everything from day one and carry on without us if you choose.