Haink builds production LLM applications and retrieval-augmented generation (RAG) systems — chat assistants, copilots, support bots and automation grounded in your own data. A senior-only team handles model selection, retrieval, evaluation and guardrails, and can deploy the whole stack privately on infrastructure we supply.
Retrieval over your documents and databases so answers are grounded, current and cited — not hallucinated. Hybrid search, re-ranking and chunking tuned to your corpus.
Tool-using agents and copilots that take actions in your systems — drafting, lookups, multi-step workflows — with human-in-the-loop controls where it matters.
LLM support bots trained on your knowledge base that resolve the majority of tickets without a human and escalate cleanly when needed.
Generation of memos, reports and structured output from raw source material, with templates, style control and review steps.
Offline and online eval suites, hallucination and safety checks, prompt-injection defenses and monitoring — so quality is measured, not assumed.
Open-weight models served on your own GPUs (vLLM/TGI) for data that cannot leave the network — sized to the hardware we quote in the same proposal.
Typical stack:
Production systems delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
Four LLM products including automated visa memorandum drafting from raw document sets (80% of routine drafting automated), case-manager workflow optimization, client-chat SLA monitoring and an FAQ assistant.
An LLM support assistant trained on the platform's knowledge base, resolving three quarters of incoming tickets without a human and reducing support load by half.
Retrieval-augmented generation grounds an LLM in your own documents and data at query time, so answers are accurate, current and citable. It is the right approach when you need the model to reason over private or frequently-changing knowledge without retraining it.
Both. We pick per project based on accuracy, cost, latency and data-residency needs — proprietary APIs where they win, open-weight models (Llama, Qwen and others) when you need private, on-premises deployment or tighter cost control.
Yes. We deploy open-weight models on your own GPUs so sensitive data never leaves your network, and quote the right-sized GPU hardware in the same proposal.
Grounding via retrieval with citations, structured output validation, evaluation suites that measure accuracy on your data, and guardrails for safety and prompt-injection — quality is measured continuously, not assumed.
Most projects reach first working results in 2–4 weeks after a short discovery phase, then iterate to production with CI/CD, evals and observability in place.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap — with right-sized GPU hardware if AI workloads are involved.