Hire AI Engineers from Kiebot
AI engineers fluent in vector databases, LLM orchestration, and the eval loops that keep AI features safe.
Why hire AI engineers from Kiebot
Kiebot has shipped production AI features for clients across HR, customer support, healthcare, and fintech. Our AI engineers know that "an LLM call" is the easy part. The hard parts: prompt versioning, eval sets, guardrails, observability, graceful degradation, and cost control. That is where we focus.
- LLM-agnostic. OpenAI, Anthropic, Google, and open models in production.
- Eval-driven development with CI gates on quality drift.
- Production RAG pipelines on pgvector, Qdrant, and Pinecone.
- Cost guardrails and graceful degradation patterns baked in.
Common use cases
- AI copilots and conversational agents
- RAG over private documents and code
- Document automation and structured extraction
- AI workflow tools with human-in-the-loop
Frequently asked questions
Do you fine-tune models?+
Rarely. Most teams get more value from RAG and prompt engineering. Fine-tuning makes sense when you need a domain dialect that prompts cannot teach.
How do you measure AI feature quality?+
Eval sets with structured grading, run on every PR. Production samples graded by an LLM judge plus a weekly human spot-check.
How do you control LLM costs?+
Per-tenant token budgets, semantic caching, and small models for routing. Most clients cut their LLM bill 40 to 60% in the first month.