Skip to main content

AI SYSTEMSlive loop

Görkem
Tanağardıgil

AI Systems Engineer

I turn messy institutional knowledge into grounded agents — support, chat, and domain specialists that answer from your data, not from thin air.

Operating model

How an agent actually runs

From knowledge base to a specialized agent — the loop people feel when support, chat, or domain bots stop guessing.

Problem · Answers float free of your docs.

Build the knowledge base

Ingest policies, tickets, manuals, and product data into a RAG store. Chunking, embeddings, and hybrid search so retrieval is specific — not a dump of the whole wiki.

Live trace

running
> ingest: 1,240 docs · policies · runbooks
> chunk + embed · hybrid index ready
> kb: support-core · status: grounded

01

Knowledge that sticks

RAG knowledge bases so agents answer from your corpus — policies, product, ops — instead of inventing it.

02

Agents with a job

Support, chat, and domain specialists on LangGraph: clear routes, tools, and handoffs — not one mega-prompt.

03

Trust under load

Citations, evaluation, and escalation paths so production teams can ship agents without gambling on every reply.

Contact

Open a channel

Roles, collaborations, or agentic product work — same channels as the contact page.