Featured solution

AI Agents

Built correctly, an AI agent is the most patient employee an institution has. We build agents with engineering discipline: measurable, traceable and clearly bounded.

The most common mistake in agent projects is trusting the model's capability and skipping the engineering. We do the opposite: from the outset we define which tools the agent may access, when it must hand over to a human, and how every step is recorded.

Source attribution is mandatory for agents working with enterprise data. In our RAG architectures every answer links back to the document it rests on; no output is produced that cannot be verified.

LLM AgentsRAGTool UseVector DB

Capabilities

What we do in this area

RAG architectures

Knowledge retrieval systems that answer from an institution's own documents, cite their sources and stay up to date.

Multi-agent systems

Agent architectures that break a task into sub-tasks and review one another.

Tool use

Secure agent access to enterprise systems; permission-limited operations, fully logged.

Evaluation (eval)

Automated measurement of agent output; catching quality regressions when a version changes.

Observability

Tracking of every step, every tool call and every cost.

Vector database

Embedding generation, indexing and refresh pipelines.

Internal document and regulation assistant
Application pre-screening and classification
Summarising and comparing long technical documents
Automatic routing of call and request records

Our approach

Why Türkol?

The multi-agent legal document architecture we built with Legaleye AI runs end to end on our own engineering — from prompt design to the vector database and observability.