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Behelitai Engineering

Engineering Production AI Systems

Start with real delivery work and technical field notes to understand how Behelitai ships enterprise AI systems.

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Case Studies

From Company Research to Market Intelligence at Scale

#Company Intelligence•September 14, 2026

How Behelitai designed a company intelligence system that turns fragmented public data into a continuously maintained asset for market mapping, commercial discovery and company monitoring.

Enterprise Interview Agent System for Structured Talent Evaluation

#HR Tech•June 06, 2026

Behelitai built a FastAPI and LangGraph based AI Agent platform that standardizes candidate profiling, competency assessment, follow-up questioning, and management reporting for enterprise hiring workflows.

How a Legal Team Cut Contract Review Time by 65%

#Legal Tech•April 12, 2026

Behelitai built a secure retrieval pipeline with strict document isolation and human sign-off controls to speed up review without compromising compliance.

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Knowledge Base

Securing Enterprise LLM Workflows Against Prompt Injection

#Security•March 28, 2026

Practical architecture patterns for input hardening, contextual boundary checks, and adversarial test loops in production-facing AI systems.

When Not to Fine-Tune: A Practical Architecture Decision Guide

#Strategy•February 22, 2026

A framework to decide between prompting, retrieval, and fine-tuning based on latency, governance requirements, and cost constraints.

Open Source vs Proprietary Models: Deployment Trade-Offs in 2026

#Benchmarking•February 05, 2026

How teams evaluate reliability, unit economics, and operational complexity when choosing between hosted and self-managed model stacks.

Open Knowledge Base

Enterprise AI: common questions

What does Behelitai build?

Behelitai designs and builds enterprise AI agents, knowledge systems and workflow automation. Published implementations cover structured interviews, contract review, support triage, operations reporting and controlled content generation.

Explore implementation examples

How do AI agents use enterprise knowledge?

Retrieval-augmented generation (RAG) supplies relevant documents to a model. An agent coordinates workflow steps and tools. Behelitai’s interview system combines role-knowledge retrieval with explicit assessment state, follow-up questions and reporting.

Read the interview system architecture

How can enterprise AI workflows keep people in control?

Controls include restricted tool permissions, approved retrieval sources, output validation and human review. Behelitai’s published architectures describe these safeguards; the controls needed for a particular deployment depend on its data and permitted actions.

Read about workflow security

Does an enterprise AI project need fine-tuning?

Not every project needs fine-tuning. First evaluate whether instructions, retrieval quality and workflow constraints address the observed failures. Compare options against the task’s reliability, latency, governance and cost requirements.

Read the architecture decision guide

What should we prepare before discussing an AI project?

Describe the workflow, its users, available data, existing systems and the outcome you want to measure. Include access restrictions and actions requiring human approval so the initial discussion can focus on scope and evaluation criteria.

Discuss your project