Solutions

AI Build and Engineering

Design, build and scale enterprise-grade AI solutions from concept to production. We help organizations operationalize AI through robust architecture, engineering rigor, and cloud-native deployment models.

What We Offer

End-to-end AI engineering across agents, GenAI applications, and machine learning systems. Our expertise spans LLM-based platforms, prompt engineering, RAG architectures, intelligent data pipelines, multi-model orchestration, and automated CI/CD - delivering secure, scalable, and production-ready AI.

AI-Agents for Business Workflows

Design and deploy AI agents that automate and augment business processes across sales, operations, finance, and customer service—enabling intelligent task execution, decision support, and workflow orchestration.

GenAI & LLM-based Applications

Build LLM-based solutions powered by advanced prompt engineering, platform deployment, and full-stack web applications—enabling conversational interfaces, knowledge copilots, and enterprise productivity tools.

RAG-based Systems

Develop enterprise document ingestion pipelines with intelligent retrieval, Retrieval-Augmented Generation (RAG) architectures, and contextual knowledge access—ensuring accurate, secure, and explainable AI outputs.

ML Engineering & Cloud-Native Deployment

Deliver ML model development, tuning, validation, and MLOps frameworks with multi-model orchestration and automated CI/CD pipelines—enabling scalable, cloud-native AI deployment and continuous model improvement.

Impact Stories

Our work speaks through results. From market entry strategies to competitive turnarounds, these stories showcase how our intelligence has powered real-world outcomes for global leaders.

Monitoring industry activity in the dynamic nutritional landscape

Building a RAG-based secure enterprise knowledge system

Objective and Scope:

Developed an enterprise-grade Knowledge Center that enables organizations to unlock value from large volumes of internal documents through AI-powered search and chat. The client wanted a platform that leverages LLM frameworks, cloud‑native infrastructure, Retrieval‑Augmented Generation (RAG), and intelligent tagging and enrichment to create a single, trusted source of contextual answers grounded in their own data.

Approach:

The platform was designed as a scalable, cloud‑native full‑stack web application that sits on top of the client’s existing knowledge repositories. Different types of documents are ingested, cleansed, and enriched with intelligent tags, then indexed to power RAG‑based retrieval and AI search. On this foundation, an AI chat interface powered by LLM to interpret user queries, retrieve the most relevant passages, and generate accurate, context‑aware responses that remain grounded in the organization’s own documents and security model. Followed MLOps practices with continuous monitoring and evaluation of model performance.

Impact:

This system helped the client in the following: Consolidate fragmented research into a single, governed source of truth, preserving institutional knowledge Transform deliverables into dynamic, queryable assets to detect regulatory shifts and competitor actions early Establish a governed ML lifecycle with continuous monitoring and version control, ensuring consistent performance and reduced operational risk

AI-Driven Video Analytics for Customer Experience Intelligence and Reputation Monitoring

Objective and Scope:

The client sought to analyse videos posted on social media platforms by customers to assess customer sentiment, track perception shifts, identify reputational risks, and gauge key experience drivers. The study focused on: Tracking sentiment shifts across specific time periods and key events Identify core themes and emerging reputation risks Recommend actions to strengthen customer experience and brand trust

Approach:

We leveraged a structured, AI-enabled video analytics framework integrating Large Language Models (LLMs), Natural Language Processing (NLP), and Computer Vision. Over 2,000+ shorts & videos posted on different social media platforms were filtered and analyzed to extract relevant customer experience content. Multimodal analysis was conducted across text (titles, descriptions, transcripts), audio (tone and sentiment), and visual elements (facial expressions, environment recognition, on-screen text). The data was classified into thematic clusters and sentiment categories across different time periods, enabling trend analysis and identification of key reputation drivers.

Impact:

The solution enabled the client to gain structured visibility into dynamic competitor activity through: Centralized intelligence engine delivering a categorized, searchable repository of competitor announcements and market movements Improved benchmarking of competitor strategies by tracking acquisitions, partnerships, product launches, and regulatory updates in a structured manner Accelerated insight-led decision-making by reducing manual monitoring efforts and transforming unstructured news into actionable strategic signals

Contact Us

Get in touch with us to explore how Benori can be your strategic partner.