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Conversational & Generative AI Systems

Redefine engagement, automation, and intelligence with custom LLMs, AI copilots, and next-gen conversational ecosystems. Our Conversational & Generative AI Systems give companies the ability to create smart assistants, specific to the domain, and enterprise chat interfaces that are all supported by large language models (LLMs). We focus on making organizations' natural language understanding and generation processes secure, scalable, and context-aware while also integrating them into current workflows smoothly.

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Description

We build LLM-driven systems that assist people with their work, provide better customer experience, and make the automation based on knowledge. Our offering encompasses the area of customer support chatbots and internal enterprise copilots. We apply various techniques such as prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuned transformer models to guarantee that the responses are contextually accurate, explainable, and safe.
Our partnership includes providing a cloud-native LLM infrastructure that is compliant with the data governance of your enterprise. Thus, the risk from generative AI is minimized while the measurable value it adds to your organization is maximized.

Knowledge Base

Methodology

Step 1
Use Case Definition & Data Preparation

We begin with the recognition of main business goals—customer support, content creation, or process automation—and the collection of domain-specific datasets to either train or fine-tune LLMs.

Step 2
Model Development & Fine-Tuning

The establishment of models for certain purposes is carried out through the utilization of OpenAI GPT architectures, Llama 3, or Mistral.

Step 3
RAG & Knowledge Integration

Through the use of vector databases (Pinecone, FAISS), we establish retrieval-augmented generation (RAG) pipelines to embed the model in current enterprise data, leading to fewer hallucinations and increased factual precision.

Step 4
System Deployment & Orchestration

Kubernetes deploys our conversational systems as microservices that can be positively scaled horizontally and easily integrated with current APIs, CRMs, and databases.

Step 5
Monitoring, Feedback & Continuous Learning

Each agent that has been deployed comes with tools for monitoring, including telemetry, analytics dashboards, and model evaluation metrics (BLEU, ROUGE, perplexity) to ensure quality, optimize response relevance and retrain when necessary.

A few of our flagship implementations of production-ready systems

G20
Ministry of Finance, Government of India
Ministry of Tourism, Government of India
Government of New South Wales | Investment NSW
Sulzer
Amul | The Taste of India
The Cosmos Co-Operative Bank Limited
Ellucian
ONDC | Open Network for Digital Commerce
Dr. Reddy's Foundation
IBM Quantum Computing
Business Standard
ThePrint

Check out the FAQs.

Let’s Build Your Next-Gen AI Copilot!

From intelligent chatbots to advanced enterprise copilots, we help you harness generative AI that learns, adapts, and scales. Deliver personalized interactions and actionable insights with LLMs fine-tuned for your domain.

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The production of chatbots, AI copilots, knowledge assistants, and LLM-driven enterprise automation tools is done by us, and these applications can be customized for either text or voicebased interactions.

Definitely. We fine-tune domain-specific LLMs using proprietary and open-source architectures (GPT, Llama, Falcon) tailored to your business domain.

Retrieval-Augmented Generation improves precision by linking LLMs to corroborated data sources, thereby making sure that the replies are both grounded and contextually right.

Definitely. Our approach allows for the development of API-based integrations to facilitate the smooth transfer of data between Salesforce, HubSpot, ServiceNow, and SharePoint, among others.

Data encryption, access control measures, and private deployment models are among the strategies we employ to ensure compliance with GDPR and SOC 2 standards.