AI/GenAI Services

Powering Business Transformation
Through Applied Artificial Intelligence

We offer cutting-edge AI and Generative AI (GenAI) services designed to help enterprises deploy intelligent solutions, automate workflows, and unlock the value of their data. Whether you're integrating LLMs into your apps, building multi-modal AI systems, or deploying end-to-end AI pipelines, our services cover every layer of the AI stack—from model hosting to fine-tuning, RAG, vision, and more.

Project
Hosted LLMs

Enterprise-Grade Hosting for Large Language Models

Deploy, scale, and manage the power of Large Language Models (LLMs) in your own environment — securely and efficiently. Our Hosted LLMs service offers a fully managed, production-ready infrastructure for running state-of-the-art models like OpenAI GPT-4, Meta’s LLaMA, Anthropic’s Claude, Alibaba’s Qwen, Mistral, and more, without depending on public endpoints or sacrificing data privacy.

Whether you're operating in regulated industries like finance, healthcare, or government, or you need lower latency and tighter control over usage and cost, our solution provides unmatched flexibility.

Core Features:

  • • Private & On-Prem Hosting: Run LLMs in your own cloud (AWS, Azure, GCP) or data centre for full data residency.
  • • GPU-Optimized Runtime: Accelerated inference using A100, L40S, H100, with autoscaling and resource throttling.
  • • Multi-Model Support: Host and switch between different models based on use case, size, or cost.
  • • Advanced Monitoring: Track token usage, performance metrics, latency, and version history through a centralized dashboard.
  • • Secure API Access: Seamless integration with your internal apps, APIs, and knowledge bases with authentication and RBAC.
Ideal For
  • icon Enterprise knowledge assistants
  • icon Legal or compliance data analysis
  • icon Code generation or technical document drafting
  • icon Financial modelling or report summarization
Model Context Protocol (MCP)

Standardized Context Windows for Multi-Agent AI Systems

Out-of-the-box LLMs are powerful — but generic. To unlock maximum value, they must be customized to your domain, tone, workflows, and terminology. Our LLM Fine Tuning service empowers you to do just that: adapt and specialize models for increased accuracy, better relevance, and enterprise-grade performance.

We support both full model fine-tuning and parameter-efficient fine-tuning (PEFT) strategies like LoRA, QLoRA, Adapter Tuning, and Prefix Tuning — dramatically lowering compute and cost requirements.

What we Offer

  • • Data Curation & Preprocessing: We help gather and clean domain-specific documents (PDFs, chat logs, emails, etc.) for safe, high-quality training.
  • • Fine-Tuning Pipelines: Use Hugging Face, DeepSpeed, or OpenLLM-based stacks to fine-tune your model efficiently.
  • • Evaluation & Testing: Custom metrics and human feedback loops to evaluate model alignment, hallucination, and bias.
  • • Multilingual Tuning: Fine-tune models for regional languages and dialects to better serve local markets.
Ideal For:
  • icon Legal, insurance, or healthcare-specific terminology
  • icon Support bots trained on internal SOPs
  • icon Brand-aligned tone and response control
  • icon Instruction-following behaviour specific to business logic
RAG (Retrieval-Augmented Generation)

Enhance LLM Accuracy with Real-Time Verified Data

Retrieval-Augmented Generation (RAG) bridges the gap between static models and live data. LLMs are powerful, but they can’t "know" your internal data unless you provide it. That’s where Retrieval-Augmented Generation (RAG) comes in — an architecture that merges LLMs with enterprise data sources for highly accurate, grounded, and up-to-date answers.

Our RAG solutions combine vector search, embeddings, and LLMs to deliver grounded, up-to-date, dynamic data retrieval to enable truly intelligent assistants, researchers, and analysts.

Architecture capabilities:

  • • Vector Indexing: Convert documents, databases, or chat logs into high-dimensional embeddings using OpenAI, Cohere, or SentenceTransformers.
  • • High-Precision Retrieval: Use FAISS, Qdrant, Pinecone, Chroma or Weaviate to find the most relevant data chunks in real time.
  • • Citation & Explainability: Get answers with source citations, improving trust and transparency.
  • • Chunking & Ranking Optimization: Ensure accurate retrieval through dynamic chunk sizing, summarization, and result ranking.

Connected Data Sources:

  • • PDFs, Excel, and CSV files.
  • • SharePoint, Notion, Google Drive.
  • • SQL/NoSQL databases.
  • • Websites and REST APIs.
Use Cases
  • icon AI-powered enterprise knowledge assistants
  • icon Legal research and document generation
  • icon Automated data insights and BI query interfaces
Model Context Protocol (MCP)

Standardized Context Windows for Multi-Agent AI Systems

The Model Context Protocol (MCP) is our proprietary standard for managing context-aware interactions across multiple AI agents and tools. It ensures coherent, stateful, and secure communication among LLMs, plugins, APIs, and user inputs.

Why MCP Matters

  • • Context persistence: Maintain state across sessions and agents
  • • Scalable memory: Handle large token windows (>100k tokens)
  • • Context segregation: Role-based access and redaction for sensitive inputs
  • • Cross-agent orchestration: Multiple agents working in parallel with shared context
Ideal For:
  • icon AI copilots that interact with multiple subsystems
  • icon RAG pipelines that need long-document grounding
  • icon Enterprise agents with evolving user histories
Computer Vision

Extract Insights from Images, Video, and Live Feeds

Our Computer Vision (CV) services leverage state-of-the-art models and tools to extract actionable insights from static images, live video feeds, and real-world environments. We build and deploy solutions tailored for high-speed, high-accuracy applications across industries.

Our CV stack includes:

  • • Object Detection & Classification: Detect and classify objects in real-time using advanced models like YOLO, DETR, MobileNet, or custom CNNs for use cases such as manufacturing QA, smart surveillance, and retail analytics.
  • • Tracking & Motion Analysis: Monitor object movement and behaviours with DeepSORT, ByteTrack, or Kalman Filters for applications like pedestrian tracking, crowd analytics, or vehicle movement detection.
  • • Face Recognition & Pose Estimation: Enable biometric identification and behavioural analysis with face matching, facial landmarking, and body pose estimation — ideal for security, authentication, or fitness/health applications.
  • • OCR & Document Parsing: Accurately extract structured data from scanned documents, printed forms, ID cards, invoices, and even handwritten notes using deep OCR pipelines and layout analysis.
  • • Image Captioning & Scene Understanding: Automatically generate contextual descriptions and understand spatial relationships within images for content tagging, accessibility, or surveillance insights.
  • • Anomaly Detection: Identify outliers, defects, or abnormal behaviour in visual data — from product surface defects in manufacturing to irregular activity in CCTV feeds or anomalies in medical scans.
  • • Edge & Platform Integration: Seamless deployment across edge devices (NVIDIA Jetson, Google Coral), cloud GPU environments, and integration with video analytics platforms, IoT systems, and enterprise dashboards.
VLMs (Vision-Language Models)

Unified Understanding of Text, Images, and Videos

Vision-Language Models (VLMs) combine natural language understanding with computer vision, enabling AI systems to interpret, reason, and respond to multimodal inputs. We help businesses integrate VLMs for use cases requiring cross-domain cognition.

Capabilities

  • • Image captioning and Q&A (e.g., “What’s in this invoice?”): VLMs can automatically generate natural-language captions for images, enabling machines to describe what they "see." When combined with question-answering capabilities, users can interact with images through natural language — asking specific questions about content.
  • • Visual search and product recommendation: With visual search, users can upload an image (e.g., a shoe, bag, or part) and the system retrieves similar or related items from a catalogue or database. Combined with product metadata and language inputs, VLMs also provide contextual product recommendations.
  • • Diagram or chart interpretation: VLMs can read and understand graphs, charts, tables, and flow diagrams, extracting key insights and even summarizing trends or anomalies in plain language.
  • • Instruction-following via screenshots or GUI screenshots: VLMs can analyse screenshots or GUI interfaces and follow instructions related to them. They recognize buttons, forms, alerts, and layout elements to generate accurate guidance or simulate user interactions.
  • • Video summarization and highlight detection: VLMs and multimodal pipelines can summarize long videos, detect key scenes, and identify important objects or events. This allows for efficient content review without manually watching entire footage.

Model Support: CLIP, BLIP, Gemini, LLaVA, Flamingo, GPT-4-Vision

Use Cases:
  • icon E-commerce image-based chat
  • icon Legal/medical document visual parsing
  • icon AI agents for robotic vision or inspection
Conversational AI

Natural Language Interfaces for Humans and Machines

Our Conversational AI solutions enable fluid, human-like interactions via chat, voice, or multimodal interfaces. We build enterprise-ready chatbots, voice assistants, and virtual agents that understand context, manage dialogues, and integrate with business systems. Built on top of state-of-the-art NLP and dialogue management systems, our bots don’t just respond — they understand, reason, and act.

From customer support and HR assistants to sales enablement bots and internal knowledge helpers, we build agents that are helpful, scalable, and always available.

Core capabilities:

  • • Multi-turn Dialogue Flow: Memory-aware and contextual responses for complex conversations.
  • • Omnichannel Delivery: Deploy across WhatsApp, Slack, Web, IVR, Teams, SMS, and voice platforms.
  • • Speech Interfaces: Integrated TTS and ASR (Google, Azure, ElevenLabs) for real-time voice interaction.
  • • Knowledge Integration: Combine with RAG to bring internal FAQs, SOPs, or policies into the conversation.
  • • Personality & Guardrails: Customize tone, behaviour, and safety filters for your brand and use case.
Real-World Applications:
  • icon Customer service automation (L1 + L2 support)
  • icon Virtual HR helpdesks
  • icon Sales and onboarding assistants
  • icon Smart appointment and booking systems