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AI & Intelligent Automation

Turn your systems into decision-making engines — not just tools.

We design and deploy AI solutions that go beyond chatbots — from CRM copilots and LLM integrations to RAG pipelines and predictive analytics. Using Gemini, Claude, OpenAI, Einstein AI, and AWS AI services, we build intelligent automation that understands your business context and drives real outcomes.

6Capabilities
3Specialized Services
What We Deliver

Our Capabilities

01

AI Assistants & Copilots

Build intelligent assistants embedded directly into your business tools — Salesforce, Slack, web apps, and mobile. These aren't generic chatbots; they understand your CRM data, customer history, and business rules to provide contextual, actionable responses.

  • CRM copilots inside Salesforce for sales, support, and operations teams
  • Internal AI assistants for teams via Slack, web apps, and mobile apps
  • Chatbots with real business context — connected to your data, not generic responses
  • Multi-turn conversations with memory and context awareness
  • Role-based AI assistants with appropriate data access controls
Einstein AIAgentforceSalesforceSlack
AI Assistants & Copilots
02

LLM Integrations

Integrate leading large language models into your existing systems. We design multi-model architectures that use the best model for each use case — whether it's Gemini for multimodal tasks, Claude for analysis, or GPT for content generation.

  • Gemini, Claude, OpenAI (GPT-4o), and open-source model integrations
  • Multi-model architecture — use the best model per use case automatically
  • Prompt engineering and response optimization for accuracy and cost
  • Fine-tuning and custom model training for domain-specific tasks
  • Token optimization and cost management across providers
OpenAIGoogle CloudAWS
LLM Integrations
03

AI Automation

Automate repetitive knowledge work with AI-powered workflows. From drafting emails and summarizing cases to processing documents and triggering intelligent actions — we connect AI to your business processes for measurable efficiency gains.

  • Auto email drafting, summarization, and case resolution
  • Intelligent workflows combining AI + Salesforce Flows + AWS Lambda
  • Document processing for contracts, invoices, PDFs, and forms
  • Automated data extraction, classification, and routing
  • AI-triggered alerts and escalations based on pattern recognition
FlowAWSSalesforce
AI Automation
04

RAG (Retrieval-Augmented Generation)

Connect AI models to your proprietary business data — Salesforce records, databases, documents, and knowledge bases. RAG ensures AI responses are grounded in your actual data, not hallucinated, with enterprise-grade security.

  • Connect AI to your business data (Salesforce, databases, documents)
  • Private knowledge base chatbots for internal teams and customers
  • Secure, context-aware responses with source citations
  • Hybrid search combining semantic and keyword matching
  • Real-time data synchronization with vector stores
AWSGoogle CloudSalesforce
RAG (Retrieval-Augmented Generation)
05

AI + Data Intelligence

Transform raw data into predictive insights and actionable intelligence. From sales forecasting and churn prediction to recommendation engines and sentiment analysis — we build AI models that directly impact business KPIs.

  • Predictive analytics for sales forecasting, churn, and lead scoring
  • Recommendation systems for products, content, and next-best actions
  • NLP, sentiment analysis, and text classification
  • Customer 360 intelligence with AI-powered segmentation
  • Real-time anomaly detection and alerting
Einstein AITableauGoogle Cloud
AI + Data Intelligence
06

AI Infrastructure

Build the foundation for scalable, production-grade AI — vector databases, model orchestration, API gateways, and inference pipelines. We architect AI infrastructure on AWS and GCP that handles enterprise workloads reliably.

  • Vector databases (Pinecone, Weaviate, OpenSearch, pgvector)
  • Model orchestration and API gateway design
  • Scalable AI pipelines on AWS SageMaker and GCP Vertex AI
  • GPU/TPU infrastructure optimization for training and inference
  • AI observability — monitoring latency, accuracy, and cost
AWSGoogle Cloud
AI Infrastructure
Case Studies

AI & Intelligent Automation Success Stories

See how we've delivered measurable results with ai & intelligent automation.

Autonomous Customer Support with Agentforce for a SaaS Platform
58%
Autonomous Resolution
Technology

Autonomous Customer Support with Agentforce for a SaaS Platform

NovaBridge Software

NovaBridge, a B2B SaaS company with 4,500 customers and 85,000 end users, was scaling rapidly but its support operation was not keeping pace. The 35-person support team handled 12,000+ tickets per month with an average first response time of 6 hours and resolution time of 3.2 days. Hiring was not solving the problem — the complexity of the product required months of agent training, and customer satisfaction was declining as the company scaled. The leadership team needed a way to handle routine support autonomously while ensuring complex issues still received expert human attention.

AgentforceService CloudEinstein AISlack Integration
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Building an AI-Powered Knowledge Base with RAG for Enterprise Support
94%
Answer Accuracy
Enterprise Software

Building an AI-Powered Knowledge Base with RAG for Enterprise Support

NovaBridge Software

NovaBridge had accumulated over 50,000 support articles, API docs, and troubleshooting guides across Confluence, Zendesk, and internal wikis. Support agents spent an average of 8 minutes searching for answers per ticket, and customers frequently received inconsistent or outdated guidance. The knowledge was there — finding it was the problem.

Google Cloud Vertex AIGemini ProClaudeCloud Run
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Intelligent Claims Automation for Property & Casualty Insurance
62% reduction
Claims Cycle Time
Insurance

Intelligent Claims Automation for Property & Casualty Insurance

Guardian Shield Insurance

Guardian Shield processed 180,000 property and casualty claims annually with an average cycle time of 23 days. The claims workflow was heavily manual — adjusters spent significant time on data entry, document review, and status updates. Customer satisfaction scores were declining as policyholders expected real-time transparency, and the company was losing market share to insurtechs offering faster, digital-first claims experiences.

Service CloudFlow OrchestratorEinstein AIAgentforce
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