We build generative AI products that are useful in production, not just demos. Our team delivers LLM-powered features, assistants, and workflow systems tied to measurable business impact.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
LLM Product Integration Integrate GPT, Claude, and other LLMs into existing products with secure backend architecture. RAG System Development Build retrieval-based applications that answer from your internal documentation and knowledge. AI Agent Workflow Design Implement agentic flows that call tools, execute steps, and escalate safely when needed. Prompt and Output Engineering Design prompt templates and structured outputs for consistency and downstream automation. Evaluation and Guardrails Add policy checks, scoring, and QA loops to improve reliability in production use cases. Cost and Latency Optimization Tune model routing and inference patterns to keep quality high and operating costs controlled.
Production Delivery Experience We have shipped AI systems in live business environments with real operational constraints. Model-Agnostic Approach We choose model stacks based on your requirements, not vendor preference. Bangalore Product Collaboration Local engineering collaboration with rapid iteration and transparent delivery. Outcome-Driven Execution Our implementation is tied to conversion, productivity, and support performance metrics.
Knowledge Assistants Assistants grounded in internal docs for support, sales enablement, and internal operations. Content and Workflow Copilots Generate summaries, drafts, and decisions inside existing team workflows. GenAI-Powered Search Search and answer systems over product, policy, and process knowledge bases.
Discovery Prioritize use cases and success metrics for generative AI implementation. Step 01: Discovery Architecture Design model, retrieval, and orchestration strategy for your product. Step 02: Architecture Build Implement features, integrations, and guardrails in iterative delivery cycles. Step 03: Build Launch & Optimize Deploy, measure quality and cost, then continuously tune system performance. Step 04: Launch & Optimize
LLM Integrations Delivered: 100+ Productivity Gains: 30%+ Weeks to MVP: 4-8
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
LLM Product Integration Integrate GPT, Claude, and other LLMs into existing products with secure backend architecture. RAG System Development Build retrieval-based applications that answer from your internal documentation and knowledge. AI Agent Workflow Design Implement agentic flows that call tools, execute steps, and escalate safely when needed. Prompt and Output Engineering Design prompt templates and structured outputs for consistency and downstream automation. Evaluation and Guardrails Add policy checks, scoring, and QA loops to improve reliability in production use cases. Cost and Latency Optimization Tune model routing and inference patterns to keep quality high and operating costs controlled.
Production Delivery Experience We have shipped AI systems in live business environments with real operational constraints. Model-Agnostic Approach We choose model stacks based on your requirements, not vendor preference. Bangalore Product Collaboration Local engineering collaboration with rapid iteration and transparent delivery. Outcome-Driven Execution Our implementation is tied to conversion, productivity, and support performance metrics.
Knowledge Assistants Assistants grounded in internal docs for support, sales enablement, and internal operations. Content and Workflow Copilots Generate summaries, drafts, and decisions inside existing team workflows. GenAI-Powered Search Search and answer systems over product, policy, and process knowledge bases.
Discovery Prioritize use cases and success metrics for generative AI implementation. Step 01: Discovery Architecture Design model, retrieval, and orchestration strategy for your product. Step 02: Architecture Build Implement features, integrations, and guardrails in iterative delivery cycles. Step 03: Build Launch & Optimize Deploy, measure quality and cost, then continuously tune system performance. Step 04: Launch & Optimize
FAQ
Share your use case and we will define a practical roadmap to ship a production-ready GenAI feature set.