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Generative AI Integration Built for Production: Grounded, Reliable, Cost-Efficient
Embed GPT-4o, Claude, and Gemini into your product with production-grade RAG pipelines, LLMOps, and hallucination controls.
Why Choose InApps Technology?
As a business leader, you need solutions that make an impact, solutions that drive results and grow with you. InApps Technology provides custom-built AI Agents that help your business run smarter, faster, and more efficiently.
We make complex AI solutions easy to implement and manage, even if you don't have a technical team in place.
Why AI Agents, Why Now?
Companies using AI agents reduce operational costs by 40% within the first year
Source: McKinsey, 2024
Early AI adopters are 3x more likely to outperform market competitors by 2026
Source: Gartner, 2024
AI agent market projected to reach $50B by 2030, adoption is accelerating now
Source: Mordor Intelligence
Real AI Applications We Build
Document Intelligence
Teams spend hours manually reviewing contracts, reports, and invoices — slow, expensive, and error-prone.
Outcome
LLM-powered document Q&A and extraction processes documents in seconds with auditable output and citation grounding.
Intelligent Customer Support
Support teams are overwhelmed with repetitive tickets while complex issues get delayed.
Outcome
Generative AI handles 60–80% of common support queries autonomously, escalating edge cases to human agents.
AI-Powered Search
Keyword-based search returns irrelevant results, frustrating users and increasing bounce rates.
Outcome
Semantic search with LLM re-ranking delivers accurate, contextual results — increasing search-to-conversion by 35%.
Content & Copy Generation
Marketing and content teams bottleneck on volume — brief-to-publish cycles take days for what should take hours.
Outcome
LLM-assisted content workflows accelerate production by 3–5x while maintaining brand voice through fine-tuned prompts.
Our AI Capabilities
We build autonomous AI agents that plan, reason, and execute multi-step tasks. From single-task agents to multi-agent orchestration systems that handle entire workflows without human intervention.
Mini Case Study
Customer support agent handling 80% of inbound tickets automatically, response time cut from 4 hours to 30 seconds.
From Idea to Production in 8 Weeks
Use case prioritization, data audit, architecture blueprint, ROI model
Working agent prototype, integration tests, stakeholder demo
Production-grade agent, guardrails, monitoring dashboard, user testing
Full deployment, runbook, team training, SLA agreement
Performance tuning, prompt versioning, cost tracking, model updates
What This Means for Your Business
Average reduction in manual processing time
Average reduction in per-task operational cost
Faster product iteration with AI-Native teams
Featured Case Study
DR.NEE Healthcare
Generative AI integration for medical Q&A, RAG pipeline grounded in clinical guidelines, 100K+ users, 4.8★ App Store rating.
Models, Frameworks & Infrastructure
LLM Providers
OpenAI GPT-4o
Claude 3.5
Google GeminiMMistralFrameworks
LangChainLLlamaIndexInfrastructure
AWS BedrockPPineconeWWeaviateAI Engineering Team
500+ in-house developers · Specialized AI/ML vertical · Vietnam + Singapore
Minh Tran
AI Solutions Architect
Linh Pham
LLM Integration Engineer
Duc Le
MLOps Engineer
FAQ
We implement RAG pipelines grounded in your verified data, confidence thresholds that trigger fallback responses, output validation layers for structured data, and human-in-the-loop review workflows for high-stakes outputs.
Yes. We offer on-premise LLM deployment using open-source models (Llama 3, Mistral) on your own infrastructure. For cloud LLMs, we configure enterprise API agreements with data privacy guarantees and avoid training data opt-outs.
Both services cover LLM and RAG integration. This service page is focused specifically on the integration work — embedding AI into existing products. Our AI Development Services covers broader AI product builds from the ground up.
A focused integration — AI-powered search, document Q&A, or content generation — takes 2–4 weeks. A production-grade RAG system with LLMOps takes 6–10 weeks. We provide a detailed roadmap after a free discovery call.
We implement model routing (smaller models for simple tasks), semantic caching for repeated queries, batching strategies, and cost monitoring dashboards. Most clients reduce LLM costs by 40–60% after optimization.
Trusted by Engineering Teams Across 15+ Countries
Start with a Free AI Readiness Assessment
Our AI architects will map your highest-ROI automation opportunities and design a tailored roadmap, at no cost.