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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Production-grade AI systems engineered for real business outcomes
App Corp builds production AI applications — from LLM-powered workflows and AI agents to RAG systems and AI-native SaaS. Senior-led teams, outcome-based pricing, 8–12 week delivery.
AI engineering is not about plugging in an API and calling it done. It is about building systems that reliably extract value from data, automate complex workflows, and make intelligent decisions at scale — without burning through your infrastructure budget or creating maintenance nightmares.
App Corp approaches AI engineering the same way we approach every system: with production discipline. We start by understanding the business outcome, challenge whether AI is actually required, and then design the simplest reliable architecture that solves the problem. Sometimes that means an LLM agent. Sometimes it means a rules engine. The best engineering solution is not the one with the most AI — it is the one that works.
Our AI engineering practice covers the full spectrum: predictive models, NLP pipelines, LLM integrations, AI agents, RAG systems, multi-agent orchestration, computer vision, voice AI, and AI-native SaaS platforms. Every system is built with evaluation, observability, cost control, and security from day one.
Production-grade agents that handle real work
Ground AI in your data with cited, accurate answers
Predictive models, NLP, and ML infrastructure
Ship production-ready AI SaaS in weeks
AI-powered healthcare platform — 7 modules + mobile
Estimate architecture, cost, and ROI before you commit.
Describe your problem. Get architecture, complexity, cost range, and risks.
Estimate development and operating costs for AI agent systems.
Design the right agent architecture for your use case.
Compare inference costs across models and providers.
Deep-dives into architecture, cost, and implementation.
AI engineering is the discipline of building production systems that reliably extract value from data and intelligent models.
The key differences in architecture, testing, deployment, and operations when building AI systems.
Real cost ranges for AI projects — from simple integrations to production multi-agent systems.
The gap between AI prototypes and production systems — and how to bridge it.
Real projects, real architecture, real outcomes.
Tell us what you are trying to build. We will tell you whether AI is the right approach, what architecture makes sense, and what it will cost — before you spend a dime.