Custom AI App Development &
Enterprise Integration
AI-powered features that generate revenue and reduce operational load inside your product. Smart search, recommendations, NL interfaces, and document intelligence — built on a production stack your team owns and can maintain.
Seven AI application capabilities.
From enterprise backends and RAG knowledge systems to regulated-industry compliance — each capability maps to a specific business outcome, not a technology showcase.
Enterprise AI Backend Engineering
We build the foundational backend for complex AI applications using FastAPI (Python 3.12+) for high-concurrency async processing, combined with Cursor IDE for AI-accelerated development. Our engineers use Claude Code to autonomously write, test, and debug production backend code, cutting development cycles by up to 60%.
- ✓ FastAPI async architecture
- ✓ Cursor & Claude Code accelerated dev
- ✓ REST & WebSocket API design
LangChain & LangGraph Application Layer
For AI applications requiring complex reasoning chains and stateful workflows, we engineer using LangChain and LangGraph. This covers multi-step document analysis tools, AI-powered CRM integrations, intelligent triage systems, and enterprise knowledge management platforms that integrate with your existing ERP and CRM.
- ✓ Multi-step LLM chain engineering
- ✓ Stateful LangGraph workflow apps
- ✓ ERP & CRM agent integrations
RAG Knowledge Application Development
We build Retrieval-Augmented Generation applications that allow your AI to answer questions grounded in your private enterprise data. Using Pinecone Serverless for sub-100ms vector retrieval, FastAPI ingestion pipelines, and OpenAI Embeddings or Cohere Reranker for accuracy, your AI never hallucinates on your data.
- ✓ Pinecone Serverless vector store
- ✓ Cohere Reranker for accuracy
- ✓ Multi-tenant data isolation
Multi-Model AI Application Architecture
Not all tasks suit the same model. We build model routing architectures that select GPT-4o Mini for high-volume classification, Claude 3.5 Sonnet for complex reasoning, and Llama 3.3 70B for on-premise sensitive data. This reduces LLM API costs by 40–70% while maintaining best-in-class quality.
- ✓ Intelligent model routing
- ✓ GPT-4o Mini + Claude 3.5 Sonnet
- ✓ 40–70% LLM cost reduction
Regulated Industry AI Applications
Building AI for healthcare, finance, or legal? We develop HIPAA-compliant, SOC 2-ready AI applications with encrypted vector stores, audit logging, PII redaction pipelines, and private LLM deployments using Azure OpenAI Service or AWS Bedrock.
- ✓ HIPAA & SOC 2 aligned architecture
- ✓ PII redaction pipelines
- ✓ Azure OpenAI & AWS Bedrock support
AI Analytics & Intelligence Dashboards
We build AI-powered analytics platforms that go beyond static reporting. Using GPT-4o Code Interpreter capabilities, LangChain data analysis agents, and real-time streaming with Server-Sent Events, your dashboards ask questions of your data and surface insights proactively.
- ✓ Conversational data analysis
- ✓ GPT-4o Code Interpreter integration
- ✓ Real-time streaming analytics
Skill-Based Agentic Backends
We implement the 2026 SKILL.md standard to build highly modular agentic backends. By packaging complex business logic into portable skill modules, we enable your applications to scale agent capabilities without increasing context complexity or management overhead.
- ✓ Modular SKILL.md backend logic
- ✓ Portable agent capability packages
- ✓ Scalable reasoning architectures
AI that ships and stays reliable.
We build AI applications engineered for production from the first commit — not retrofitted for scale after launch. Performance, compliance, and cost-efficiency are design requirements, not afterthoughts.
Frequently asked questions.
What is the difference between an AI app and an AI agent?
An AI app is a structured application with a defined interface — a chatbot, a dashboard, a search tool — that uses AI to power specific features. An AI agent is a more autonomous system that can independently plan and execute multi-step tasks across multiple tools. We build both, and often combine them: an AI app with an embedded agent powering complex workflows underneath.
How do you handle data privacy in AI applications?
We treat data privacy as a first-class engineering requirement. For regulated industries, we deploy AI using Azure OpenAI Service or AWS Bedrock where your data never leaves your cloud tenancy. We implement PII redaction pipelines, encrypted vector stores, audit logging, and RBAC access controls. All applications are designed to align with HIPAA, SOC 2, and GDPR requirements from the first commit.
What tech stack do you use for custom AI app development?
Our primary backend stack is Python 3.12+ with FastAPI for high-concurrency async APIs, LangChain and LangGraph for AI orchestration, and Pinecone Serverless for vector storage. On the frontend, we use Next.js 15 App Router with TypeScript. For models, we use GPT-4o, Claude 3.5 Sonnet, and open-weight alternatives depending on the use case and data sovereignty requirements.
How much does custom AI app development cost?
A focused MVP with AI features typically starts at $20,000–$40,000. Enterprise applications with multi-model routing, RAG pipelines, and compliance requirements range from $50,000–$150,000+. We scope every project during a paid discovery engagement so you get a fixed price and realistic timeline before committing to a full build.
Thirty minutes.
We'll tell you exactly
where your ROI is.
No sales deck. No “AI readiness assessment.” Just a direct conversation about which of your workflows are costing the most and whether AI can fix them. If there's no compelling answer, we'll say so.