Custom AI Applications

Go offers superior performance, built-in concurrency with goroutines, and lower memory footprint compared to Python/Node.js. For AI applications handling high throughput (thousands of requests/sec), Go provides 5-10x better performance. We use Go for APIs and business logic, while AI models run via Ollama or Python microservices. This hybrid approach gives you the best of both worlds: fast, reliable infrastructure with powerful AI capabilities.

Vector databases like ChromaDB, Qdrant, and Milvus store embeddings (numerical representations of text/images/audio). They enable semantic search, RAG (Retrieval Augmented Generation), and similarity matching. For example, instead of keyword search, users can ask questions in natural language and get relevant results from your knowledge base. Essential for AI chatbots, document Q&A, recommendation systems, and intelligent search applications.

ChromaDB is open-source and can run embedded (no separate server needed), making it perfect for smaller projects and easy to deploy. Qdrant and Milvus are better for large-scale production with advanced filtering. Pinecone/Weaviate are managed services (vendor-hosted). We typically recommend: ChromaDB for MVPs, Qdrant for production, Milvus for enterprise scale. All are supported, and we help you choose based on your needs and scale.

Absolutely. Go's standard library and ecosystem make it easy to integrate with any system via REST APIs, gRPC, webhooks, or database connectors. We've integrated with Salesforce, SAP, Oracle, MySQL, MongoDB, legacy SOAP services, and more. If it has an API or database connection, we can integrate it. We also build custom ETL pipelines if needed.

You own the complete source code, so you can modify it in-house or hire any developer. We also offer ongoing maintenance and feature development at hourly rates ($100-$150/hour) or monthly retainers ($5K-$15K/month). Many clients start with post-launch support (included for 60-180 days) and then choose a retainer for continuous improvements.

We can deploy models three ways: (1) Use cloud APIs (OpenAI/Anthropic) - you pay per token, (2) Self-host open-source models via Ollama (Llama 4, DeepSeek-R1, Qwen3) - zero API costs, or (3) Hybrid approach. For high-volume apps, self-hosting saves 80-90% vs APIs. We help you calculate costs and choose the most economical approach for your usage patterns.

Yes! We build native apps (Swift/Kotlin), React Native apps (single codebase for both platforms), or Flutter apps. Choice depends on your requirements: native for maximum performance, React Native for code reuse with your web app, Flutter for beautiful UI and cross-platform consistency. All options include full AI integration, offline capabilities, and App Store/Play Store deployment.

We follow agile methodology with 2-week sprints. Requirements changes are normal and expected. For small changes, we accommodate them within the scope. For significant scope changes, we provide updated timelines and costs upfront. You'll see working software every 2 weeks, so you can course-correct early. We prioritize delivering value over rigid adherence to initial specs.