Custom AI Built for Your Data. Shipped to Production.

Custom AI development at SH runs in four phases: Discovery (use case definition, data and infrastructure assessment, architecture proposal), Build (engineering team assembled, weekly demos, integration with your systems), Validate (accuracy testing, hallucination tests, security audit, load testing), and Deploy & Monitor (production deployment, monitoring stack integration, model drift monitoring). Every phase ends with concrete deliverables, not progress reports. The team that runs discovery is the team that ships the system, so context never gets lost between phases.

For a custom AI project, you typically need senior AI engineers, ML specialists, a data engineer, and a delivery lead. Hiring this team in-house takes 4 to 6 months and significant ongoing cost. Working with SH gives you a complete AI engineering team in 2 to 4 weeks. We assemble the team based on your use case, your stack, and your compliance requirements. Mid-level and senior engineers only, never juniors. The team works in your sprint cadence and stays through deployment.

Timelines depend on scope, data complexity, integration requirements, and compliance constraints. Honest answer: it depends. Reference points: a focused proof of concept runs 2 to 4 weeks. A custom AI feature inside an existing platform typically runs 8 to 16 weeks. End-to-end custom AI product development, from discovery to production, typically runs 12 to 24 weeks. Complex enterprise AI systems with regulated data and deep integrations can run 6 to 12 months. We give you a precise estimate after a discovery call, scoped to what you're actually building.

AI development cost depends on scope (how much you're building), complexity (regulated industry, real-time data, deep integrations), and timeline (parallel teams ship faster but cost more). For reference, a generative AI proof of concept runs 30,000 to 80,000 EUR. A custom AI feature inside an existing platform runs 80,000 to 250,000 EUR. End-to-end custom AI product development runs 200,000 EUR to several million depending on scale and ongoing development. We scope precisely after a discovery call so the estimate matches what you're actually building, not a generic price list.

Yes. We work in two modes. End-to-end custom AI development where we own delivery, and AI team augmentation where we embed senior AI engineers into your existing team. For augmentation, we match engineers to your stack, role, and project, run interviews with your team, and onboard within 2 to 4 weeks. AI team augmentation is the right call when you have an internal AI team that needs more capacity or a specific skill set (LLM, agents, RAG, MLOps) you don't have in-house.

We build with the technology that fits the use case, not the one we have the most experience with. Common stack: OpenAI, Anthropic Claude, open-source LLMs (Llama, Mistral) for self-hosted deployment, LangChain and LangGraph for agent orchestration, vector databases (Pinecone, Weaviate, pgvector), Python and TypeScript for application code, FastAPI and Node.js for backend services. We're cloud-agnostic and certified on GCP, AWS, and Azure. Technology selection happens during discovery based on your data, infrastructure, and compliance constraints.

Buying an AI product gives you a working system fast, with a fixed feature set and limited customization. Custom AI development gives you a system designed exclusively for your use case, your data, and your business logic, but takes longer and costs more. SH builds both. Our proprietary AI products (KnowHub, SmartSearch, InProduct AI) cover common enterprise use cases like knowledge management, semantic search, and SaaS copilots. Custom development is the right call when your use case is unique, your data has constraints standard products can't handle, or your competitive advantage depends on the AI itself, not just having it.