Custom AI Development That Ships to Production

Custom AI development typically ranges from $20,000 for a proof-of-concept prototype to $200,000+ for a production system with integrations and compliance requirements. At Digital Scientists, our Experiment delivers a working AI prototype in 5 days for a fixed $20,000 — so you can validate the approach before investing in a full build. Full production AI projects with model training, system integration, and deployment typically fall in the $75K–$200K range over 8–16 weeks. The actual cost depends on data complexity, integration requirements, and regulatory needs (HIPAA, SOC 2, etc.).

An AI proof of concept takes 5 days. A production MVP with trained models and basic integrations typically takes 8–12 weeks. Enterprise deployments with complex data pipelines, compliance requirements, and multi-system integrations run 3–6 months. We work in two-week sprints so you see working AI from week two, not at the end. Most clients start with an Experiment or Working Session to validate the approach before committing to a full timeline.

It depends on the type of AI. Generative AI and LLM applications (document analysis, chat interfaces, content generation) can work with minimal proprietary data — the foundation models bring the intelligence. Custom ML models (predictions, classification, anomaly detection) do require training data, and we help you assess data readiness as part of our Discovery phase. If your data is messy, incomplete, or siloed, our AI Readiness Assessment identifies gaps and gives you a practical plan to close them before investing in development.

Healthcare is where we go deepest — 9 of our 14 AI projects are in healthcare. We deploy AI on HIPAA-compliant infrastructure (AWS with BAA, encrypted at rest and in transit), implement role-based access controls, and maintain audit logs for all AI-processed PHI. For LLM-based features, we use API configurations that prevent model training on patient data. Our healthcare practice has shipped systems processing 100,000+ patient encounters across 130+ facilities in 7 states. We understand clinical workflows, EHR integration, and the real reasons AI fails in health systems.

A chatbot responds to questions in a conversation. An AI agent takes autonomous action — it reads emails, updates your CRM, generates documents, makes API calls, and orchestrates multi-step workflows without human intervention. Agents can work in swarms where multiple specialized agents collaborate on complex tasks. We build both, but the biggest ROI we see comes from AI agent systems that replace entire manual workflows — like our SBIR grant automation that reduced a weeks-long screening process to daily automated digests.

Yes — integration is usually the hardest and most important part of an AI project, and it's where most vendors fall short. We've integrated AI systems with Epic, PointClickCare, Salesforce, SAP, and dozens of custom platforms via HL7/FHIR, REST APIs, and database-level connections. Our engineering team handles the full stack — from model development to the API layer to the frontend that your team actually uses. AI that doesn't connect to your systems stays a demo. We ship production integrations.