End-to-end AI platform design and development — from architecture to deployed product. Scalable, secure, cloud-native systems built for real-world AI workloads.
Building an AI feature is one thing — building a reliable, scalable platform that serves AI to hundreds of thousands of users is another. AI platforms require careful architecture: model serving infrastructure, async job queues, cost controls, rate limiting, observability, and the ability to swap models as the landscape evolves.
Dawnovation AI delivers full-stack AI platforms from initial architecture through to deployed, monitored production systems. Whether you need an internal AI tool, a customer-facing AI SaaS product, or an AI API layer for your business, we build it with the engineering rigour to scale.
We design the full platform architecture — microservices vs monolith, sync vs async processing, database selection, caching strategy, and AI model serving pattern.
RESTful and GraphQL APIs wrapping your AI capabilities — with authentication, rate limiting, versioning, comprehensive documentation, and SDK generation.
Admin and user dashboards for monitoring usage, visualising AI outputs, managing configurations, and tracking costs — built with modern React/Next.js.
Enterprise-grade authentication (OAuth, SAML, SSO), role-based access control, API key management, encryption at rest and in transit.
Automated testing, containerisation with Docker, CI/CD pipelines, infrastructure-as-code (Terraform/CDK), and zero-downtime deployment processes.
Platforms scale automatically with demand and include built-in AI cost tracking per user/request, budget alerts, and model routing to optimise cost vs quality.
Deep-dive into your use case, user personas, scale requirements, security needs, and budget — resulting in a detailed architecture document.
UI/UX wireframes, API contract design, and a working prototype to validate the core AI experience before full development begins.
Frontend, backend, AI integration layer, database design, and infrastructure — built in iterative sprints with regular demos.
Comprehensive testing: unit, integration, load, and security penetration testing to ensure the platform is production-ready.
Production launch with full observability — logging, metrics, tracing, alerting — and ongoing development sprints to add features.
Custom platforms giving your teams access to AI capabilities — document summarisation, content generation, data analysis — with SSO and usage tracking.
AI-powered products your customers pay for — from AI assistants embedded in your product to standalone AI SaaS platforms with billing and usage tiers.
Monetisable AI APIs exposing your trained models or AI capabilities to third-party developers, with developer portals, usage metering, and SDKs.
AI-augmented analytics platforms that combine traditional BI with LLM-powered natural language querying and automated insight generation.
Core stack used for Platform Development
Everything you need to know about our platform development service.
Still have questions? →We build for both. Most clients start with a major cloud provider (AWS, Azure, or GCP) for faster iteration. We also design and deploy fully on-premise or hybrid architectures for regulated industries.
An MVP can typically be delivered in 6–12 weeks. A full production platform with all features typically takes 3–6 months. We use agile sprints with working software delivered every 2 weeks.
We offer ongoing maintenance and development retainers, or we fully document and hand over to your internal team. Most clients choose a hybrid: we handle AI model updates while their team manages product features.
Yes. We integrate with whatever stack you have — whether that's a legacy monolith, a modern microservices architecture, or a hybrid. We meet you where you are.
Ready to get started?
Book a free discovery call — we’ll scope your project and outline a clear path forward.
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