End-to-end product engineering for AI-native applications, from foundational architecture through MLOps and production operations.
Some problems sit outside the standard service catalog. Domain-specific models. AI-native products. Multi-modal applications. Edge deployments with custom hardware.
Off-the-shelf AI hits ceilings at the edges of every business: specialized domains, custom hardware, multi-modal complexity, or product requirements that need deeper engineering than configuration can provide. Custom AI Engineering exists for those problems. Total Sync builds AI products end-to-end, from foundational architecture through MLOps and steady-state operations, with engineering teams embedded alongside yours.
Production AI products engineered for your specific problem and your stack. We handle the full lifecycle: foundational architecture, custom model development and fine-tuning, MLOps infrastructure, production deployment, and ongoing operations. Embedded engineering pods work alongside your team, with IP transfer and knowledge handover engineered into the engagement from day one.
Select the team configuration needed to build your custom AI architecture. See how resource shifts affect delivery timeline complexity.
*Delivery timelines represent typical project schedules using dedicated sprints and parallel integrations. Timelines are finalized at project kickoff.
Foundational architecture design for AI-native products and platforms.
Custom model training and fine-tuning on your data and use cases.
Full MLOps infrastructure spanning training, evaluation, and deployment.
Embedded engineering pods working alongside your team.
Production deployment with full observability and operational tooling.
Steady-state operations and continuous capability improvement.
IP transfer and knowledge handover engineered into the engagement.
Select any core capability below to view system architecture mockups and configs.
Foundational architecture design for AI-native products and platforms. We build high-throughput vector storage hubs, custom inference models, and real-time streaming engines designed to scale with your user demands.
Dives deep into the problem space, technical constraints, and product requirements.
Produces a foundational architecture covering model strategy, data architecture, and MLOps.
Includes model development, evaluation, and production deployment with parallel team integration.
Brings the system into operational use with handover documentation, moving into steady-state operations.
Post-launch, the embedded pod transitions to advisory mode, with the client team owning operations. Typical timeline runs 8 to 20+ weeks depending on scope.
Ideas held in prototype stage for months, awaiting a production roadmap.
Production AI product with clear ownership, observability, and a defined scaling path.
Schedule a discovery call to discuss your custom AI engineering needs and explore how an embedded engineering pod could accelerate your build.
Whatever your stack and product require. Custom AI Engineering engagements are architecturally flexible, building on the tools and infrastructure that fit your specific environment.