Technical Discovery & Architecture
Feasibility, architecture, data, latency, validation, and workflow review before a build becomes expensive. We help scope the smallest deployable path from idea to production system.
Find the hard constraints before they become expensive
AI projects often discover their real constraints too late: data quality, latency, workflow fit, evaluation design, deployment cost, integration boundaries, or regulatory expectations. Technical discovery is meant to surface those constraints early and turn them into a practical architecture, scoped roadmap, and build plan. TensorHarmony brings senior applied AI and production engineering judgment across computer vision, medical imaging, full-stack product delivery, and research-to-production systems.
Technical feasibility & risk sizing
An honest read on whether the idea is buildable, what makes it hard, and which assumptions need validation before serious investment.
- Technical feasibility against current model and data constraints
- Latency, cost, privacy, and deployment risk review
- Build-vs-buy-vs-API recommendations
- Data availability and quality assessment
- Go / no-go recommendation for the next phase
Product scope & deployable MVP
Define the smallest useful product slice that can be shipped, tested, and expanded without creating architectural debt.
- MVP definition with explicit cut-lines
- Workflow and user assumptions to validate
- Data and labeling plan tied to product scope
- Phased roadmap from prototype to production
- Success criteria for adoption, performance, and reliability
Reference architecture & integration boundaries
A practical architecture for the model, data flow, interfaces, infrastructure, and deployment path before engineering work expands.
- Model, API, data, and infrastructure architecture
- Integration boundaries with existing systems
- Evaluation and validation infrastructure design
- Inference packaging and release-artifact planning
- Cost, latency, scaling, and operations trade-offs
Regulated-readiness checkpoints
For clinical, imaging, or high-risk systems, add early checkpoints around intended use, validation, risk, and evidence expectations.
- Intended-use and claim boundary review
- High-level study and validation planning
- Acceptance metrics linked to product behavior
- Failure-mode and risk framing
- Train / tune / test split strategy for regulated workflows
Discovery engagement flow
Technical intake
We review the problem, product goal, data situation, current code or prototype, workflow, team constraints, and timeline.
Architecture and risk review
We identify the highest-risk assumptions, evaluate feasibility, map integration boundaries, and define the technical path toward a deployable system.
Roadmap and handoff
The deliverable is a written architecture and roadmap: recommended scope, risks, build phases, technical decisions, and next-step implementation plan.
Validate the Technical Path Before You Build
Pressure-test feasibility, architecture, data requirements, deployment constraints, and the path from prototype to production.
Useful for early-stage planning, MVP scoping, and architecture review