Technical Review & AI Advisory
Independent technical review for investors, operators, and product teams — plus senior advisory for organizations that need depth on AI systems, infrastructure, validation, and production delivery.
Independent technical review for AI systems and claims
AI claims can look reasonable in a slide deck and very different inside the codebase, data pipeline, validation design, or deployed inference path. TensorHarmony provides senior technical review for investors, acquirers, boards, and operators who need an independent read on architecture, data lineage, reproducibility, validation posture, operational readiness, and delivery risk. The same engagement model can also support founder-stage teams through ongoing AI advisory around architecture, roadmap, vendor decisions, and production trade-offs.
Model & pipeline review
A direct review of the actual code, training pipeline, inference path, and release process against what the system claims to do.
- Architecture review against intended use
- Training pipeline reproducibility check
- Inference packaging and release-readiness review
- Code quality and maintainability assessment
- Comparison to current technical alternatives
Data lineage & label quality
A structured assessment of the dataset, annotations, splits, provenance, and quality controls behind the model’s reported performance.
- Cohort and inclusion-criteria review
- Label quality and inter-rater consistency
- Train / test leakage and split-integrity review
- Subgroup coverage and bias analysis
- Provenance, consent, and documentation review
Validation & claim review
A review of whether the metrics, endpoints, study design, and evidence actually support the product or investment claim being made.
- Endpoint-vs-claim alignment
- Statistical methodology review
- Confidence intervals and uncertainty review
- Reader-study and pilot-study soundness
- Failure-mode and limitation disclosure
AI advisory & technical leadership
Ongoing senior technical judgment for teams making architecture, roadmap, hiring, vendor, model, and infrastructure decisions.
- Weekly or biweekly advisory sessions
- Architecture and roadmap reviews
- Vendor, model, and infrastructure decisions
- Hiring loops and early ML / engineering interviews
- Decision support for delivery risk and technical trade-offs
How review and advisory engagements run
Scope the review
We define the question being asked, the decision the work needs to support, and the required access: code, data, documentation, infrastructure, team interviews, or prior reports.
Review the real system
Review engagements focus on the actual architecture, data flow, training process, validation evidence, deployment path, and operational risk — not just the presentation layer.
Deliver findings and guidance
Review ends with written findings and a walkthrough. Advisory engagements continue through a regular cadence of architecture notes, decision logs, and technical recommendations.
Discuss Your Next System or Deployment
Technical discovery, production engineering, and deployment-focused collaboration for intelligent systems, computer vision, and medical imaging workflows.
No obligation — just a focused technical conversation