Most AI projects fail between prototype and production. We close that gap — with domain expertise, hardware-specific optimization, and a performance gate at every stage.
Each service is standalone and delivers value independently. Used together, they compound — every stage informed by the one before it.
Prepare real operational data or generate synthetic domain data with a performance gate that validates downstream model accuracy — not just statistical similarity. ISO-certified compliance artifacts generated automatically.
→Domain-specific fine-tuning of foundation models — Llama, Mistral, Qwen, Phi — using LoRA/QLoRA. Edge-optimized from the start. Every model delivered with a benchmark showing accuracy delta over the base model on your domain tasks.
→Port, optimize, and validate any model for constrained edge silicon — automotive-grade processors, embedded AI platforms, and ARM devices. Real hardware validation with latency, throughput, and accuracy measured on the actual target device.
→Production deployment across cloud, on-premise, and edge. Routing logic across model tiers. OTA update pipeline for edge device fleets. Continuous drift monitoring. Every inference call logged with a full compliance audit trail.
→Data feeds fine-tuning. Fine-tuning feeds hardware onboarding. Orchestration routes using real benchmark numbers. Monitoring detects drift — and the loop restarts automatically.
Every stage proves accuracy before advancing. No handwaves.
Engage any single service. Add stages as your program matures.
Drift triggers automated retraining. The system improves on its own operational data.
We operate in regulated and constrained environments — where accuracy isn't a preference, it's a requirement.
Perception models, in-vehicle AI, V2X, and safety-critical edge inference for automotive grade silicon.
Grasp detection, 6-DOF pose estimation, scene understanding, and real-time robot inference at the edge.
Clinical synthetic data, HIPAA-compliant pipelines, and precision AI for diagnostic and operational workflows.
Fraud detection, credit underwriting, fair lending compliance, and synthetic financial data with FCRA/ECOA audit trails.
Operational AI for constrained field environments — pipeline integrity, anomaly detection, and edge inference.
Capital project intelligence — document AI, schedule risk, and cost forecasting for hyperscale build programs.
Predictive maintenance, cooling optimization, and operational AI for large-scale infrastructure environments.
Perspectives on AI engineering, domain-specific models, and the gap between prototype and production.
Why smaller, domain-optimized models consistently outperform large general-purpose models in production environments.
Our framework for building production-ready AI — from data provenance through deployment and continuous retraining.
The coming divide between organizations that deploy AI as a tool and those that embed it as infrastructure.
Data quality is not a pre-processing step. It is the primary determinant of model behavior in the field.
Precision, timing, and the relentless elimination of noise. What drumming taught me about engineering reliable AI.
How the principles behind effective model training mirror what cognitive science has known for decades about human expertise.
We work with engineering teams and technology leaders who are serious about taking domain AI to production. If you're dealing with a gap between prototype and deployment — data quality, model accuracy, hardware constraints, or operational reliability — we want to hear about it.
Away from the hype. Focused on outcomes.
Precision AI Assessment — 2-week fixed scope, fixed fee. Diagnoses the root cause. Defines the path forward.