Platform Solutions
Deep-dive into the four core solution domains — each built for production scale, enterprise compliance, and measurable AI impact.
Multi-Tenant Physical AI Telemetry Platform
A horizontally-scalable, multi-tenant Physical AI platform designed from the ground up to ingest, process, and route massive volumes of sensor, robotic, and equipment telemetry — while guaranteeing strict per-tenant data isolation and enterprise SLA commitments.
Challenges
- Strict tenant data isolation across physical robot & machine fleets
- Handling 30k–60k burst telemetry events per day reliably
- Sub-20ms ingest latency at p95 with high compression
- Maintaining 99.9% uptime across multi-AZ deployments
Solutions
- Kafka topic-per-tenant partitioning with dedicated consumer groups
- Apache Flink stateful stream processing for enrichment & sensor dedup
- KEDA event-driven autoscaling on queue depth and throughput metrics
- Multi-AZ Kubernetes with pod disruption budgets and topology spread
Spatial, Hardware & Calibration Intelligence
Production-grade Retrieval-Augmented Generation systems built for physical equipment calibration, machine schematics, robotics runbooks, and high-stakes operational intelligence workflows. Hybrid retrieval with domain-aware chunking and cross-encoder re-ranking.
Challenges
- Accurate retrieval from unstructured physical calibration, CAD, and operational docs
- Data leakage prevention across multi-tenant hardware and client corpora
- Context degradation on complex multi-variable hardware manuals
- Ultra-low latency requirements for real-time field operations
Solutions
- Hybrid dense + BM25 sparse retrieval fused via RRF ranking
- Domain-aware chunking strategy with per-tenant document namespacing
- Cross-encoder reranker improving answer precision by ~30%
- Async pre-embedding pipeline with pgvector and ChromaDB fallback
Physical AI & Sensorimotor Fine-Tuning Pipelines
End-to-end parameter-efficient fine-tuning pipelines using LoRA and QLoRA adapters, enabling domain-specific LLM customization on physical telemetry, sensor logs, and operational vocabularies at a fraction of full-tuning GPU cost.
Challenges
- Full fine-tuning prohibitively expensive on 7B–70B parameter models
- Domain shift between general-purpose LLMs and physical/industrial telemetry
- Managing experiment reproducibility across distributed sensor training runs
- Deploying fine-tuned adapters without model serving re-architecture
Solutions
- LoRA rank-16 adapters: <0.1% parameter overhead, full fine-tune quality
- QLoRA 4-bit NF4 quantization reducing GPU memory by 60–70%
- FSDP across 8–32 GPUs with activation checkpointing
- W&B + MLflow for experiment tracking, comparison, and lineage
- Adapter hot-swapping on vLLM for multi-task serving from one base model
Secure Edge & Cloud Inference Architecture
Production secure inference serving stack built for real-time cyber-physical workloads. Combines vLLM's PagedAttention with Ray Serve orchestration, enforced via deterministic safety policies, Istio service mesh, and automated redaction at the request boundary.
Challenges
- Multi-tenant LLM serving with guaranteed workload and physical safety isolation
- Deterministic latency bounds for cyber-physical control loops
- GPU underutilization from conservative static batch sizing
- Secrets and per-tenant hardware API key management at scale
Solutions
- Kubernetes namespace-per-tenant with resource quotas and network policies
- Istio mTLS for all inter-service communication with SPIFFE identity
- OPA admission webhooks enforcing safety, compliance, and RBAC policies
- Deterministic edge filtering and NER redaction pipeline at gateway layer
- vLLM continuous batching + PagedAttention maximizing GPU throughput
- HashiCorp Vault for tenant secrets; KEDA for scale-to-zero on idle
Ready to scale your AI platform?
Whether it's designing multi-tenant RAG systems, fine-tuning LLMs for domain accuracy, or cutting GPU costs by 30%+ — let's connect.