Our complete service architecture
From the physics of a 3nm transistor to the control software that stabilizes a city’s power grid: 8 service areas, 26 systems, one architectural foundation.
How it connects
Each area exists because the one below or beside it sets the limit for the next. Scroll through the stack.
We work at the layer most software never sees: process control inside the fab, the EDA algorithms that turn RTL into a mask-ready layout, and the mixed-signal IP that moves 224 gigabits per second down a single lane.
About semiconductorA data center is one machine with a hundred thousand parts. We build the controller that programs its switches, the kernel that schedules its cores and tiers its memory, and the orchestration layer that treats every cluster as one fleet.
About cloudModel quality is bounded by how fast gradients move between GPUs and how many tokens a serving engine can return per watt. We build the communicators, partitioning schemes, quantizers, and schedulers that set those limits.
About ai & mlEvery layer of abstraction costs cycles. We build the hypervisors and isolation runtimes that give workloads the hardware directly, and the migration engines that move a running machine without anyone noticing.
About virtualizationA power grid or a continuous process plant cannot pause for a software update. We build the protocol gateways, historians, controllers, and alarm systems that run these environments with deterministic timing and audited access.
About industrial controlA model is not a product. What makes it one is the retrieval layer that gives it context, the evaluation harness that proves it is right, the guardrails that stop it when it is not, and the interface that puts the answer in front of the person who has to act on it. We build those systems for the software use cases that carry real operational weight.
About ai productsMost of what an organization calls software is a system of record under load. We treat it the way we treat a switching fabric or a control loop: measure it, find the layer that actually sets the limit, and rebuild that layer. This is not application maintenance. It is the architecture of the systems a business cannot switch off.
About enterprise platformsWe are asked most often to say what a system will do before anyone commits to it: whether a design holds at the next order of magnitude, what a platform is really worth in an acquisition, and which layer to rebuild first. The answer comes from measurement and from having built the layer in question, not from a reference model or a maturity grid.
About consultationCategory A
From the physics of a 3nm transistor to the I/O that carries its signal off the die.
About semiconductor
We work at the layer most software never sees: process control inside the fab, the EDA algorithms that turn RTL into a mask-ready layout, and the mixed-signal IP that moves 224 gigabits per second down a single lane.
Real-time inference across every tool in the fab
A distributed, real-time inference engine that ingests 10,000+ sensor data points per second from fab tools (etch, deposition, lithography). It runs multivariate statistical models and lightweight neural networks to detect equipment drift and outputs automated recipe adjustments (R2R) and predictive maintenance alerts.
From RTL to a mask-ready GDSII at 3nm
A suite of algorithms that convert RTL (Register-Transfer Level) code into a physical GDSII file ready for mask generation. Includes logic synthesis, place-and-route, clock tree synthesis, DRC/LVS, and parasitic extraction—optimized for 3nm, 5nm, and GAAFET architectures.
PAM4 and NRZ transceivers up to 224 Gbps per lane
Mixed-signal PHY IP blocks including PAM4/NRZ transceivers up to 224Gbps per lane, adaptive equalizers (FFE/DFE) for >40dB channel loss, clock-data recovery (CDR) circuits, and built-in self-test (BIST) for link health monitoring. Delivered as synthesizable RTL or hardened GDSII.
UCIe, CXL 3.x, PCIe 6.0/7.0, and 800G Ethernet logic
Protocol stack implementations and bridge architectures for UCIe (die-to-die chiplet links), CXL 3.x (memory pooling and coherency), PCIe 6.0/7.0 (peripheral I/O), and 400G/800G Ethernet fabrics. We build the digital logic for arbitration, flow control, retransmission, and cache coherency protocols.
Category B
The network, the operating system, and the control plane of a hyperscale facility.
About cloud
A data center is one machine with a hundred thousand parts. We build the controller that programs its switches, the kernel that schedules its cores and tiers its memory, and the orchestration layer that treats every cluster as one fleet.
Intent to flow table, with micro-burst telemetry
A centralized SDN controller (ONOS-based or custom) that programs P4-programmable switches and eBPF-based host networking. Translates high-level intents into low-level flow tables, ACLs, and routing rules. Includes real-time telemetry collection (sFlow/IPFIX) for micro-burst detection and anomaly detection.
A kernel written for the silicon it runs on
A custom Linux kernel distribution and user-space runtime. Features include: NUMA-optimized CPU schedulers, tiered memory management (DRAM + PMEM + CXL-attached memory), SPDK-based I/O stack that bypasses the VFS layer, and power-capping algorithms for dynamic frequency scaling.
Every cluster on every cloud, managed as one fleet
A Kubernetes-based control plane extended with multi-cluster federation (managing K8s across AWS/Azure/GCP/on-prem), auto-scaling agents (Karpenter), FinOps cost-monitoring dashboards, disaster recovery controllers (automated failover with RPO <15min), and GitOps agents (ArgoCD/Flux) for declarative state management.
Category C
Training that scales across thousands of accelerators and inference that serves in microseconds.
About ai & ml
Model quality is bounded by how fast gradients move between GPUs and how many tokens a serving engine can return per watt. We build the communicators, partitioning schemes, quantizers, and schedulers that set those limits.
PyTorch and TensorFlow, tuned for the interconnect
Extensions and optimizations for PyTorch and TensorFlow. Includes custom NCCL/RCCL communicators for multi-GPU all-reduce, ZeRO-3 partitioning (model states, gradients, and optimizers), mixed-precision trainers (FP8/BF16), asynchronous checkpointing, and elastic training that dynamically adds/removes nodes mid-run.
FP16 to INT4 with under 1% accuracy loss
A serving engine built on TensorRT and ONNX Runtime with custom optimization passes. Includes quantization algorithms (PTQ/QAT to compress FP16→INT4 with <1% accuracy loss), graph-optimization passes (operator fusion, kernel tiling), dynamic batching schedulers, speculative decoding routers, and serverless scaling adapters (Knative integration).
Category D
Hypervisors, micro-VMs, and isolation runtimes with the smallest possible exit latency.
About virtualization
Every layer of abstraction costs cycles. We build the hypervisors and isolation runtimes that give workloads the hardware directly, and the migration engines that move a running machine without anyone noticing.
Type-1 hypervisors, micro-VMs, and zero-downtime migration
Type-1 hypervisors (KVM/Xen modifications for minimal exit latency), micro-VMs (Firecracker, Cloud Hypervisor) for lightweight serverless workloads, container isolation runtimes (gVisor, Kata Containers), and hardware virtualization enablement (VT-x/EPT, vGPU, SR-IOV). Includes live-migration engines for zero-downtime VM movement.
Category E
SCADA and DCS platforms that keep grids stable and plants running at five nines.
About industrial control
A power grid or a continuous process plant cannot pause for a software update. We build the protocol gateways, historians, controllers, and alarm systems that run these environments with deterministic timing and audited access.
Protocol gateways to 3D HMI, compliant with NERC CIP and IEC 62443
A full-stack SCADA platform including protocol gateways (DNP3, IEC 61850, MODBUS, OPC UA), RTU/PLC polling engines, a time-series historian database (QuestDB/InfluxDB-based), HMI visualization dashboards (3D geospatial mapping), and role-based access control (RBAC) with NERC CIP and IEC 62443 compliance.
PID and MPC control with 99.999% controller uptime
A high-speed, distributed process automation platform for continuous industrial production. Includes PID (Proportional-Integral-Derivative) and MPC (Model Predictive Control) algorithm libraries, deterministic fieldbus interfaces (Profibus, EtherCAT), redundant controller configurations for 99.999% uptime, alarm management systems (ISA-18.2 compliant), and integrated batch process control engines (ISA-88 compliant).
Category F
Applied AI systems: the software that turns a trained model into something an operation can act on.
About ai products
A model is not a product. What makes it one is the retrieval layer that gives it context, the evaluation harness that proves it is right, the guardrails that stop it when it is not, and the interface that puts the answer in front of the person who has to act on it. We build those systems for the software use cases that carry real operational weight.
Agents that hold a plan, call real systems, and stop where they should
Multi-step agent runtimes wired into the systems an organization actually runs on. Includes tool and function-calling layers over internal APIs, planner-executor loops with bounded retries, durable run state that survives a restart, per-step policy checks and human approval gates on irreversible actions, token and cost budgets enforced at the run level, and a full trace of every call an agent made.
Retrieval that cites its source, across contracts, drawings, and archives
Retrieval systems over document estates that were never designed to be queried. Includes layout-aware parsing of PDFs, scans, and technical drawings, table and field extraction with per-field confidence, hybrid vector and lexical retrieval with re-ranking, chunking tuned to document structure rather than character count, answers bound to a citable source page, and incremental re-indexing as the corpus changes.
Defect detection and site monitoring at line rate
Vision pipelines that run at the speed of the process they watch. Includes detection, segmentation, and anomaly models trained on the small, imbalanced defect sets real production yields, deployment to edge accelerators and industrial GPUs, camera calibration and multi-view geometry, drift monitoring against a golden set, and active-learning loops that route uncertain frames back to a human before they become a false accept.
Forecasts, anomalies, and optimizers that end in a decision
Systems that turn history into a decision rather than a dashboard. Includes probabilistic forecasting with calibrated prediction intervals, multivariate anomaly detection over operational telemetry, constrained optimization and scheduling solvers, scenario simulation against assumptions that are written down, backtesting harnesses that score a model on the decision it would have made, and drift monitoring on every input distribution.
Speech and language front ends that hand off cleanly to a person
Voice and text front ends over operational systems. Includes streaming speech recognition and synthesis with sub-second turn latency and barge-in, intent handling grounded in live system state rather than a scripted tree, multilingual and code-switched input, redaction of personal data before it reaches a model, escalation that hands a live conversation to a person with the full context, and transcript-level quality scoring.
Category G
The load-bearing software an organization runs on, engineered to the tolerances of the hardware beneath it.
About enterprise platforms
Most of what an organization calls software is a system of record under load. We treat it the way we treat a switching fabric or a control loop: measure it, find the layer that actually sets the limit, and rebuild that layer. This is not application maintenance. It is the architecture of the systems a business cannot switch off.
Event-driven cores that hold consistency under load
Transactional cores designed for correctness under concurrency. Includes event-sourced and CQRS architectures, idempotent APIs and exactly-once processing over Kafka and Pulsar, saga and outbox patterns for cross-service consistency, partitioning and sharding chosen from the measured access pattern, back-pressure and load-shedding at every boundary, and multi-region topologies whose consistency and failover semantics are written down before they are needed.
Lakehouse and stream processing with sub-second freshness
Data platforms built for freshness and lineage, not volume alone. Includes lakehouse storage on Iceberg or Delta, change-data-capture ingestion from operational systems, streaming transformation in Flink or Spark, columnar and time-series serving layers, metric and semantic definitions held in one place, column-level lineage end to end, and contract tests that fail a pipeline before a dashboard reports a wrong number.
Error budgets, traces, and failure testing on the paths that matter
The measurement and failure discipline that keeps a platform inside its availability target. Includes service level objectives derived from user-visible paths rather than host metrics, distributed tracing and high-cardinality telemetry through OpenTelemetry and eBPF, cost-aware retention tiers, progressive delivery with automated rollback on an error-budget breach, chaos and load testing against production topologies, and incident review that ends in a code change.
Identity, key custody, and zero-trust paths through the whole stack
Security designed into the architecture rather than added at its edge. Includes workload identity and short-lived credentials, key custody in HSMs and KMS with rotation and recovery documented and rehearsed, end-to-end encryption and tokenization for regulated data, zero-trust paths with policy enforced at every hop, supply-chain integrity through signed builds and SBOMs, and threat models written against the system as it is actually deployed.
Front ends for systems where a wrong click has consequences
Interfaces for people who operate complex systems under time pressure. Includes real-time views over streaming telemetry at high update rates, geospatial and topology visualization that stays readable as the estate grows, alarm shelving and rationalization so the signal that matters survives, confirm-and-commit flows on irreversible actions, an audit trail of who saw what and acted when, and colour and contrast treatments that hold up on a control room wall.
Category H
An independent read on an architecture, from the engineers who would otherwise have to build it.
About consultation
We are asked most often to say what a system will do before anyone commits to it: whether a design holds at the next order of magnitude, what a platform is really worth in an acquisition, and which layer to rebuild first. The answer comes from measurement and from having built the layer in question, not from a reference model or a maturity grid.
A first read on the architecture, written by the engineer who would build it
Scoped engagements that produce a decision rather than a deck. We take the system as it is measured today, in latency, throughput, yield, uptime, and unit cost, locate the layer that sets the limit, and write down the options with their trade-offs and what each would cost to build. Delivered as a written architecture read with the measurement behind every claim, and the engineer who wrote it available to defend it.
What an engineering asset is worth, and what it will cost to keep
Independent assessment of a technology asset ahead of an investment, an acquisition, or a long contract. Covers the architecture and the scale at which it breaks, code and data ownership, third-party and licence exposure, security posture and incident history, key-person and knowledge concentration, infrastructure cost per unit of work, and a costed remediation plan in the sequence we would actually follow.
A sequence that keeps the system running while it changes underneath
Plans for replacing load-bearing systems without an outage. Includes dependency and data-flow mapping of the estate as it actually runs, strangler-fig sequencing in reversible steps, dual-run and reconciliation that prove the new path before the old one is cut, data migration verified at every cut-over, rollback criteria written before the work starts, and a cost and risk model per step so the programme can stop at any point and still be ahead.
Evidence that a design meets the standard it claims
Assurance for systems that must satisfy an external standard and prove it. Includes gap assessment against IEC 62443, NERC CIP, ISO 27001, and sector-specific regimes, controls mapped to the architecture rather than to a spreadsheet, evidence collection automated wherever the system can produce it, design review at each gate, and independent verification before an audit rather than after one.
Tell us where latency is costing you, from the die to the data center to the control room. An architect replies with a first read of the problem, not a sales deck.
