Sensor to decision with bounded low latency
The applications below are a selected set of examples chosen to illustrate how Loom's sensor-to-decision architecture maps onto real deployment verticals. For long-context, large-model, and retrieval-heavy examples, see the Loom Node page.
The workload that runs under the verticals
The verticals below look like different industries, but the workload underneath is consistent: the same handful of stages, run in order, then run again on the next token, frame, or tick.
The same shape runs under edge LLM inference at low batch, long-context and KV-heavy serving, retrieval-heavy local inference, sparse mixture and routing patterns, sensor-to-decision systems, radar, RF, vision, and audio pipelines with ML fused into the loop, post-training adaptation and LoRA-style updates, and deterministic embedded autonomy where jitter matters. Each one maps onto those eight stages; Loom is built for the stages.
Intelligence close to the process
Defect detection · Quality control · Sorting · Packaging verification
Trigger-driven inspection loops stay on one device: sensing, inference, and actuator signaling. GPIO-triggered dispatch and on-chip command execution keep the response path close to the hardware and reduce dependence on host wakeups or OS jitter.
Semiconductor tools · Chemical processing · Precision manufacturing
Learned control paths combine preprocessing, inference, and small on-device model updates on one device. Assisted control loops and low-rank update flows suit adaptive calibration and drift compensation workloads where host latency is a problem.
Unified control and perception
Humanoid balance loops · Flight dynamics · Visual navigation
Many deployed robots still split fast control and high-level perception across separate processors because GPUs cannot guarantee control timing. Loom eliminates the split. By moving the data plane onto the device, from sensor DMA, through the MXU, and out to actuator GPIO via the on-chip command processor, Loom runs kHz-class humanoid balance loops and flight dynamics natively. No RTOS fallback. No host OS jitter. The learned policy is the hard real-time controller.
Smart city cameras · Industrial surveillance · Searchable video
Camera-adjacent pipelines get detector-style preprocessing, patch extraction, transformer-style vision blocks, and local alert policy on one device, for systems moving from simple detection toward ranked search, summarization, and richer scene understanding without pushing every frame upstream.
Bounded-latency inference for clinical instruments
AI-guided imaging · Automated measurement · Instrument assistance
Ultrasound and other clinical instruments need inference close to the signal path under tight thermal and power limits. Loom keeps beamformed data, scan-conversion-style transforms, model inference, and local control or annotation policy close together on one device.
Clinical companion processors · Neural speech separation · Real-time noise suppression
Next-generation neural speech separation and real-time noise suppression can outperform classical DSP, but the compute does not fit at the ear. Loom runs that compute as a body-worn companion processor: it takes raw audio from ear-level mics over low-latency wireless, isolates the target voices with deep streaming models, and sends the clean audio back to the earpiece. A full clinical shift of neural denoising on a standard battery pack, with on-device adapter updates for user- or environment-specific tuning without cloud connectivity.
Neural signal decoding · Adaptive prosthetic control · Signal drift compensation
The biggest unsolved problem in neural interfaces and powered prosthetics is signal drift: brain electrical signals change daily as electrodes shift and neural pathways adapt. Loom pairs continuous signal ingest with on-device adapter updates that track drift without requiring clinical recalibration visits. All neural data stays on-device, never leaving the patient's body.
Autonomy at the tactical edge
Autonomous drones · Electronic warfare · Tactical belt nodes
Loom runs the mission host-free at the tactical edge: when the link goes down and GPUs idle, the on-chip command processor drives navigation, tracking, and decisions in total radio silence.
Earth observation · Onboard triage · Adaptive downlink management
Downlink is often the bottleneck. Loom runs image triage close to the sensor so the spacecraft can keep, rank, or summarize the frames that matter most before transmission. Embedded-vision operators and on-chip control suit cloud rejection, maritime monitoring, and change-detection-style workloads where host round-trips are too expensive.
RF classification · Waveform adaptation · Jam-assist · Retune policy
RF front-end and converters live off-chip; Loom is the DSP and inference engine behind them. Classify hostile or unknown signals from streaming I/Q, adapt waveforms, retune parameters, and assist jamming and protection decisions with low-latency local execution. FFT, FWHT, complex vector math, and on-device codebook compression sit on the same silicon as the neural network, so classify/adapt/retune/assist decisions do not need to round-trip through a radio chip, a CPU, and a GPU.
Natural fit for passive RF sentries, cognitive radio pods, and adaptive EW nodes where the host link is intermittent or absent. When the waveform set and decision policy are frozen, Loom-ROM extends the same pipeline into a fixed-function silicon derivative for tighter timing and lower power.
Real-time data reduction and local analysis
Collider event selection · Anomaly screening · Streaming classification
Modern collider trigger systems operate under microsecond-scale latency budgets, and ML triggers are increasingly moving into that regime. Trigger-style workloads keep a compact model and its working set on-chip, and an external event releases a pre-staged graph. Loom supports the building blocks for fixed-latency streaming classifiers.
Streaming diffraction analysis · Experiment steering · Real-time reconstruction
Modern synchrotron detectors produce gigabytes per second of diffraction data. Scientists wait hours for offline processing to discover that the experiment needed different parameters. Loom performs streaming classification on detector data as it arrives, enabling real-time experiment steering: the chip identifies crystal hits, flags anomalous diffraction patterns, and feeds decisions back to the beamline control system within microseconds.
Portable basecalling · Sequence alignment · Variant calling
Portable sequencing systems often rely on tethered external compute for real-time analysis. Loom keeps more of the pipeline local: streaming raw signal ingest, model-based basecalling, and follow-on alignment or variant workflows on the same device, reducing reliance on laptop-attached GPUs in field or lab deployments.
Radio-native AI
Complex lane formats · Streaming datapaths · Metadata sideband control
Loom supports native complex arithmetic for GEMM/GEMV and a TX metadata sideband, so IQ-domain math and radio-control metadata can share one command and streaming model with minimal format translation.
Learned demodulation · Channel estimation · Signal recovery
Learned receiver research pipelines keep IQ-domain math, feature extraction, and classification or estimation close together on one device.
Constellation monitoring · Modem preset updates · Link maintenance
Loom can sit close to the modem decision loop: streaming datapaths handle signal processing while sideband metadata and a lightweight configuration bridge let the system adjust radio presets without bouncing every decision through the host.
Private, local intelligence for organizations
On-premises chatbots · Document summarization (RAG) · Coding assistants
Loom runs local inference on large models; supported model size depends on precision, architecture, and context strategy. Long-context and retrieval-heavy workflows can stay on-premises, keeping sensitive documents, code, and internal knowledge bases inside the local environment.
Always-on personal agent · Local data integration · Morning digest
A persistent, private background assistant that runs on local Loom hardware and keeps working even when your laptop is closed. It integrates with your own data, email, documents, and spreadsheets, and with third-party apps, to organize your schedule, draft email, track subscriptions, or plan an event. Any action that spends money or sends a message asks for confirmation first. Each morning it produces a skimmable digest that prioritizes urgent items and suggests next steps for the day.
Legal/Medical model adaptation · Enterprise model updates · Self-learning appliances
Loom supports parameter-efficient updates such as LoRA and other adapter-style tuning methods on-device. That makes it suitable for workflows where organizations want local adaptation on proprietary data without sending documents or logs to an external training service.
AR glasses · Body cameras · Field instruments · Continuous context
Next-generation AR glasses and extreme-environment body cameras must actively understand and retrieve past context over multi-hour sessions. Loom's compression capabilities are designed to maintain a large, resident-memory context window of everything the user has seen or heard, enabling retrieval and summarization locally without streaming to the cloud. Privacy stays on-device.
Perception pipelines on one device
Embedded vision · Patch extraction · Transformer blocks · Image classification backbones
Loom covers patch extraction, projection, attention-style blocks, and FFN building blocks on one device, reducing the need to split vision preprocessing and dense model execution across separate chips.
Convolutional vision with native spectral support
Loom runs a wide range of CNN perception tasks on one device. The Sparse Compute Engine accelerates the spectral and sparse operations these models lean on, keeping the convolution-heavy backbone and its post-processing in the same bounded pipeline.
Inline intelligence for network and data systems
Inline DPI/IDS · DDoS mitigation · Encrypted traffic analysis
Inline and near-inline security pipelines run as streaming ingest, feature extraction, model scoring, and fast local response, without pushing every decision through host software. For high-rate environments, it can keep larger capture windows in local memory and apply deeper analysis selectively, reducing reliance on packet sampling and post hoc cloud processing.
Log analytics · Semantic search · Edge RAG · Anomaly ranking
The retrieval flow uses Loom-native scoring, ranking, and gather/scatter primitives to perform content-based lookup without host-side merge once launched.
Real-time risk engines · Non-linear alpha · Order-book feature extraction
Loom runs the real-time risk and alpha layer alongside the market data path. It ingests full-depth order books and execution streams via streaming DMA and continuously updates local risk parameters, macro features, and non-linear alpha signals within microsecond-scale budgets, close to the feed and without host round-trips. Loom is the model that decides what the order book means.
Autonomous intelligence at distributed nodes
Substation control · Microgrid balancing · Battery dispatch optimization
The power grid is fragmenting into millions of solar panels, batteries, and EV chargers. Local substations and microgrids must make millisecond-scale dispatch decisions based on volatile local conditions. Loom runs the local optimization loop, balancing battery discharge, load shedding, and grid stability without round-tripping to a central controller. On-device adaptation tracks seasonal and equipment-aging drift continuously.
Per-plant targeting · Fleet drone adaptation · Micro-dose application
Farm conditions vary by the square meter: lighting, weed phenotypes, crop spacing, soil moisture. Fleet drones and ground rovers run local vision inference at the point of action, with on-device adaptation that specializes the model to the exact plot of land over repeated passes. The result is per-plant targeting rather than broadcast application.
Wildfire detection · Air quality · Remote environmental monitoring
Thousands of solar-powered sensor nodes across remote terrain need to run classification locally, wake only on relevant events, and operate for years without maintenance. Loom's low-power sleep modes and on-device adaptation are designed for sentinel workloads where the model must continuously recalibrate against seasonal and environmental drift without human intervention.
Bridge inspection · Pipeline integrity · Acoustic fault detection
Inspecting airplane wings, submarine hulls, pipelines, and suspension bridges for micro-fractures currently requires taking the asset out of service. Loom listens continuously while the structure operates: streaming sensor data, running spectral analysis and classification on-chip, and flagging anomalies locally without cloud connectivity. On-device adaptation tracks baseline drift as environmental conditions change over months and years.
If you want sensing and inference on the same device, talk to us.
