Vision Analytics · AI · GenAI
Active Learning · On-Premise & Cloud · No ML Team Required
Sightlinq

From raw image or video to trained model — on your terms.

Sightlinq is a vision analytics platform for teams who work with cameras in the physical world — images or continuous video. Capture from edge devices, annotate manually or let the model do it, train on your own data, and deploy back to the edge. GenAI-generated insights review your annotation quality in plain language as you go. Run fully on-premise with no data leaving your network, or connect to cloud storage and cloud AI backends for multi-site deployments — your choice, your configuration, your data.

Request a Demo See How It Works
Continuous improvement loop
01
Capture
Edge devices upload images and video automatically. Deduplicated and organised by project.
Automated
↓
02
Annotate
Round 1: manual. Round 2+: model pre-draws boxes, human corrects only mistakes.
Human + AI
↓
03
Train
One click. Training runs on your hardware — GPU if available, CPU otherwise. Status live in the browser.
Automated
↓
04
Deploy & Loop
Model deployed as a live inference function. Becomes the auto-annotator for the next batch.
Edge-ready

The Problem

Training a Vision Model
Shouldn't Require a Data Science Team.

Most visual AI tooling assumes you have annotators, ML engineers, GPU clusters, and cloud accounts. Most engineering teams have none of these. They have a camera, a defect to detect, and a deadline.

📸
Images pile up with no path forward
Edge cameras produce thousands of images. Without a structured pipeline to annotate and train on them, they accumulate on a hard drive and the insight stays locked inside.
🛠️
Annotation tools, training tools — nothing connects
Existing solutions treat annotation, training, and deployment as three separate problems requiring three separate tools, three separate teams, and constant manual handoff between them.
☁️
Cloud-first tools that can't touch your data
Many annotation and training platforms require uploading images to a third-party cloud. For regulated, confidential, or simply proprietary production imagery, that's a non-starter.
🔁
Manual annotation never gets faster
Without a feedback loop, every new batch of images costs the same annotation effort as the first. There's no compounding — hours per batch stays hours per batch, forever.
🎓
Generic models trained on the wrong data
Off-the-shelf models are trained on public datasets. They've never seen your PCB, your lighting, your defect types. Precision in production requires a model trained on your images.
📡
No clear path from trained model to edge device
Even teams that get a model trained hit a wall at deployment. Getting it onto an industrial PC, a Jetson, or a Pi — in the right format, running reliably — requires engineering effort most teams don't have.
⚠️
No way to roll back a bad model
When a newly deployed model starts causing false rejects on the production line, teams scramble to find the previous weights file — if it was even saved. There's no version history, no rollback button, no audit trail. Every deployment is a one-way door.
Sightlinq closes every one of these gaps.

One interface. One pipeline. Images arrive from the edge, get annotated — with increasing AI assistance each round — trained on your hardware, and deployed back to the device. No cloud account. No ML team required.

See How It Works

Data sovereignty

Your images
don't have to leave your site.
Your training platform
shouldn't either.

Sightlinq deploys fully on-premise in air-gapped environments — a factory floor, a retail back office, a field station, a hospital network. Role-based access controls, project-scoped annotators, and audit trails give you traceability from raw image to deployed model — without a single byte leaving your network.

🔒
Air-gapped deployment
Every component runs on your infrastructure. No external API calls, no telemetry, no vendor access. Fully auditable at every layer.
👥
Granular access controls
Annotators see only the projects they're assigned to. Managers control who trains, who labels, and who deploys. No shared credentials.
☁️
Cloud when you need it
Running multiple sites? Switch storage to Azure Blob, AWS S3, or GCS with a single config change. Want a cloud AI backend instead of your own GPU? Point training and inference at AWS Bedrock, Google Vertex AI, or Azure OpenAI. The annotation pipeline stays identical — only the backend changes.
✔  No cloud account required for on-premise deployments
✔  Data encrypted at rest and in transit when using cloud storage
✔  Suitable for regulated, defence, and IP-sensitive environments
✔  Self-hosted open-source annotation engine — no vendor lock-in

End-to-end pipeline

Seven Steps.
One Click at a Time.

Every step after image capture is automated. The only human action is annotation — and by Round 4, even that is mostly reviewing what the model already got right.

01
Edge
Image Capture
Cameras on edge devices write images or video to a local folder. An uploader agent wakes on a schedule, deduplicates via content hash, and syncs new files to pluggable storage — local, network share, or cloud.
automatedhash dedup
02
Ingest
Task Creation
The manager UI detects new images and video, automatically creates annotation tasks, assigns them to the right team members, and sends a notification — "14 new images ready in PCB Inspection." Video files are split into frames automatically.
automatedRBAC-aware
03
Annotate
Human Review — or AI-Assisted
Annotators log in, click Annotate →, and land directly on their job. Round 1: every box is drawn by hand — with video, CVAT interpolates boxes between frames automatically, reducing effort dramatically. Round 2 onward: the trained model pre-draws boxes and annotators only correct mistakes.
manualai-assistedimages + videoframe interpolation
04
Export
Annotation → Training Format
The manager clicks Export & Train. Sightlinq pulls all completed annotations, converts them to the training format, splits 80/15/5 train/val/test automatically, and writes the dataset manifest to storage.
automated80/15/5 split
05
Train
Model Training
Training starts immediately in the background. Status updates every 10 seconds in the browser. The manager can close the tab — training keeps running on your hardware. A notification fires on completion.
GPU or CPUlive statusnotifications
06
Deploy
Auto-Deploy as Live Inference
After training, the best model is automatically deployed as a live serverless inference function. The annotation UI can now use it to pre-label the next image or video batch without any manual configuration.
serverlessautomatededge-exportable
07
Loop
Continuous Improvement
The next batch of edge images or video arrives. The model pre-annotates them. Annotators review, catching edge cases and new defect types. Accuracy improves every iteration — without anyone reconfiguring anything.
self-improvingdomain-specific
Annotation time — per 500-image batch (or equivalent video)
Round 1
8–10 hrs
Round 2
3–4 hrs
Round 3
1–2 hrs
Round 4
30 min
Round 5+
minutes
Real test results — PCB defect detection
0.894
mAP50 best class
27
Auto-detections
693
Training images
83 min
CPU-only training

No GPU required. The test above ran on a laptop CPU — Intel i7-1355U, 30 epochs, 83 minutes. The missing_hole class reached mAP50 0.894, production-quality for inline inspection — on commodity hardware.

Automated ingestion

Images and Video.
From Camera to Annotation Engine — Automatically.

The gap between "camera captures something" and "annotator has a task to work on" is usually filled by manual file transfers, shared drives, and someone remembering to upload. Sightlinq closes that gap entirely.

📁
Watch any folder
Configure the uploader to watch one or more folders on the edge device. When new images or video files appear, they are picked up automatically — no trigger needed, no script to run.
🔁
Deduplicated automatically
Every file is fingerprinted before upload. If the same image or video has already been processed — from this device or another — it is skipped. No duplicate tasks, no wasted annotation time.
🗂️
Tasks created and assigned instantly
Once files are uploaded, Sightlinq creates an annotation task automatically and assigns it to an annotator based on your configured strategy — round-robin or least loaded. The annotator opens their app and the task is already waiting.
🎬
Video handled natively
Video files are uploaded as a single file. The annotation engine extracts and manages frames internally — no pre-processing, no frame splitting scripts. Annotators annotate frames directly, with box interpolation between keyframes to reduce effort.
🔒
Validated before upload
The uploader checks that each file is fully written and readable before sending. Truncated files — common when cameras write while the uploader is watching — are automatically skipped and retried.
uploader config — watches.yml 
watches:
  - path: C:\inspection\camera_a
    project: pcb_line_1
    batch_size: 20

  - path: C:\pen_camerasarn_a
    project: livestock_counter
    batch_size: 1  # video files
Multiple folders. Multiple projects. One config file.
What happens next — automatically
1File detected and fingerprinted
2Validated — fully written, not corrupt
3Uploaded to storage
4Annotation task created
5Task assigned to annotator
6Annotator opens app — task already waiting

Deployment flexibility

One Platform.
Four Deployment Modes.

Whether your data must never leave the building or you need to aggregate images and video from ten sites globally, Sightlinq adapts to your infrastructure — not the other way around.

Most common
🏭
Fully on-premise
Edge devices, annotation server, training server, and storage all run on your local network. No internet required. No data leaves the building. Ideal for regulated environments, defence, and IP-sensitive deployments of any kind.
☁️
Hybrid — edge local, training cloud
Edge devices capture and upload to a private cloud storage bucket. The annotation and training server runs in your cloud VPC. Images are encrypted in transit and at rest. Suits multi-site operations without a centralised server room.
🌐
Multi-site cloud
Multiple sites each run local edge devices. All images flow into a shared cloud storage layer. A single annotation and training server serves all sites centrally. Models deploy back to each site independently.
🧠
Cloud-native AI backend
Already committed to a cloud AI stack? Point Sightlinq's training and inference at AWS Bedrock, Google Vertex AI, or Azure OpenAI instead of your own GPU. No infrastructure to provision — the annotation pipeline is identical either way.
Storage backends: local folder · NAS/SMB network share · Azure Blob Storage · AWS S3 · Google Cloud Storage — configured in a single .env file. Switch without changing any application code.
AI backends: your own GPU or CPU · AWS Bedrock · Google Vertex AI · Azure OpenAI — selected per project, not locked in at install.

System Architecture

Three Layers.
On-Premise, Cloud, or Both.

Sightlinq runs fully on your own infrastructure — air-gapped, on-site local network, or hybrid. Storage is pluggable: swap between local folder, network share, and cloud object storage with a single configuration change. The annotation and training layers can run on-site, in a private cloud VM, or against a cloud AI backend — your choice.

Device layer
Edge Device Industrial PC · Raspberry Pi
  • Camera captures images or video to local folder
  • Uploader agent runs on schedule
  • Content-hash deduplication
  • Pushes to storage via sync endpoint
  • Reads config from project file
  • Supports images and video files (.mp4, .avi, .mov)
Annotation layer
Annotation Server Manager UI · Annotation engine · Inference runtime
  • Project & user management with RBAC
  • Bounding box annotation interface
  • Serverless model inference for auto-label
  • Annotators scoped to their projects
  • Deep-link from manager to annotation job
Training layer
Training Server CPU or GPU · Any on-premise machine
  • Training triggered from manager UI
  • Domain-specific augmentation presets
  • Best model saved to storage automatically
  • Auto-deploy to inference runtime post-training
  • Export to ONNX, TensorRT, CoreML, TFLite
Local storage
Default for development and single-site deployments. Zero configuration.
Network share (NAS / SMB)
For on-site local network environments where all machines can mount the same volume.
Cloud object storage
For multi-site or hybrid deployments. Images from multiple facilities flow into a single shared storage — training and annotation remain centralised.

Auto-label loop

The Model Trains
Your Next Annotator.

Every production run makes annotation faster. The model trained on your images and video, under your lighting conditions, with your defect classes, becomes the pre-annotator for the next batch.

Generic auto-labeling services use models trained on broad public datasets — useful for common objects, useless for your specific defect types under your specific conditions. Sightlinq trains on your data. The model that pre-labels your next batch has seen hundreds of examples of your defects under your lighting. That's why correction rates drop so fast.

Annotation time per batch — improving with every round
Round 1
8–10 hours
Round 2
3–4 hours
Round 3
1–2 hours
Round 4
30 minutes
Round 5+
Spot-check
Round 1
Manual annotation
~500 frames · 8–10 hrs
Every box drawn by hand. Slow, but builds the foundation dataset. Train model v1.
→
Model v1
First trained model
Knows your defect classes
Imprecise, but usable for pre-annotation. Deploys as a live inference function.
↓↓
Round 4+
Near-automatic
~500 frames · 30 min
Model auto-labels ~95%. Annotators spot-check only. Catches edge cases and new variants.
→
Model v4+
Production-grade
Reliable for inline inspection
Retrains automatically when new defect types appear in the feedback loop.
↓↑
Round 2
AI-assisted
~500 frames · 3–4 hrs
v1 pre-labels ~40% correctly. Annotator corrects the rest — 60% faster than Round 1.
→
Model v2
Improved model
More data, better generalisation
Pre-labels ~70% correctly next round. Deployed automatically after training.

Enterprise model management

Every Model Version.
Full Control. Zero Risk.

Sightlinq integrates MLflow — an industry-standard model registry. Every training run is automatically tracked, versioned, and auditable. Deploy with confidence. Roll back in seconds if something goes wrong.

Model version history — PCB defect detection
v4
Archived
mAP50: 0.823 · 2026-07-29
↩ Restore
v3
● Live
mAP50: 0.891 · 2026-07-27
↺ Redeploy
v2
Archived
mAP50: 0.756 · 2026-07-20
↩ Restore
v1
Archived
mAP50: 0.612 · 2026-07-15
↩ Restore
Rollback in 60 seconds. If v3 causes false rejects on the line — click Restore on v2. Model redeploys automatically. No re-training. No downtime.
QA workflow: Train → download ONNX → test offline → approve → Deploy to Production
🔢
Every version tracked automatically
Each training run registers a numbered version with full metrics — accuracy, precision, recall, dataset size, hyperparameters. Nothing is ever overwritten or lost.
⚖️
Compare before you deploy
Side-by-side metric comparison of any two versions. See exactly how much the model improved — mAP50, precision, recall, dataset size — before committing to production.
🔒
Governed deployment
Optional manual promotion gate — no model goes live without manager approval. Download ONNX, test offline, then promote. Full audit trail for ISO 9001 and regulatory compliance.
↩
One-click rollback
Restore any previous version to production in under 60 seconds. Click Restore — model redeploys automatically. No re-training, no file hunting, no downtime on the inspection line.
✔  MLflow model registry — industry standard used by leading customers worldwide
✔  Runs on-premise — model artifacts stored in your infrastructure, not a vendor cloud
✔  Full audit trail from raw image to deployed model version — ISO 9001 ready

GenAI Insights

Your Model Detects.
Your GenAI Explains.

Sightlinq doesn't just find defects — it tells you what they mean, why they happened, and what to do next. Built-in generative AI reasoning runs entirely on your hardware. Ask questions in plain English. Get answers that a quality engineer would be proud of.

Where the category is heading: industry analysts point to the same shift across vision analytics — from passive dashboards and alerts toward agentic, conversational systems that can be asked what a camera is seeing and reason about it. Sightlinq's GenAI insight layer is a working version of that shift today, not a roadmap item.
Root cause analysis
Manager
"Why did the reject rate triple after 2am?"
Sightlinq
All 14 spurious_copper failures between 02:10–03:45 share the same board quadrant. Pattern correlates with batch L2406-C loaded at 02:05 — suggest checking stencil temperature logs for that window.
→ Answered in 4 seconds · No engineer required · Fully offline
Defect description
Defect detected on line
Class: missing_hole · Confidence: 0.94
Sightlinq
Through-hole pad at component U4, pin 3 — drill missing. Severity: Critical. Board will fail functional test at ICT. Flag for immediate rework before wave solder.
→ Instant severity triage · No manual inspection needed
Shift report generation
Manager
"Generate the shift report for today's morning run."
Sightlinq
Shift Summary — Line 1 · 06:00–14:00
847 boards inspected · 23 failures (2.7%) · Down from 4.1% yesterday. Most common: spurious_copper (14). Recommend reviewing stencil cleaning schedule — rate rose 3× in final two hours.
→ ISO 9001 ready · Generated in seconds · Fully offline
Annotation quality review
Before Export & Train
Reviewing 240 annotations from last session
Sightlinq
Quality score: 8.2/10. 3 issues found: Image 47 — box too wide, includes non-defective area. Image 91 — label should be mouse_bite not open_circuit. Image 188 — defect visible but not annotated.
→ Catches labeling errors before they corrupt your model
Natural language image search
Manager
"Show me all defects near the power connector from last week."
Sightlinq
Found 11 images matching. 8 are spurious_copper near J3 connector, 3 are spur on adjacent ground plane. All from shifts on Tue–Wed — same time window as reported paste issue.
→ Search by meaning, not by filename · All data stays on-site
🔒
Every answer stays in your building.
The AI model runs on your GPU. Your images never leave your network. Your defect data never trains someone else's model. Fully air-gapped deployment available.
✓ No cloud account ✓ No data egress ✓ Air-gap ready

Sightlinq + SchemaShifts

Detection Data That Goes
Further With SchemaShifts.

Sightlinq stores every detection — structured, local, customer-owned. Customers who want to go further can adopt any of the six SchemaShifts-governed databases that support native vector search. SchemaShifts governs every schema change the vision data layer requires — from the first VECTOR column to third embedding model upgrade — with an immutable audit trail throughout.

Vector-capable · governed by SchemaShifts
🗄️
Apache Cassandra 5
Native VECTOR type + SAI ANN-OF queries. Billions of detection events + embeddings in one table. Best for air-gapped, high-throughput production lines.
cassachange
☁️
Google Spanner
10B+ vector search via ScaNN. SQL + vector + graph in one query. Best for globally consistent, multi-site deployments with regulatory audit requirements.
spanchange
🌐
CockroachDB
C-SPANN distributed vector indexing. REGIONAL BY ROW keeps embeddings close to where they were generated. pgvector-compatible SQL.
cockroachchange
⚡
SingleStore
HNSW + IVF in-memory vector search. HTAP — real-time detection writes and historical analytics in the same engine, no ETL.
singlestorechange
🔀
YugabyteDB
USearch HNSW + Vector LSM storage. Geo-partitioned tablespaces — per-site embeddings stay local, global view always available.
ybchange
📊
TiDB
HNSW + TiFlash HTAP + GraphRAG. Root cause chains across defect types via graph-augmented vector retrieval.
tidbchange
Schema governance included. As the detection model evolves — new defect classes, new embedding dimensions, new vector index types — the corresponding SchemaShifts tool versions every change with an immutable audit trail. No untracked schema drift. Rollback in one command.
What customers can build on a SchemaShifts-governed vector store
🔍
Semantic similarity search
Find all historical detections visually similar to a current defect — across batches, shifts, and sites — using ANN vector search. No SQL keyword matching. No hand-written queries. Similarity by meaning.
🧠
RAG-grounded AI tooling
Build AI chat or reporting tools that retrieve real detection records as grounding context before generating an answer. "Have we seen this pattern before?" answered by retrieved evidence, not model memory.
🔌
MCP integration
Expose the inspection history as an MCP tool — letting AI agents query detection vectors, retrieve similar cases, and surface root cause context across any MCP-compatible interface.
📋
Audit-ready schema evolution
Every VECTOR column addition, index change, and embedding dimension upgrade versioned by SchemaShifts. Immutable audit log. SOC2 and ISO 9001 ready. Who changed what, when, and was it approved — answered automatically.
Manager — "Have we seen this bridging pattern before?"
Customer RAG tool — Cassandra 5 ANN search (cassachange governed) 11 visually similar cases found. 9 on Line 2, batch L2406-C. Stencil temperature 2°C below threshold on all 9 shifts. Same pattern absent on Line 1. Recommend reviewing calibration logs 2026-07-14 to 2026-07-17.
✔ Customer-built tooling ✔ Schema governed by SchemaShifts ✔ Audit-ready
Full architecture deep-dive

Why teams choose Sightlinq

Built Different.
For a Different Kind of Team.

Most visual AI tooling was built for developers with cloud accounts, ML background, and time to integrate. Sightlinq was built for engineering teams who need to ship — with what they have, where they are.

🔌
No per-image pricing
Subscription and per-image models make sense at low volume. At production scale — tens of thousands of images per week — the economics break down fast. Sightlinq is a platform you own. Process a hundred images or a million. The cost is the same.
🏗️
Annotation, training, and deployment in one tool
Many teams stitch together a labeling tool, a training notebook, and a separate deployment service — and spend as much time on integration as on the actual problem. Sightlinq connects all three. Annotate, train, and deploy without leaving the platform or writing integration code.
✈️
Works where internet doesn't
Remote agricultural sites, production floors, offshore platforms, secure facilities — many of the environments where visual AI would add the most value are exactly the places with the least reliable connectivity. Sightlinq runs fully offline. Air-gapped is a first-class deployment mode, not an afterthought.
🎯
Domain-specific training presets
Generic augmentation settings are a compromise. A model for thermal seal inspection should train very differently from one counting livestock in an overhead pen. Sightlinq ships 14 domain-specific augmentation presets — each tuned for how images actually look in that environment. Better accuracy, out of the box.
🧠
AI reasoning that runs on your GPU
Most platforms that offer AI-powered insights send your data to a third-party API to generate them. Sightlinq runs a vision-language model entirely on your own hardware. Root cause analysis, shift reports, defect descriptions — generated locally, with your data never leaving your network.
📦
Runs on your existing hardware
No specialist hardware required to get started. A standard Windows or Linux server with a CPU is enough to train and deploy a production-quality inspection model. Add a GPU when you need faster training or real-time AI reasoning. The platform scales with your hardware — not the other way around.
Sightlinq ✓
Air-gapped deployment✓
Annotation + training + deploy unified✓
Edge device auto-ingestion✓
No per-image fees✓
On-premise AI reasoning✓
14 domain augmentation presets✓
Works fully offline✓
Cloud annotation tools
Air-gapped deployment✗
Unified pipelineAnnotation only
Edge ingestionManual upload
PricingPer image
On-premise AI✗
Works offline✗

Where Sightlinq is deployed

Any Camera.
Any Environment.

Sightlinq was designed for the physical world — wherever cameras are deployed in operational environments where data sensitivity, connectivity, or scale makes cloud tooling impractical.

🔬
Electronics & PCB
Solder defects, missing components, PCB trace faults. Inline AOI augmentation with models trained on your specific boards.
🌾
Agriculture & Livestock
Pig, cattle, poultry and sheep counting from overhead video or stills. Crop disease detection. Greenhouse monitoring. Models trained on your pens, your lighting, your breed — video frame interpolation cuts annotation time dramatically.
🍽️
Food & Beverage
Contamination detection, fill level inspection, packaging integrity. Colour-critical models for ripeness and quality grading.
🌡️
Thermal & Infrared
Seal integrity, heat anomaly detection, equipment monitoring. Domain preset preserves thermal gradient information during training.
💊
Pharmaceutical
Blister pack inspection, pill presence and orientation, label verification. Zero-rotation preset for fixed-orientation production lines.
🔩
Metal & Fabrication
Casting defects, surface finish faults, weld seam inspection. Grey-tone optimised presets for metal surface imaging under industrial lighting.
🏥
Medical Devices
Device component inspection, packaging integrity. On-premise deployment satisfies regulatory requirements — no patient or production data leaves the facility.
🏗️
Infrastructure & Safety
Pipeline, bridge and asset inspection via drone video footage. Construction site safety compliance monitoring. Utility equipment wear detection. Runs offline in the field — no internet required.
🤖
Robotics & Logistics
Bin picking, package damage detection, warehouse shelf monitoring. Full-rotation presets for overhead cameras where object orientation is random.
⚙️
Your use case
If you have a camera, images, and something to detect — Sightlinq can train on it. Custom domain presets available on request.
Get in touch →

Platform Capabilities

Everything in One Place.
Nothing You Don't Need.

Sightlinq covers the full lifecycle from edge capture to production model — without forcing you into a cloud subscription, a vendor's annotation format, or a training service you can't audit.

📡
Edge Image Ingestion
Uploader agents run on industrial PCs and single-board computers. Images and video files are content-hashed for deduplication and synced on a configurable schedule. Video is uploaded as a single file — CVAT extracts and manages frames automatically. No manual file transfers.
🗂️
Project & Team Management
Projects contain datasets, teams, and models. Annotators are scoped to specific projects via role-based access control. Managers see training status and annotation progress across all projects.
🖊️
Bounding Box Annotation
Full annotation interface built for production use — bounding boxes on images and video frames. For video, CVAT interpolates boxes between annotated frames automatically, cutting annotation time significantly. Annotators land directly on their assigned job from the manager UI.
🎬
Video Annotation & Training
Upload video files directly — the uploader detects them automatically and creates CVAT tasks with frame extraction handled internally. Use frame interpolation to annotate a keyframe every 10 frames and let CVAT fill in the rest. Trained models export as ONNX and run frame-by-frame on recorded video or live camera streams via inspect.exe.
🤖
AI-Assisted Auto-Label
After the first training run, the model pre-draws bounding boxes on new images and video frames. Annotators review and correct — each round, fewer corrections are needed as the model learns your specific defect types and conditions.
⚡
One-Click Model Training
Export annotations and start training from the manager UI in a single action. Training runs in the background on your own hardware — CPU or GPU. Live status updates in the browser. Notifications on completion.
🔁
Continuous Improvement Loop
Trained models deploy automatically as live inference functions. They immediately become the annotator for the next batch. Each iteration tightens the loop — less human time, better model performance.
🗄️
Enterprise Model Registry
Every training run automatically registers a versioned model in MLflow — an industry-standard model registry. Compare metrics side-by-side, approve versions before deployment, roll back to any previous version in under 60 seconds. Full audit trail from image to deployed model. Runs entirely on your own infrastructure.
🗄️
Pluggable Storage
Switch between local folder, NAS/SMB network share, and cloud object storage with a single configuration change. Raw images, video files, labeled datasets, and trained model weights follow the same folder structure regardless of backend.
📦
Multi-Format Model Export
Trained models export to ONNX, TensorRT, TFLite, CoreML, and TorchScript — covering Industrial PCs, Jetson devices, Raspberry Pi, and Apple Silicon. The same model, deployable anywhere.
🔒
On-Premise by Default, Cloud-Ready
Deploy fully on your own servers with no cloud account required — or connect to cloud storage for multi-site operations. Your data stays where you configure it. Role-based access controls and audit trails regardless of deployment mode.

Get started

Ready to train on
your own images and video?

Sightlinq is a production-ready platform for visual inspection, safety monitoring, and quality control. Designed for on-premise, hybrid, or cloud deployments, Sightlinq is ready to be deployed in real-world environments and integrated into your existing operations. Get in touch to see how Sightlinq can support your use case.

Request a Demo ← Back to Fluxor