Fluxor builds the products that take AI from raw data to production, inside your own Google Cloud or on your own hardware. Your data stays yours, people sign off on what matters, and every model leaves evidence.
Runs in your Google Cloud·On-premise·Air-gapped
Forward-Deployed Engineering In
Runs Where Your Data Already Lives
Fluxor Engineering
AI now gets built faster than teams can explain it. Agents write the pipelines, the prompts and the infrastructure, and too often nobody can say what it costs or what happens when it fails.
Our forward-deployed engineers work inside your team, from the first question (which problem is worth solving?) to systems in production that your people understand and own. Not a handoff: we build it with you, on Google Cloud or on your own infrastructure, and every engagement sharpens the products.
Before any model or tool: the business problem, what success looks like, the data you really have, and whether AI is the right answer at all. Most failed AI projects failed here.
Landing zones for AI on Google Cloud or on-premise: identity, encryption under your own keys, private networking, data residency and audit, built in on day one rather than bolted on before the audit.
Agents that do real work with your tools and data, within limits you set: MCP and tool integration, guardrails, evaluation before release, and a person in the loop where it matters.
Training, release, monitoring and retraining you can audit: versioned data and models, evaluation gates, drift checks and evidence for every release. Pipewright, where it fits.
Language models in production: managed or self-hosted serving, retrieval (RAG), versioned prompts, evaluation sets, and cost and latency you can see per request.
Keeping AI healthy and affordable once it is live: monitoring, alerting, incident response and cost control, plus AI applied to your own operations where it pays back.
Every engagement ends with a team that can run it without us. Hands-on training for engineers and technical leads on how models, agents and pipelines really work, on your own stack and your own use cases.
The Fluxor Flywheel
None of our products started as a pitch deck. Pipewright, Sightlinq, PiEdge, SchemaShifts and Threadlinq each solved a hard problem for a real client first, and became a product once the same problem showed up again. Engineering work finds the pattern; products scale it. That loop is how a focused team ships five products, and how we choose the next one: by demand we have already seen, not by guesswork.
Sovereign by Design
Regulated teams cannot ship their data to someone else's AI. Every Fluxor product runs inside your own Google Cloud project or entirely on your own infrastructure, and nothing leaves unless you decide it should.
Pipewright runs governed machine learning inside your own Google Cloud project, encrypted with your keys and behind your perimeter. Sightlinq can use Google Gemini Enterprise Agent Platform as a model backend and Azure Blob, S3 or GCS for storage. SchemaShifts governs your database wherever it is hosted. Multi-site and hybrid are a configuration change, not a re-architecture.
Air-gapped plant, factory LAN or a remote site with patchy connectivity: Sightlinq, PiEdge, SchemaShifts and Threadlinq run entirely on your own infrastructure. No cloud account, no per-event metering, no external calls.
The Fluxor Platform
Each product solves one hard problem completely and stands on its own. Together they cover the AI lifecycle: data at the edge, vision and documents, the databases underneath, and governed models in production. Adopt one, add the next when you are ready.
The control plane for machine learning on Google Cloud. Each use case is one folder of YAML and pipeline code; Pipewright builds, releases, runs and monitors it, with every release pinned and verified. Drift opens a decision for named approvers instead of retraining blindly, and every model leaves an evidence trail an auditor can read. Tested releases and approved models reach production unchanged. Available now in early access, with a GKE blueprint on the roadmap; Enterprise adds a private-by-default blueprint, keyless CI, evidence packs, Fleet View across every environment, and support from Fluxor.
Visual AI from camera to production: capture, annotate with AI assistance, train on your own GPU or CPU or a cloud backend such as Google Agent Platform, and deploy back to the edge. For any team with a camera and a detection problem, from factory lines to field operations.
Decisions where the data is born. PiEdge runs SQL-style rules over sensor and event streams on the device itself, with a REST API and a role-based dashboard, and nothing to install beyond the standard library.
The governance layer underneath your data: versioned migrations, immutable audit logs and distributed locking across Cassandra, Spanner, DynamoDB, CockroachDB and more. As AI workloads add vectors and embeddings, every schema change stays reviewed and on record.
Document AI for compliance. Threadlinq reads the certificates, inspection reports, test records and manuals you already have, scans and phone photos included, and shows every checklist item as in date, due soon, expired or missing, linked to the exact page it came from. On-premise, in your own cloud, or hosted by Fluxor.
Sightlinq in Production
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.
No GPU required. Intel i7-1355U laptop, 30 epochs, 83 minutes. The missing_hole class reached mAP50 0.894: production quality for inline inspection, on commodity hardware.
Better Together: Sightlinq + SchemaShifts
Sightlinq stores every detection: structured, local and owned by the customer. Teams that want to go further, with vector search across millions of inspections, RAG-grounded tooling or MCP, need a database built for it. Six of the nine databases SchemaShifts governs support native vector search, and SchemaShifts manages every schema change the vision data needs as the detection model evolves.
This is how the Fluxor products compound. Sightlinq produces the data; SchemaShifts makes the database that holds it safe to evolve. Each is useful on its own, and more valuable together.
Six of the nine databases SchemaShifts governs support native vector search. Whichever one a customer adopts (Cassandra 5 for air-gapped production lines, Spanner for global multi-site deployments, SingleStore for sub-millisecond HTAP), SchemaShifts governs every schema change: VECTOR columns, ANN indexes and embedding dimension upgrades, each recorded in an immutable audit log.
Sightlinq does not change. The database gains the governance it needs for vector search, RAG and MCP as the detection model evolves and new defect classes arrive.
Full Technical Deep-DiveVECTOR column type + SAI ANN-OF queries + HNSW indexing. Billions of detection events stored alongside their embeddings, with no joins and no separate store. Best fit for air-gapped, high-throughput production lines.
GA vector search to 10B+ vectors via ScaNN. SQL + graph GQL + vector in one query. Best for globally consistent, multi-site deployments with regulatory traceability requirements.
Distributed vector indexing via C-SPANN (v25.2). REGIONAL BY ROW keeps embeddings close to where they were generated. pgvector-compatible SQL. Multi-region data residency aware.
HNSW and IVF via Faiss, in-memory for sub-millisecond ANN search. HTAP handles real-time detection writes and historical trend analytics in one engine, no ETL required.
USearch HNSW + Vector LSM storage. pgvector-compatible. Geo-partitioned tablespaces keep per-site embeddings local while a global aggregate view remains queryable centrally.
Native VECTOR type + HNSW index + TiFlash HTAP. GraphRAG support: knowledge graph traversal (defect → batch → supplier) combined with vector similarity for root cause retrieval.
Teams that want vector search, RAG or MCP on their Sightlinq detection data can adopt any of the six SchemaShifts-governed databases that support it. Our engineers can help you choose the right one for your deployment, air-gapped, multi-site or cloud, and put the schema governance in place.
SchemaShifts
Versioned migrations, immutable audit logs and distributed locking across Cassandra, Spanner, DynamoDB, CockroachDB, YugabyteDB and more. Docs and downloads live on its own site.
Sightlinq · Visual AI Platform
Annotation, training and deployment in one interface that runs on your network, on your hardware, with your data.
A 500-image batch that takes 8–10 hours to annotate by hand takes 30 minutes by Round 4. By Round 5 it is a spot-check.