Hardened AI for Industrial Operations
Global supply chains and manufacturing lines produce massive amounts of unstructured data—from bills of lading to machine telemetry. We turn this chaos into deterministic operational state.
Built on robust Go microservices, Senrok's industrial agents ingest, verify, and route logistics data with deterministic execution.
Manifest Ingestion
Agents autonomously extract critical entities (SKUs, weights, hazardous material flags) from messy, unstructured PDF manifests or vendor emails.
Deterministic Validation
Extracted data is instantly cross-referenced against your ERP (e.g., SAP, Oracle) to ensure vendor compliance and inventory accuracy.
Automated Dispatch
Once verified, the payload is deterministically routed to the correct warehouse queue or fleet management API.
Built for the Factory Floor
On-Premise Ready
Unlike cloud-only AI wrappers, our Kubernetes-native orchestration can be deployed entirely within your secure VPC or on-premise industrial data centers.
Deterministic Execution
We don't use LLMs to make business decisions. LLMs extract data; strict code executes the routing logic. This eliminates catastrophic supply chain errors.
Real-time Telemetry
Every step of the agent pipeline is heavily instrumented. Operators get complete visibility into ingestion rates, validation failures, and API latencies.
How we help
Three ways to make complex operations easier to run.
Some teams need AI workflow automation. Some need better internal software for operators and reviewers. Some need cleaner backend and platform foundations before automation can work reliably.
Insights
AI & Product Engineering Insights
Practical essays for teams deciding what to automate, what to redesign, and what needs stronger software foundations first.
Approach
Senior builders, directly accountable.
No account managers translating the work from the outside. You work directly with the people designing and building the system, so strategy, architecture, and delivery stay connected.
Map the real workflow
We study the decisions, exceptions, handoffs, and unofficial workarounds your team already performs, not the idealized process in a slide deck.
Design the operating loop
Tools, memory, rules, review points, permissions, and evaluation criteria are defined before a single interface pixel is drawn.
Ship beside operators
The first release launches with the people who will use and trust the system. We instrument, iterate, and expand from evidence.
Leave control behind
Documentation, observability, and maintainability are part of delivery, so your team can operate and improve the system after launch.
Frequently Asked Questions
How does AI-based defect detection work on a production line?
Sensor and image data is streamed into a multi-modal classification pipeline that runs a constrained model against your defect taxonomy. Findings below the confidence threshold are routed to a human reviewer with the relevant context already attached, and confirmed findings write to your MES or quality system with the full evidence chain.
Can the system produce immutable traceability for audits?
Yes. Every input, inference, and downstream action is written to a write-once audit log. The log is signed and timestamped, so a regulator can verify that a specific defect decision was made at a specific time with a specific model version.
How do you keep AI from blocking the line on a false positive?
The orchestrator uses a dual-path design. Low-confidence findings are surfaced in the review queue without halting the line, while high-confidence findings trigger the configured downstream action. The thresholds and the fallback path are tuned per work cell during deployment.
Work with us
Systematize your supply chain
Walk us through your workflow and the systems involved. We'll provide an objective assessment of whether AI automation or traditional engineering is the best path forward.