SYSTEM / AI AGENTS THINK · ACT ON-PREM · UAE DATA STAYS IN-COUNTRY
Sensors and the mesh sense. This is the other half: Python AI agents on local LLMs, running on hardware you own. They read the live stream, decide what matters, then act — raise the work order, extract the field, answer the question, draft the report. Cloud fallback is policy-gated with PII redaction, so your data stays on-prem and in-country. ZAMTEK develops the AI, supplies & integrates the hardware, and consults & trains. Wireless equipment we integrate is TDRA type-approved.
Local LLM inference on hardware you own — no per-token cloud bill, no data shipped out, keeps working when the link drops. A policy-gated fallback reaches a frontier model only when you allow it, PII redacted first.
See the stack →Detect the event, extract the document, answer from your own files, fire the dispatch, file the compliance report. Actions, not another dashboard for a human to refresh.
Book a pilot →Each family starts as a 2-week proof of concept on one workflow, then a 4–8-week pilot with measured ROI, then phased rollout on hardware you own. No capex. The model and your data stay on-prem in the UAE.
Agents sit on the live stream from your sensors, fleet and mesh, triage the noise, and surface only what warrants a response — a man-down signal, a leaking header, a threshold crossed. They decide what matters and escalate with context, instead of dumping every reading into an inbox.
Turn delivery notes, permits, invoices, gauge photos and inspection imagery into structured fields — no folders of scans, no manual re-keying. A local LLM reads the document, a vision model reads the picture, and the agent writes the record straight into your system.
Ask questions in plain language and get answers grounded in your manuals, SOPs, contracts and logs — with citations back to the source. Retrieval and generation both run on-prem, so the private document set never leaves the building to be indexed by anyone else.
The decision becomes an action: task the nearest unit, raise a CMMS work order, update the CRM, and send the right WhatsApp reply at the right moment. Because the agent reasons over the same live data your operation runs on, the actions stay in context — no human relay for every step.
The agents don't replace the sensing layer — they sit on top of it. Data comes in, a local model reasons, guardrails hold the line, and automation acts. Swap any layer; the method holds.
Events, telemetry, documents and images flow in from your sensors, mesh, fleet and systems of record — the raw stream the agents read. Nothing has to be uploaded to a cloud to be understood.
Python agents reason over a large language model running on hardware you own — retrieval-augmented against your own documents, with a vision model for imagery. No per-token bill, no data shipped out, works offline.
Between think and act sits the policy layer: PII redaction on anything that could leave the building, and a policy-gated fallback that reaches a frontier cloud model only for the workflows you allow. Privacy is a property of where the system runs.
The decision becomes an action: dispatch a unit, raise a work order, update the CRM, push a WhatsApp reply, draft the compliance report. Actions, not alarms — carried out without a person in the loop for every step.
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A Python program that reasons over a local large language model running on hardware you own. It reads your live data stream, decides what matters, and acts — raising a work order, updating a CRM record, sending a WhatsApp reply or drafting a report. It is the THINK and ACT half of sense → think → act, the layer no sensor or hardware vendor ships.
No. Inference runs on-premise on a local LLM, so operational data stays in-country by default. If a task needs a frontier cloud model, that fallback is policy-gated and PII is redacted before anything leaves — you set which workflows may use it and which never can.
Five families: event detection over live streams, document and image extraction into structured fields, RAG assistants that answer from your own documents, dispatch / CRM / WhatsApp automation, and audit-ready compliance report drafting. Each starts as one workflow and expands once it proves out.
ZAMTEK develops the AI agents, supplies and integrates the hardware they run on, and consults and trains your team to operate them. Any wireless equipment we integrate is TDRA type-approved.
A 2-week proof of concept on one workflow, then a 4–8-week pilot with measured ROI, then phased rollout on hardware you own. No capex, and the model stays on-premise throughout.
Pick the process that eats your team's hours — the report nobody wants to write, the inbox of alarms, the pile of scans to re-key. We put agents on local silicon on your site, point them at your data, and let them reason and act. Two weeks to proof, one quarter to measured ROI.
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