Fleet oversight at scale
Watch dozens of turbines, compressors, or treatment trains in parallel. Notice when one of them starts trending differently from its peers — before the alarm fires.
What JARVIS v4 is
JARVIS v4 puts an autonomous engineer next to every system you care about. Not a chatbot. Not a dashboard. An operator that knows its scope, holds context across days and weeks, and takes action where it’s safe to act.
Each agent is dedicated to one system or one fleet — a turbine site, a pipeline corridor, a treatment plant, a cluster of edge devices. It speaks the protocols those systems already use, parses the data they already produce, and reports the way humans already work: through chat, in plain language, with the receipts attached.
Continuous oversight
Always watching. Tag values, alarm streams, log files, network health, asset state — checked at the cadence the system actually needs.
Engineered action
When something moves outside spec, the agent investigates, summarizes, and engages on-call — with the diagnostic already attached.
Native to your team
No new dashboards. Agents talk in the chat channels your engineers already live in — per-system, on-topic, on-the-record.
What JARVIS connects to
An agent is only useful if it can reach the system it owns. JARVIS v4 was built for the messy, multi-vendor reality of industrial operations — not one greenfield stack.
PLCs & PACs
GE PACSystems RX3i, Siemens S7, Rockwell ControlLogix, Mitsubishi, Schneider — through the same protocols your integrators already use.
- Tag & register reads
- Alarm & event streams
- Program revision & change detection
SCADA & historians
Ignition, WinCC, FactoryTalk, OSIsoft PI, AVEVA — agents query the historian, parse the alarm log, and reason about trends across hours and shifts.
- Historian queries & trend math
- Alarm rationalization & flood triage
- Shift summaries & anomaly call-outs
Sensors & edge devices
From smart sensors and gateways to remote I/O and embedded controllers — over MQTT, OPC-UA, Modbus, CAN, REST, or vendor SDKs.
- Edge telemetry aggregation
- Calibration drift & sensor health
- Field-device firmware status
Infrastructure & OT networks
Linux servers, edge compute, industrial switches, firewalls, time sources — the substrate everything else runs on.
- Service health, disk, memory, log scrapes
- Switch ring & PTP/NTP discipline
- Backup verification & recovery drills
What our agents actually do
An agentic engineer is judged the same way a human one is — by the work it ships and the trouble it catches. Common scopes JARVIS agents take on for SW7FT clients today:
Alarm triage & first response
When alarms fire, the agent is already pulling the relevant tag history, recent operator actions, and similar past events — so the human on call gets a briefing, not a klaxon.
Shift & site handover
End-of-shift summaries written in plain language: what changed, what alarmed, what was acknowledged, what’s still open. Posted to the channel the next shift already reads.
Anomaly detection & baseline drift
Quiet, persistent comparison against historical baseline — vibration creep, valve cycle counts, network jitter, historian gaps — surfaced before they become events.
Knowledge capture & institutional memory
Every fix, every diagnostic, every “why did we do it that way” conversation appended to the agent’s context. The system remembers, even when the team turns over.
On-call partner for engineers
The agent does the boring half of incident response: pulls logs, compares baselines, drafts the timeline. The human keeps the judgement, signs off the action.
How SW7FT uses JARVIS today
JARVIS v4 is not a research project — it’s how SW7FT delivers ongoing operational support across multiple complex client systems at the same time.
We run a fleet of agents internally. One owns the LNG turbine systems we commissioned. One watches the mine SCADA layer. Others are scoped to client-specific OT networks, custom plant software, edge gateways, or QNX-based product lines we maintain. Each agent reports into its own channel; our engineers cycle through them the way a senior would cycle through field reports.
The result is real: we cover more ground without losing the rigour. Issues we used to learn about from a Monday-morning email, we now get a Sunday-evening heads-up about — with the relevant tag plot already attached.
Why we say “agentic” and not “AI”
Most products labelled “industrial AI” are dashboards with a chat box on the side. They surface insights; the human still has to act on them. JARVIS agents are different in one specific way: they have execution authority within their scope.
That means an agent can pull a historian sample, run a diagnostic script, query a switch, parse a log file, or post a summary — on its own, when the moment calls for it. Anything outside its remit is escalated to a human, with the work already half-done. The boundary is hard, the audit trail is permanent, and the human is always the final word.
That’s the difference between an assistant and an engineer. JARVIS agents behave like junior engineers — tireless, on-shift, and accountable to a senior.
Built for industrial trust
OT environments don’t reward cleverness. They reward systems that operators understand, that can be audited after the fact, and that fail in predictable ways. JARVIS v4 was designed against that bar.
Per-system isolation
One agent, one scope. An incident on one client’s system can never reach another. Tear-down is a single command.
Permanent audit trail
Every observation, every action, every reply written to disk. Reconstructable hours, days, or months later — for review, training, or post-incident.
Humans in the loop by design
Agents act inside a clearly drawn box. Anything beyond it surfaces to a human in the channel, with context attached — never silently.
Self-hostable
JARVIS v4 runs on infrastructure you control. No client data leaves your environment unless you decide it should.
Deploying JARVIS in your environment
JARVIS v4 is offered as a managed engagement. SW7FT scopes the agent fleet, integrates with your control systems and chat platform, runs the agents alongside your team, and trains your engineers to extend them.
Typical first-engagement shape:
- Discovery (1–2 weeks) — map the systems, identify high-leverage scopes, agree on the boundaries each agent operates within.
- Pilot agent (2–4 weeks) — one scope, one agent, real channel, real escalations. Validate the behaviour against actual operations.
- Fleet expansion — replicate the pattern across additional systems as confidence builds. Add capability modules as new use cases emerge.
- Ongoing operation — SW7FT runs the platform with your team or hands it over fully, depending on what fits your model.