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Intelligence & sensor fusion
An agent correlates ISR feeds, sensor telemetry, and field reports into a single sourced summary — flagging anomalies for an analyst, never acting on them unsupervised.
Agentic AI in defence & public safety
Agents built for zero-tolerance environments .
We build agents that fuse sensor and intelligence feeds, triage maintenance and security alerts, and draft mission-ready reports — with the audit trail and human sign-off a security review board will actually approve.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Defence and public-safety AI dies in accreditation, not the demo
Sensor fusion, maintenance triage and incident reporting are document-heavy decisions carrying a high cost of error. Accreditation is where these systems stall, not the demo: an agent that cannot show its work, cannot retain human command authority, or cannot survive a security review will never clear review. A rules engine cannot handle unstructured intelligence, and a chatbot cannot maintain the audit trail an accreditation process requires.
every recommendation logged, human authority retained
Use cases
Where agents earn trust in defence and public-safety operations
Workflows with a clear human-authority boundary and a mandatory paper trail.
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Predictive fleet maintenance
Agents read telemetry and maintenance logs across vehicle, aircraft, or equipment fleets and draft work orders ahead of failure — reviewed by a maintenance officer before dispatch.
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Cybersecurity alert triage
Correlates SIEM and SOC alerts across systems, drafts an incident report with evidence attached, and escalates high-confidence threats to an analyst — compressing triage time without removing the human decision.
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Emergency dispatch coordination
Aggregates incoming incident data and unit availability to recommend resource allocation — the dispatcher retains the final call, and every recommendation is logged.
Delivery path
From workflow audit to accredited production agents
TriStorm keeps security review and engineering aligned — classification and command-authority questions surfaced before full build commitment.
Map workflow and classification boundaries
We audit the target process, data classification, and network boundaries — ranking use cases by mission impact and accreditation risk.
- Workflow & classification audit
- Data boundary map
- Prioritised use case
Build, test and validate
We implement against real sensor, log, or incident data shapes, with an evaluation suite scored against your own protocols before any recommendation reaches a human.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and audit trail
Production rollout inside your security boundary, with every action logged and a structured handoff so your operations and security teams run the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them inside your own classification and review boundaries. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
What the mechanism delivers
Zero-tolerance validation, measured in production
These numbers come from shipped agentic AI engagements outside defence — Schmitt-Thompson (healthcare), Synera (engineering software) and Mixam (print on demand). They are here because they measure the rigor bar this work needs: staged validation instead of a single pass, traceable multi-step reasoning, and orchestration that holds up under load. Every figure links to the case study behind it.
Agents do not issue an order or clear a target. They gather, cross-check and draft the assessment that sits before a human decision. Every step is logged for review.
0 hallucinations
across 329+ nurse-validated scenarios
Not a defence deployment — Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
2 hrs → 3 min
to generate a validated workflow
Not a defence deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
95.4%
Success rate in workflow results
Not a defence deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
Client results
Proof from zero-tolerance-for-error deployments
We have not yet shipped a production agent inside a defence or public-safety organization. These are the closest available references — the same staged-validation and traceable-reasoning mechanism, shipped in healthcare, engineering software and print on demand.
FAQ
Agentic AI in defence & public safety, answered
Where does agentic AI actually fit in defence and public-safety operations?
In workflows with a clear human-authority boundary and a paper trail already required — intelligence and sensor fusion, predictive maintenance triage, cybersecurity alert correlation, incident and dispatch coordination. We deploy agents to gather, cross-check, and draft. A commander, dispatcher, or analyst stays the final decision-maker.
Do you build autonomous weapons or lethal-decision systems?
No. We build decision-support and back-office agents — analysis, triage, drafting, coordination. Command authority and any use-of-force decision stay with a human, by design, not as an afterthought bolted on for compliance.
What does a typical deployment look like?
An agent reads a high-volume, unstructured input (ISR feeds, maintenance logs, SIEM alerts, incident reports) cross-references it against your existing systems, and produces a sourced draft: a summary, a work order, a triage recommendation. It escalates when confidence drops instead of guessing. This is the same rigor bar our clinical triage build for Schmitt-Thompson Clinical Content met: zero hallucination events across 329+ validation scenarios in a zero-tolerance-for-error setting.
How do you handle classified or controlled data?
Agents deploy inside your existing network boundary (air-gapped, on-premise, or private cloud) and operate within your current access controls and clearance model. We map the data boundary before we design the agent, not after.
What happens when the agent is not confident in its output?
It escalates. Confidence thresholds and coverage gaps route to a human reviewer with the full reasoning trail attached, so nothing acts unsupervised on incomplete information.
Can this integrate with existing command, control, or dispatch systems?
Yes, through your existing APIs and data infrastructure, not a rip-and-replace. Agents are built to sit alongside current C2, CAD, or maintenance-management systems, not to replace the system of record.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three weeks. Full production rollout with monitoring and operator handoff follows the same TriStorm phases as any other Vstorm engagement.
Do we own the agent code after deployment?
Yes. Full source ownership of agent logic, integrations, and the evaluation harness — no proprietary runtime lock-in on what we deliver.
Start with one workflow
Map one mission or safety workflow worth automating
A 30-minute call identifies classification constraints, integration points, and a realistic path to a working agent your security review board will sign off on.