01
Citizen-request triage and routing
Reads incoming requests and routes them to the right department with reasoning attached, instead of a generic ticket queue.
Agentic AI in smart city
Agents for the coordination layer of urban operations.
We build agents that route citizen requests, review permitting documents, and monitor infrastructure signals — with the audit trail a public-sector team can actually trust.
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
Urban operations run across systems that do not talk to each other
Citizen requests, permitting records, and infrastructure sensor data typically live in separate systems, and resolving any one case means checking more than one of them. An agent reads across those sources, drafts a routing or resolution decision, and escalates the genuine edge cases — the same mechanism that works for any multi-system, document-heavy operation.
every routing decision traces to its source data
Use cases
Where agents earn trust in urban operations
Multi-system workflows with a clear escalation path to a case officer.
02
Permitting document review
Cross-checks permit applications against zoning and code requirements, drafting a sourced summary for the reviewing officer.
03
Infrastructure sensor monitoring
Reads sensor and maintenance data across city infrastructure, flagging issues before they escalate into service failures.
04
Compliance record-keeping
Maintains sourced, auditable records across departments for regulatory and public-records review.
What the mechanism delivers
Cross-system coordination that leaves a record
None of these figures come from a municipality. We have not shipped a production agent inside a city administration or a smart-city program, and nothing below pretends otherwise. They come from Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare), and they are here because they measure the three things a municipal workflow actually depends on: orchestration across systems that were never built to talk, real-time evaluate-and-route across a distributed operation, and staged validation where a wrong answer lands on a person. Every figure links to the case study behind it.
The agent does not approve a permit, close a citizen case, or dispatch a crew. It reads across the systems, drafts the decision with its sources attached, and hands it to the officer who signs off — and because public-sector accountability means the record matters as much as the outcome, every retrieval, draft and escalation is logged for later review.
Manual setup per multi-step workflow
2 hrs
Not a smart-city deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node.
95.4%
Success rate in workflow results
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
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.
Client results
Proof from production agentic deployments
We have not shipped a production agent inside a municipality or a smart-city program. These are the closest available references — the same cross-system orchestration, real-time routing, and staged validation with a full audit trail, shipped in engineering software, print on demand and healthcare.
“My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4% — so it definitely exceeded expectations.”
95.4%
Success rate in workflow results
Schmitt-Thompson · Clinical triage
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.
0 hallucinations
across 329+ nurse-validated scenarios
Delivery path
From workflow audit to a production agent
TriStorm keeps public-sector compliance and engineering aligned.
Map systems and workflow
We audit target processes, department-system boundaries, and data access — ranking automation candidates by impact and integration risk.
- Systems & workflow audit
- Data access map
- Prioritised use case
Build and validate the agent
We implement against real municipal data shapes, with an evaluation suite scored before any output reaches a case officer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring and audit logging, plus a structured handoff so your team runs 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 to your own service-request and permitting workflows. 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.
FAQ
Agentic AI in smart city, answered
Where does agentic AI fit into municipal and urban-infrastructure operations?
In workflows that coordinate across multiple city systems (permitting, citizen requests, infrastructure sensor data) where a decision benefits from cross-referencing more than one source before a case officer signs off.
How is this different from the case-management software cities already use?
Case-management software routes a ticket to a queue. An agent reads the request alongside relevant records and systems, drafts a resolution or routing decision with reasoning attached, and escalates genuine edge cases instead of routing everything to a person.
What municipal workflows are realistic first projects?
Citizen-request triage and routing across departments, permitting document review, and infrastructure sensor-data monitoring for proactive maintenance flags.
Can an agent handle citizen data responsibly?
Yes — agents operate within your existing access controls and audit requirements, with every decision logged and reasoning attached, designed for public-sector review from day one.
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 to six weeks, following the same TriStorm phases as any Vstorm engagement.
Do we own the system after it is built?
Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in, with your team trained to operate and extend it.
Start with one workflow
Map one urban-operations workflow worth automating
A 30-minute call identifies integration points, data access, and a realistic path to a working agent.