Who we build for
AI for Municipalities
Municipal AI work divides into three problems: understanding what happens in public space, making decades of paper records searchable, and handling the volume of correspondence residents send.
All three carry constraints a commercial deployment does not. Processing usually has to stay inside a defined jurisdiction, decisions affecting residents must be explainable and appealable, procurement is subject to review, and personal data has retention limits set in law rather than by preference. Every service below can run inside your own data centre or in a cloud account your authority already holds, with data residency, network isolation and retention configured per deployment.
Services that apply
The services municipalities commission most often, grouped by the problem they solve. Each links to its own page with the models, hardware and throughput behind it.
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Public space and transport
Counting and flow measurement that inform service decisions without identifying anyone.
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Crowd counter
Occupancy at events, squares and transport interchanges, for safety limits and capacity planning. Nobody is identified.
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People entrance / exit counting
Footfall at libraries, leisure centres and civic buildings, measured continuously rather than sampled by hand.
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Queue length / dwell-time analytics
Waiting times at service counters and licensing desks, which is the measurement that justifies staffing changes.
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General object detection and tracking
Vehicle and cycle counts, illegal dumping, blocked accesses and obstructed pavements from existing camera footage.
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Live object detection / tracking
The same detection on live feeds where an operator can act — a barrier breach, a vehicle in a pedestrian zone.
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Safety / PPE detection
Compliance auditing on municipal works sites and depots, by area, shift and contractor.
Civic archives and records
Turning paper and scans into records that can be searched, cited and published.
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PDF/document to structured JSON
The groundwork for every archive project: headings, tables, reading order and figures recovered as data.
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Handwriting recognition
Registers, minute books and historical records that no printed-text reader can handle.
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Table extraction
Budget schedules, planning registers and statistical returns recovered as rows and columns.
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Form extraction
Completed applications and permits read field by field, including ticked boxes and handwritten entries.
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Semantic search
Officers finding the relevant policy or precedent by meaning rather than by guessing the filed wording.
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Document question answering
Direct answers from the archive with the page and passage cited, which is what makes an answer defensible.
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Multilingual translation
Notices, forms and correspondence in the languages your residents actually use, processed in-house.
Correspondence and disclosure
Handling what residents send in, and what has to be released back out.
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Customer-support ticket routing
Resident enquiries routed to the right department on arrival, with a priority, instead of being passed around.
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Text summarization
Long submissions, consultation responses and objections reduced to the substance for a committee pack.
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Sentiment / intent analysis
Distinguishing a complaint from a query from an escalation, so urgent cases are seen the day they arrive.
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PII detection and redaction
Personal data found and masked before a dataset is shared, analysed or published.
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Document redaction
Freedom-of-information and subject-access releases where removed content must be genuinely gone, not hidden behind a black box.
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Private chatbot / enterprise assistant
An internal assistant over your own policies and procedures, answering staff questions without any material leaving the authority.
Bring your own model
Authorities that have already commissioned a model — a local traffic classifier, a records model built with a university partner, a supplier’s system you now want to run yourselves — can run it on Super-GPU infrastructure rather than rebuilding it.
We help you select the right hardware, containerize and deploy the workload, build the processing pipeline and define the infrastructure required to scale it.
Your model. Your data. Your deployment choice.
Bring your existing GPU containers
We support standard Linux GPU workloads including:
- Docker
- OCI images
- NVIDIA CUDA
- vLLM
- TensorFlow
- PyTorch
and other GPU-enabled environments. Images can be uploaded directly or pulled from registries including AWS ECR, Docker Hub, GitHub Container Registry, Google Artifact Registry, Azure Container Registry, Harbor and private registries.
Where it runs
Deployment is a procurement decision, not a technical one, and for municipal work it is usually the decisive one.
On premises, in your AWS account, GCP project or OCI tenancy, on Super-GPU Cloud, or across a hybrid environment that keeps latency-sensitive work local and reaches for cloud GPU capacity when scale demands it. You control where data is processed, stored and retained.