Problem
Teams hold answers across PDFs, photos, calls, and video—but tools still treat each format as a separate silo.
Solutions
Applied AI
Applied AI is how we put intelligence into the product itself: vision and multimodal evidence, data platforms models can trust, custom platforms with AI in the architecture, and digital twins that give operators one picture of the facility or network. We advise on the shape, then build so sensing, data, and models ship with the product—not as a side experiment.
Explore multimodal intelligence
Mixed evidence, one controlled AI workflow
Vision-language models, speech-to-text, and multimodal retrieval so teams query across file types in one controlled workflow. We keep the same access rules and human review for sensitive or low-confidence outputs—so mixed evidence becomes a decision path, not three separate tools.
What we deliver
A single AI workflow that reads mixed evidence under your access rules and returns grounded, reviewable answers.
How it works
- Vision-language models for documents, photographs, and video context
- Speech-to-text and image understanding inside approved permissions
- Multimodal retrieval so teams query across file types in one pass
- Human review for low-confidence or sensitive combined outputs
Outcomes
- Faster investigation when evidence is mixed
- One permission model across modalities
- Less manual stitching between tools
Explore computer vision
Turn image and video streams into operational events
Object and event detection, occupancy, intrusion, and facility monitoring that turn streams into alerts and workflow triggers. Edge when latency demands it; biometric-enabled workflows only where legally authorised. We tune thresholds with your operators so the system raises events they can act on.
Problem
Cameras and image feeds produce noise until someone watches—too late for operations that need events, not archives.
What we deliver
Detection pipelines that turn streams into alerts, counts, and workflow triggers—including edge when latency cannot wait.
How it works
- Object, event, and anomaly detection in images and video streams
- Occupancy, crowd, and intrusion conditions for facility operations
- Edge deployment where latency or connectivity requires local inference
- Face and biometric-enabled workflows only where legally authorised
Outcomes
- Operational events instead of unused footage
- Lower false-alarm load with tuned thresholds
- Works in cloud, private cloud, or on the edge
Review data readiness
Data platforms AI can actually use
Ingest, quality, lineage, and access rules so RAG, reporting, and models consume approved operational data. We connect the systems you already run and make ownership, freshness, and permissions explicit—so AI is grounded in data you can defend, not a generic lake diagram.
Problem
Models and RAG fail when operational data is siloed, stale, or missing clear ownership and access rules.
What we deliver
Pipelines, quality checks, lineage, and BI so assistants and models consume approved operational data.
How it works
- Ingest from operational systems with quality and freshness checks
- Lineage and access rules aligned to roles and business functions
- Warehouses and BI that feed RAG, reporting, and scoring
- Governance so AI cannot see what the user cannot
Outcomes
- Retrieval and models grounded in current data
- Clear ownership for sources and permissions
- Analytics that connect to AI workflows, not a separate stack
Discuss a platform build
Build the product so AI is part of the architecture
Custom platforms for awards, marketplaces, ops, and industry workflows—APIs, roles, data models, and AI features shipped together. Discovery, architecture, and delivery stay one engagement so intelligence is part of the product model from the first release, not a retrofit.
Problem
Bolting AI onto a finished product creates fragile demos. Durable AI needs data models, APIs, and controls from day one.
What we deliver
Product and operational platforms with sensing, data, AI features, and integrations designed into the architecture.
How it works
- Product discovery and domain modelling with AI use cases in scope
- APIs, roles, and data models that support retrieval and actions
- AI feature layers—assistants, ranking, document flows—wired to the product
- Launch, observability, and a backlog for the next AI capability
Outcomes
- AI that fits the product, not a side experiment
- Faster iteration on new AI features
- One codebase for workflow, data, and intelligence
Explore digital twins
An operational picture of a facility, city, or network
Combine spatial data, IoT, analytics, and models for planning, infrastructure management, and scenario analysis. Assistants and vision sit on the same twin state so planners and operators share one picture—and AI acts on that picture, not on disconnected reports.
Problem
Leaders need one picture of assets, sensors, and events—not separate GIS, IoT, and BI tools that never agree.
What we deliver
Twins that fuse spatial, IoT, and operational data with AI assistants and vision for planning and live ops.
How it works
- Consolidate spatial, demographic, transport, and asset data for planning scenarios
- Monitor assets, maintenance, and operational events across infrastructure
- Analyse mobility, environment, and service patterns for response
- Layer AI assistants, vision, and forecasting for operational teams
Outcomes
- Shared operational picture for planners and operators
- Faster scenario and investment decisions
- AI that acts on twin state, not disconnected reports
Where twins land
Urban and territorial planning
Consolidate spatial, demographic, transport, environmental, and development data to support planning scenarios and investment decisions.
Infrastructure operations
Monitor assets, maintenance needs, service conditions, and operational events across distributed infrastructure.
Mobility and transport
Analyse traffic, public transport, parking, logistics, and mobility patterns to improve planning and operational response.
Environmental monitoring
Combine sensor, geospatial, and external data to track environmental indicators and identify priority interventions.
Public services
Improve service planning and delivery by connecting citizen requests, operational data, document workflows, and management dashboards.
AI-assisted city operations
Use AI assistants, computer vision, forecasting, and data analysis to support operational teams and municipal decision-makers.
Plan the build with our engineers
Describe the process, data sources, and deployment constraints. We will advise on architecture, then develop the integrations and success metrics with you.
Talk to our AI engineers