Highlighter
Silverpond · Video
Highlighter AI is an enterprise perception system from Silverpond that connects imagery, video, LiDAR and sensor data with human judgement and AI agents, so teams see what's happening and act in time.
About Highlighter
What Is Highlighter AI
Highlighter AI is an AI perception platform built as an Enterprise Perception System. It pulls observations from cameras, video, LiDAR, IoT sensors, documents and existing business systems into one operational environment, then helps teams work out what's happening, why it matters and what to do next.
As computer vision for enterprise operations, the platform goes past plain detection. The company behind it is Silverpond, an Australian AI firm. Highlighter is aimed at operations teams, asset managers and transformation leaders in industries where perception tasks run at high volume, such as asset inspection, defect detection and real-time monitoring. AI agents handle continuous watching at scale, while people review, validate and stay accountable for the decisions that matter.
The biggest limitation is fit. This is not a self-serve tool you sign up for and start using in an afternoon. Pricing is quote-based, the entry point is a structured pilot of three to four months, and the value depends on how well your existing cameras, sensors and systems can feed it. Small teams without that infrastructure won't get much from it.
Getting Started
- Register for a demo and describe the operational question you need the platform to answer.
- Run a structured pilot of roughly three to four months with defined success criteria and implementation support.
- Connect the relevant data sources, from cameras and sensors to enterprise systems, through APIs and connectors.
- Configure detection types, schema tags and human-in-the-loop workflows in the visual interface.
- Review AI assessments, approve or correct the outputs, and expand the workflow to new use cases.
Product Information
A quick look at Highlighter's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Operations teams
- Asset and quality managers
- Engineering teams
Tasks
- Asset inspection and defect detection
- Real-time operational monitoring
- Data review at scale
Scenarios
- Running inspections across many sites
- Auditing assets in regulated industries
- Automating a repeatable operation
Key features
Multi-source perception and data ingestion
Highlighter connects imagery, video, LiDAR, IoT telemetry, documents and enterprise systems in a single operational environment. The point is to stop observations from being scattered across tools. Teams see the same picture whether the signal comes from a camera, a sensor feed or a business system.
Human-in-the-loop review
People review, correct and approve AI predictions rather than trusting them blindly. Every decision stays traceable, and corrections feed back into the model over time. This keeps accountability with your specialists and lines the workflow up with governance and regulatory obligations.
No-code workflow configuration
Business users configure detection types, schema tags and workflows through a visual interface, no scripting needed. Developers aren't locked out either. They extend the same platform with integrations and custom code when a use case calls for it.
AI agents for continuous awareness
AI agents watch across more assets, observations and workflows than a team could cover by hand. They surface changes and exceptions worth a human's attention instead of flooding reviewers with noise. Coverage grows without adding headcount.
Edge, cloud and hybrid deployment
Highlighter runs in the cloud, on the edge or as a hybrid of both. Edge AI deployment lets organisations keep capturing data and running AI inference offline, then sync results to the central platform once connectivity returns. That matters for remote sites, vehicles and field operations.
API, SDK and enterprise integrations
A documented GraphQL API and Python SDK handle secure integration. Highlighter connects with platforms such as Power BI, SAP, SharePoint and Azure Data Factory, so perception workflows slot into systems a business already runs.
Operational memory and audit trail
The platform links observations with recommendations, decisions, evidence and outcomes. Over time that becomes an auditable operational memory that improves with each verified action, and it gives teams the context to understand the next observation faster.
Pros and cons
Pros
- Handles multiple data types, from imagery and video to LiDAR and IoT telemetry, in one platform.
- Human-in-the-loop design keeps specialists accountable for critical calls instead of handing decisions to the AI.
- No-code configuration lets operations teams set up workflows without waiting on engineering.
- ISO 27001-certified with encryption, role-based access and audit logging, which suits regulated industries.
- Data stays portable: annotations, media and metadata export in JSON, CSV and Avro.
Cons
- No free plan or public self-serve tier, so you can't try it before committing to a sales process.
- Pricing is quote-based, which makes budgeting hard without talking to the vendor first.
- The realistic entry point is a three to four-month pilot, so it's a poor fit for teams wanting a quick test.
- Value depends on existing camera, sensor and system infrastructure. Without that, there's little to connect.
Frequently asked questions
It's an Enterprise Perception System built by Silverpond. It brings observations from imagery, video, LiDAR, sensors and enterprise systems into one operational environment, then connects them with AI agents and human expertise so teams understand what's happening and what to do next.
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