Value Proposition
In plain english - Pulse's workflow engine allows tiny AI models to do repeatable work at a cost and scale that rivals writing backend business logic in code. This flexibility and relatively low cost allow you to configure workflows that match your use case and change gracefully with your organization. We have hero use cases at the end as well, the next section can be skipped if you're non-technical.
Technical Advantages
Rather than "agents" or "chatbots" Pulse uses Workflows and refers to Large Language Models in them as "Workers." Our workflow engine allows much smaller worker models to perform tasks that usually require a much larger model. For example, a task that typically requires a 122B+ parameter model like GPT 4.1 outside of Pulse can be converted into a workflow that can be completed by a 4B or 8B parameter model and configured to run on a schedule or automatically against new incoming data.
Workflows support webhooks for easy integration with teams, slack, zapier, ntfy, and other popular enterprise applications. They can be combined with Pulse's best-in-class OCR and VQA capabilities to run workflows on new incoming data from the ingest tool.
"Hero" Use Case Examples
- Verticals where chatbots become time consuming and existing bespoke solutions are limited
- Inventory and WMS System for small medical supply warehouse team
- Intake at car dealership/auto shop
- Visual QA For Industry
- Restaurant order quality, prep area/lobby status monitoring
- Text-from-surveillance-video
- Complex OCR -> Templating Tasks
- converting ugly tax forms into personalized email templates
- Extracting key information from a badly printed traffic citation for use by a paralegal or clerk
- Data Classification and Extraction
- Tagging incoming alerts from a monitoring system based on real severity nodes from administrators
- Extract a static set of shared key stats (duration, size, etc) from incoming metrics in widely varied formats
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