Model API
Useful when managed capability and rapid iteration matter more than offline operation or full model control.
AI & Machine Learning Development
Use AI where it improves the workflow—and make the result measurable and maintainable.
HerbDev helps determine whether a business problem needs AI, what should stay deterministic, where inference should run, how results should be evaluated, and how the feature fits into an application people can operate.
Discuss Your ProjectWhere should the model run?
Hosted custom model
Useful when the workflow needs controlled deployment, specialized evaluation, or a model tailored to the available data.
On-device model
Useful when privacy, latency, offline use, bandwidth, or direct mobile integration matters and device constraints can be met.
The appropriate choice depends on data, privacy, latency, reliability, cost, device capability, evaluation, and the consequences of an incorrect result.
AI Implementation Guidance
Choose where AI belongs in a product workflow, what should stay deterministic, what needs human review, and what should not be automated yet.
AI Workflow Architecture
Design prompts, tools, data flow, routing, storage, monitoring, and fallback behavior around the actual operational need.
AI Operational Strategy
Plan how AI systems are evaluated, maintained, reviewed, secured, and improved after launch.
AI-Assisted Code Repair
Clean up AI-generated code failures, unstable agent-built features, broken tool use, and implementation drift in mobile or cloud-backed applications.
AI as a secondary focus
The goal is not an AI demo. The goal is a system that can be owned.
HerbDev can help choose a model, design a workflow, review AI-generated code, or stabilize an AI-assisted build. The same standard applies: the project must become easier to understand, easier to release, and easier to maintain.
Relevant engineering evidence
Engineering research project
OrganicVision
Research workflows for capture, annotation, datasets, evaluation, reproducibility, and explicit evidence states.
Read the case study →Architecture examples
Human-reviewed AI workflows
Claims and scheduling examples show confidence, audit, routing, integration, and approval boundaries without implying customer deployment.
View workflow evidence →Manage browser learning data
“Remove my test data” clears all AI 101 and ML 101 scores, reviews, project checklists and rating selections, plus saved vocabulary and recent history in this browser. Test entries cannot be separated from other learning progress. Cookie preferences, security protections, submitted feedback, contact emails and past analytics are kept.