Full-stack software · Search · Machine learning
LAYIQ brings job discovery, personalized recommendations, AI-assisted applications, and tracking into one product across web, iPhone, and Android. Underneath is a full software platform: Python services, PostgreSQL, OpenSearch, and a learning system that responds to each person’s choices.
Public discovery, a signed-in web workspace, and dedicated iPhone and Android apps.
Persistent accounts, search settings, workflows, and application history.
Filtered retrieval and recommendations informed by member feedback.
Authentication, billing, error visibility, and product analytics.
LAYIQ extends into dedicated iPhone and Android apps. Job Match, Fit Analysis, application materials, and tracking connect to the same account and server-owned data.
The LAYIQ experience, available through Google Play.
View on Google Play (opens in a new tab)
Screens from LAYIQ’s Android app listing. Select a screen for a closer look.
Public discovery and a personal workspace share the same product foundation.
One coherent product can support discovery, conversion, and ongoing customer use.
Durable records keep the product consistent across searches, devices, and background work.
Business rules and customer history stay anchored in a dependable data model.
A dedicated search layer handles retrieval while PostgreSQL retains the canonical records.
Rich search can grow without forcing the application database to do every kind of work.
Job Match uses learned preferences to decide which opportunity to show next.
Recommendations respond to individual choices instead of relying on the same ordering for everyone.
The recommendation system includes the machinery to improve models and control their release.
Machine learning becomes an accountable product system with measurable promotion criteria.
Fit Analysis and document assistance turn source material into useful next actions.
AI has a defined job, supported inputs, and a clear place in the customer journey.
The product connects recurring work and commercial access to each customer’s account.
A working platform can support recurring value and paid customer relationships.
Search visibility and operational feedback are part of the implementation.
The product can be found, measured, diagnosed, and improved after launch.
Discoverability needs its own engineering: the right public information, the right metadata, and clear paths into the experience.
Public jobs, companies, guides, and tools create useful entry points beyond the homepage.
Job and product markup is built from supported facts, with validation before publication.
Canonical URLs, sitemap generation, and robots rules distinguish public content from private workflows.
Product events and error reporting help explain how visitors reach value and where they get stuck.
Search platforms, recommendation products, subscription software, data-rich marketplaces, AI-assisted workflows, and internal tools with complex business rules.