AI-POWEREDMATCHING PIPELINES ON GCP
AI-POWERED
MATCHING PIPELINES ON GCP
A repeatable model for startups solving two-sided marketplace matching problems
THE CHALLENGE
Startups building two-sided platforms
where matching between users, services,
or opportunities is core to the value prop –
often face a familiar set of issues:
- Manual or brittle matching logic
- High engineering effort per use case
- Difficulty scaling matching quality with
platform growth
There’s a smarter, faster way forward.
01
THE AI PATTERN THAT WORKS
Next Orbit recently partnered with a startup to solve an interesting challenge: matching thousands of candidate profiles to open roles – not by keywords, but by measuring true similarity in experience, skills, and fit.
They leveraged Next Orbit’s repeatable blueprint that any startup tackling similar two-sided matching problems in their domain.
02
HERE IS THE
RECIPE
• Design a matching pipeline powered by vector
similarity (cosine, embeddings, etc.)
• Process data using NLP techniques
• Build a scalable system on GCP using containerized
services + Terraform
• Deliver explainable, tunable match scores – integrated
into the core product
AND, THERE IS MORE
This startup used their GCP credits to fund initial experimentation and
architecture setup. $0 infra cost & top-notch solution = infinite ROI
WHERE THIS APPLIES
This AI matching model scales across use cases like:
