KriyaXlabs · Vertex AI and Gemini
Google Vertex AI and Gemini development
In short
KriyaXlabs builds production systems on Google Vertex AI and Gemini, including agentic pipelines and verification gates that check model output before it reaches a user. ExamXFlow uses this stack to generate and independently verify exam questions; ArchXFlow uses it for agentic architectural design.
The hard part of building on a frontier model is not the API call. It is everything you put around it: retrieval that returns the right context, a verification stage that catches the confident mistakes, cost controls that hold at volume, and a failure path for the day the model is wrong or the service is down.
ExamXFlow generates exam-realistic questions and then judges them with a separate pass before they enter the bank. That gate is the product. Without it you have fifty thousand plausible questions and no way to know which ones are wrong.
ArchXFlow pushes further into agents: a pipeline that reads public site data, reasons about constraints and produces architectural options, with each stage inspectable rather than a single opaque prompt.
What we build with Vertex AI and Gemini
Agent pipelines
Multi-step reasoning split into inspectable stages, with tool use, retries and a record of what each step decided.
Verification gates
An independent pass that checks generated output against ground truth or rubric, and refuses what does not clear the bar.
Retrieval systems
Chunking, embeddings and pgvector retrieval tuned on your corpus, evaluated against real questions rather than impressions.
Cost and rate governance
Model tiering, caching, batching and hard ceilings, instrumented per operation from week one.
Running in production
Our own products on Vertex AI and Gemini.
Engagements that use it
Questions about Vertex AI and Gemini
Vertex AI and GeminiAre you tied to Google's models?
No. We build on Vertex AI and Gemini most often, and also on Claude and OpenAI models. The integration layer is written so the model behind a task can be swapped without rewriting the product around it.
Vertex AI and GeminiHow do you stop the model from being confidently wrong?
A separate verification stage that evaluates output rather than trusting it, plus retrieval that grounds answers in your own content, plus explicit refusal paths. We also keep and count the failures, because a system that never reports a rejection is not checking anything.
Vertex AI and GeminiCan you work inside our Google Cloud project?
Yes. We deploy into your project with your billing, your quotas and your service accounts, and hand over infrastructure-as-code at the end so nothing depends on us remaining involved.
Everything we build on
Building on Vertex AI and Gemini?
Thirty minutes, no obligation. We reply within one business day and share a detailed proposal within two business days of the call.
30 min · No obligation · Reply within one business day