In short
KriyaXlabs uses Python for AI generation, verification and data pipelines. ExamXFlow is a Python system built on Vertex AI and Gemini that has produced a bank of 50,002 exam-realistic questions, each passed through an independent verification stage before it is allowed into the bank.
Python is where our generation work lives. Not the chat interface, but the part underneath: prompt orchestration, batching, verification, scoring, storage and the long unglamorous loop that turns model output into something you would let a student sit an exam on.
ExamXFlow is the clearest example. Generating a question is trivial. Generating fifty thousand questions that are exam-realistic, correctly keyed and free of the failure modes language models produce at scale is an engineering problem, and the answer was a separate verification pass that judges the output rather than trusting it.
The same discipline applies to less exotic work: extraction pipelines, scheduled jobs, plotting and reporting. SketchXFlow's generation pipeline is Python behind a Next.js front end.
What we build with Python
Generation and verification pipelines
Model output passed through an independent checking stage, with the failures kept and counted rather than discarded quietly.
Data and content pipelines
Extraction, normalisation and loading into stores that downstream products can actually query, with reruns that are safe.
Scheduled and batch systems
Long-running jobs with checkpointing, cost controls and observable progress, so a twelve-hour run is not a black box.
Analysis and plotting
matplotlib output generated as part of the pipeline, not by hand, so figures regenerate when the data does.
Engagements that use it
We pair it with
We choose a stack for the problem rather than the reverse. If Python is the wrong answer for what you are building, we will say so on the call.
Questions about Python
PythonDo you do machine learning training, or only LLM integration?
Mostly integration, orchestration and verification around hosted models, which is what the great majority of products actually need. We are direct about the line: if your problem genuinely requires training a model from scratch, we will say so rather than sell around it.
PythonHow do you control AI running costs?
Batching, caching, model tiering and hard budget ceilings enforced in code. We instrument cost per operation from the first week, because a pipeline that is affordable in a demo and ruinous at volume is the most common way AI products fail.
Everything we build on
Building on Python?
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