KriyaXlabs · AI development
AI development, LLM integration and agent systems
In short
KriyaXlabs builds AI systems that are checked before they ship: multi-agent pipelines, retrieval with vector search, MCP servers so coding agents can use your tools, and — the part most teams skip — independent verification gates and hard spend caps. ExamXFlow, SketchXFlow, ArchXFlow and Crampad are all LLM products we run in production.
Getting a language model to produce something plausible takes an afternoon. Getting it to produce something correct, ten thousand times, at a cost you can predict, is engineering — and it is where most AI projects quietly fail after the demo.
Every product in our own portfolio is an AI product, which means we pay the model bills and answer for the outputs. That is why our client work ships with verification gates, spend meters and kill switches as standard, not as an upsell.
What you get
Agent pipelines
Multi-stage agents where each step produces an inspectable artefact, so you can see which stage failed rather than staring at a wrong answer.
Retrieval-augmented generation
Vector search over your documents with the chunking, ranking and citation discipline that makes answers trustworthy.
Verification gates
A second, stronger model that independently checks output before it ships — the pattern that makes ExamXFlow's questions provably correct.
MCP servers
Expose your tools to coding agents through the Model Context Protocol. SketchXFlow ships one in production.
Cost control
Hard currency caps that halt a run mid-batch, kill switches usable without a deploy, and measured per-run spend rather than estimates.
Document, vision and voice workflows
OCR, structured extraction, image understanding and speech, wired into the same verified pipeline.
How it runs
- 01Scoping: what the model must get right, what 'right' means, and how it will be checked.
- 02Prototype in days against your real data, with cost measured from the first run.
- 03Harden: verification, retries, caps, observability.
- 04Ship with a runbook your team can operate.
Built with
Proof, not promises
Products of ours that do this in production.
How we think about it
Generating questions is easy. Verifying them is the product.
Any current model will write you a plausible multiple-choice question. Getting one that is provably correct, with exactly one right answer, is a different engineering problem — and it is the only part that matters.
AI engineeringDesign-to-code is easy to demo and hard to ship
Converting a design into markup takes an afternoon. Converting it into a codebase that compiles, is responsive, and a developer will accept is a different problem — and the gap between them is where most tools live.
Questions about ai development
AI developmentWhich AI models do you work with?
Gemini on Vertex AI, Claude, and OpenAI models, chosen per task. We routinely generate with a fast, cheap model and verify with a stronger one, because verification output is small and the asymmetry makes it nearly free.
AI developmentHow do you stop an LLM system from running up costs?
A hard spend meter in currency that halts a run the instant it is crossed, a kill switch that refuses every billable call without a deploy, and measured per-run cost reported rather than estimated. These ship with every system we build.
AI developmentCan you build an MCP server for our tools?
Yes. We ship one in production for SketchXFlow, so coding agents can design, inspect and iterate programmatically. The same approach exposes your internal tools to agents safely.
AI developmentDo you build RAG systems?
Yes — retrieval over your documents with vector search, with attention to chunking, ranking and citation so answers can be traced to sources.
Need ai development?
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