PARADOX AI LABS
HOW WE WORK

No borrowed logos.
Just the method.

We're a young studio, so rather than dress up a client list, here is exactly how we approach the problems we're best at: what we do, what you receive, and how we decide whether it worked.

The engagements below are illustrative examples of our process, not reports of past client results.

01APPLIED AI · EVALS

Getting an LLM feature from demo to production

The situation

A chatbot, assistant or summariser looked great in the demo. Now real users report wrong or odd answers, and nobody can tell whether the last prompt change made things better or worse.

What we do
  1. 1.Collect real conversations and known failures into a test set
  2. 2.Build an evaluation harness that scores every change against it
  3. 3.Fix the biggest failure modes first: retrieval, prompting, guardrails
  4. 4.Add monitoring so new problems show up before users report them
What you get
  • Evaluation suite and test set
  • Fixes as reviewed pull requests
  • Quality report you can re-run
  • Handover docs
How we measure it
  • Answer quality on the test set
  • Failure rate by category
  • Latency and cost per conversation
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02APPLIED AI

Turning messy documents into reliable structured data

The situation

A team is copying figures out of PDFs, emails or forms by hand. A quick LLM prototype works on the demo files but is inconsistent, slow, and hard to trust on real inputs.

What we do
  1. 1.Collect a test set of real documents and agree on the exact output schema
  2. 2.Build the pipeline with schema-validated outputs and typed tool calls
  3. 3.Add retrieval, retries and fallbacks where the model is weakest
  4. 4.Run an evaluation suite on every change so quality is measured, not guessed
What you get
  • Working pipeline in your stack
  • Evaluation suite and test set
  • Cost and latency report
  • Handover docs
How we measure it
  • Field-level accuracy on your test set
  • Schema conformance
  • Latency and cost per document
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03PRODUCT ENGINEERING

Making a slow, fragile website fast and solid

The situation

A marketing site or web app has poor Core Web Vitals, slips in search rankings, and gets hammered by bots and scanners. Every change feels risky because nothing is tested.

What we do
  1. 1.Profile real-user and lab performance to find the actual bottlenecks
  2. 2.Fix rendering, images, fonts and data fetching in priority order
  3. 3.Add security headers, input validation and sensible rate limits
  4. 4.Put basic automated checks in place so it stays fixed
What you get
  • Before/after performance report
  • Code changes as reviewed pull requests
  • Checklist for future releases
How we measure it
  • Core Web Vitals (LCP, INP, CLS)
  • Lighthouse scores on key pages
  • Errors and blocked bad traffic
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Start small

The easiest way to judge us is a small first project.

A two-to-three week Feasibility Sprint gives you a working prototype, real numbers and a clear recommendation to judge us by, before committing to anything bigger.

Tell us about your project ↗About the sprint →