AI integration
AI is easy to demo and hard to depend on. WayTech helps you find the places where language models and machine learning genuinely save time or unlock a feature, builds them into the software you already use, and measures whether they work before you rely on them.
What you get
A scoped, working prototype
A small version of the feature running on your real data, so you see what is possible, what it costs and where it falls short before committing to a full build.
An evaluation you can trust
A set of realistic test cases and clear measures of success, so a change to a prompt or a model can be judged on evidence.
Production integration
The feature built into your application or workflow with sensible error handling, rate limits, logging and fallbacks when a model is slow or wrong.
Guardrails and privacy
Decisions about what data leaves your systems, how personal information is handled and how outputs are checked, written down and implemented.
Monitoring and cost visibility
Dashboards or reports for quality and spend, so the feature stays useful and affordable as usage grows.
How it works
Find the use case
We look at your workflows and pick one where AI can clearly help, and agree how success will be measured.
Prototype and evaluate
A quick prototype and an evaluation set show whether the approach works well enough, and which model and design give the best result for the cost.
Build it in
The winning approach is integrated into your product with the checks, fallbacks and privacy protections it needs to run reliably.
Watch and tune
After launch, quality and cost are monitored and the feature is improved as real usage shows what matters.
Common questions
Which AI models do you use?
Whichever fits the job. I start with well-supported hosted models because they are fastest to build with, and design the feature so the model can be swapped later. For the right problems, custom-trained models are also an option.
What happens to our data?
It depends on the provider and the plan, so I check the terms first and design around them. Sensitive data can be minimised, masked or kept out of prompts, and where needed the work can use providers or setups that meet your requirements.
Will it make things up?
Language models can produce confident mistakes. I reduce the risk by grounding answers in your own data, constraining outputs, testing against realistic cases and keeping a person in the loop where mistakes are costly.
Do we need our own trained model?
Usually not. Most business needs are met by well-designed prompts, retrieval over your data and good evaluation. If a custom model would truly help, an early prototype will show it.
Can you improve an AI feature we already built?
Yes. A review of prompts, evaluation, cost and failure cases often finds quick wins, and I can help set up the measurement that was missing.