AI product development
AI features with defined controls.
We define the use case, evaluation criteria, data access, operating cost, and approval requirements before an AI feature is released.
What you get.
- Evaluation criteria and representative test cases
- Documented costs, limitations, and fallback behaviour
- Human approval for higher-risk actions where appropriate
- A client-accessible repository and handover documentation
How it works.
01
Name the decision
We document the intended decision, available data, permissions, and points that require human review.
02
Measure before you launch
Representative cases establish a baseline that can be monitored as the feature changes.
03
Ship behind a flag
Where the product allows it, the feature is released gradually with logging, cost tracking, and access controls in place.
Fit.
These criteria help determine whether the engagement model matches your requirements.
This is a fit when
- Software your team already runs, with a specific job a model might do better
- An AI feature that needs measurable acceptance criteria
- Work where being wrong is expensive and quality has to be measured
A poor fit when
- Projects where AI is the requirement and the problem comes later
- A chatbot bolted on because a board asked for one
- A research lab or a foundation-model training programme
Products we run.
We run this on our own products. LobeStack uses models to study a brand and draft posts, then a person approves what goes out. Averil uses a model to read a career against a market, then states the gap in plain language.
LobeStack
LiveLive marketing software. Models draft, a person approves, and rejected posts change the next batch. Nothing publishes because a model felt confident.
Write-upAveril
LiveLive career software. The model reads skills against hiring data. There is no vanity score, and the product refuses to mass-apply on anyone's behalf.
Write-upQuestions.
You build with AI. Is the code safe?
We use AI-assisted development tools where they are useful, but code remains subject to engineering review and testing. Access control, data separation, dependencies, and database changes are reviewed according to the risks of the system.
Do you only build AI products?
No. We also build web and mobile products, integrations, internal tools, and platform modernization work. AI is recommended only when it is suitable for the use case and operating requirements.
How long does a project take?
Timing depends on the scope, existing systems, integrations, and review requirements. We use an initial assessment to define the delivery stages and provide a more reliable estimate before the main build begins.
Plan a product build.
We aim to reply within two working days. Email info@techsity.comif you would rather provide project details directly.