technocity

AI Solutions · Generative AI development

Generative AI development for real product features.

We add generative AI where it saves users real effort, and we measure whether it works. Each feature ships with tests, cost limits and a way for people to correct it.

In short

What is generative AI development?

Generative AI development means building software features that create text, speech or images using large language models and related tools. technocity designs and builds these features inside web and mobile products, such as drafting, summarising, feedback and AI-assisted marking, with evaluation sets, usage limits and human review where accuracy matters.

Sound familiar?

The problems we solve

An AI demo works once but fails on real user input

Model costs grow faster than usage or revenue

No way to tell if a prompt change made answers worse

Users do not trust AI output they cannot check or edit

What’s included

How we can help

AI feature prototype

A working version of one AI feature, tested on your real data, to decide if it is worth building.

from 2 weeks

Production AI feature

The feature built into your product with error handling, logging, limits and an editing interface.

from 6 weeks

AI-assisted assessment

Marking and feedback for writing or speaking tasks, with rubrics and teacher review.

from 8 weeks

Evaluation and model review

A test set and scoring routine so you can compare models and prompts before switching.

from 2 weeks

Timelines are typical starting points. Every project is scoped with you first.

What you get

  • Source code for the AI feature in your repository
  • Prompt and model configuration under version control
  • An evaluation set built from real examples
  • Per-user and per-day usage and cost limits
  • Logging of inputs, outputs and user corrections
  • A short guide on changing models or prompts safely

Tools and technology

OpenAI APIAnthropic ClaudeGoogle GeminiPythonTypeScriptNext.jsPostgreSQLLangfuse
Get a scoped estimate

How it works

From first call to results

1

Pick the job

We choose one task where AI can save time and define what a good output looks like.

2

Build an evaluation set

We gather real examples and expected results so quality can be measured, not guessed.

3

Prototype and compare

We try more than one model and prompt, then pick on quality, speed and cost.

4

Ship with guardrails

We release behind limits, with logging and an easy way for users to edit or flag output.

Questions

Common questions

What is generative AI development?

Generative AI development is building software that produces new text, speech, images or code using AI models. In business products it usually means features such as drafting replies, summarising documents, giving feedback or marking answers, built into an existing app with testing and controls.

Which AI model is best for my product?

The best AI model depends on the task, the language, the response speed you need and your budget. We test several models from providers such as OpenAI, Anthropic and Google on your real examples and choose on measured results rather than reputation.

Can AI mark exams or written work accurately?

AI can mark structured writing and speaking tasks against a rubric and give useful feedback, but it is not fully reliable on its own. We build AI-assisted marking where a teacher or examiner can review, adjust and approve scores, especially for high-stakes results.

How do you control generative AI costs?

Generative AI costs are controlled by choosing the smallest model that meets quality, caching repeated requests and setting usage limits per user and per day. We also log cost per feature so you can see where spending goes and adjust before it grows.

Will our data be used to train AI models?

No, your data is not used to train AI models unless you agree to it. We use business API terms from model providers, which do not train on API inputs by default, and we can keep data in a region you choose where the provider supports it.

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