AI integration
A model goes in where it genuinely removes work or makes something possible that wasn’t. Not because it’s fashionable.
AI integration
AI is worth it where there is repetitive work over text, images or data: triaging enquiries, searching an internal knowledge base, drafting, extracting fields from documents, generating content inside a product. Where an ordinary conditional in code would do, a model only adds cost and unpredictability.
This has been my main area for the past two years: custom LoRAs, fine-tuning, generative pipelines, LLM applications in production with real users and real token bills. Hence the habit of treating the cost per model call as seriously as the cost of a server.
The other half is honesty about limits. Models are non-deterministic and confidently wrong sometimes. So the system gets checks, constraints and sensible fallbacks — rather than hope that it won’t make things up this time.
Assistants, chatbots and scripted dialogues inside your product or site — with protection against injection through user input, spend caps, and sane behaviour when the provider fails.
Search and answers over your documentation, knowledge base or contract archive — with citations to the source, not a paraphrase from the model’s memory.
Orchestrating several models against the task and the budget: a cheap model for the easy steps, an expensive one only where quality drops without it.
Custom LoRAs and fine-tuning for your domain, for when general models miss your style, terminology or output format.
Video, image, music and text generation inside your service: queues, long-running jobs, credit billing, result delivery.
Triaging incoming enquiries, extracting data from documents and PDFs, drafting, routing — where a person does it by hand today.
We look at the process and find the step where a model removes load or creates new value. If there isn’t one, I say so — that’s cheaper than adopting AI for its own sake.
A fast check against real examples, not demo ones. This is where both answer quality and cost per call become visible.
Integration into the existing service: interface, storage, spend caps, behaviour when the provider is down, logs for reviewing disputed cases.
After launch we watch real conversations and real spend, adjust prompts, and switch models where that’s the better trade. This is ongoing, not a one-off setup.
It depends what is being integrated: a simple assistant over your documentation and a multi-model pipeline with fine-tuning are different orders of magnitude. Billing is hourly. Describe the process you want automated and I’ll give an orientation — and say whether it pays off at all.
Not always. Some jobs run on models deployed on your own infrastructure. If the data is sensitive, that’s the first thing we discuss, before choosing a model.
Sometimes, yes — that’s a property of the technology, not a defect of a particular build. It’s reduced by answers with source citations (RAG), output checks, and flows where the model must admit it has no answer. Nobody can promise it never happens.
Model call costs are separate from development and scale with usage. By the prototype stage the cost per call is usually clear, and the monthly budget follows from it.
That’s why the system is built so the model can be swapped: one interface across providers, plus a fallback. It’s designed in up front, not after the provider disappears.
Yes, that’s the most common case. I work inside your codebase rather than proposing a rewrite.
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