AI application development servicesService — AI Application Development
AI features built into products people already use — grounded in your own data, measurable after launch, and shipped as part of the application rather than as a demo beside it.
What AI application development actually involves
The model is the smallest part of the job. Most of the work is everything around it: getting your data into a shape the model can use, deciding what happens when it returns something wrong, and making the feature answerable to the same authentication, roles and audit rules as the rest of the product.
- A defined job for the feature — what it decides, and what it must never decide alone
- Your own data as the source of truth, retrieved rather than memorised
- A fallback path for every case where the model has no good answer
- The same auth, roles and tenancy rules the rest of the application uses
- Logging and evaluation, so a change to the prompt or model can be measured
- Cost controls sized to real usage rather than a demo's
Generative AI application development
Generative features have the widest gap between a convincing prototype and something you can put in front of customers.
Text generation needs a house style and an editing path. Image generation needs outputs usable at production resolution and consistent across a catalogue. Both need an answer for the run where the output is unusable — because there will be one, and a feature with no answer ships that answer to your customer.
Adding AI to a product you already have
Most of this work is integration, not greenfield. The model calls and retrieval layer sit behind your existing API, the vector store runs alongside your current database, and the feature ships as an addition rather than a parallel system nobody owns. No rebuild is required, and the AI surface inherits the permissions model you already have rather than inventing a second one.
Hiring an AI developer for a defined build
Hiring an AI developer is different from hiring an AI research team, and the difference is what you are buying.
This is application engineering: the model is a dependency chosen for the job, kept behind an interface so it can be swapped, and judged on whether the feature works rather than on benchmark scores. If you need a novel model or original research, that is a different discipline and I will say so rather than take the work.
Where this splits into two deeper pages
Two shapes of this work come up often enough to have pages of their own — conversational interfaces, and the retrieval architecture underneath almost every grounded AI feature.
- Chat and support interfaces, with escalation and guardrails
- Retrieval, embeddings, evaluation and inference cost control
Where I’ve done this
Shipped projects that use the work described above.
Common questions
What is AI application development?
- Building AI capability into a product that has a job to do — a support assistant, a search that understands intent, a generation step inside an existing workflow. It is application engineering with a model as one dependency, as distinct from training models, which is a separate discipline with a different cost base.
Do we need to train our own model?
- Almost never at the start. Retrieval over your own data gives a general model your specific knowledge at question time, costs a fraction of training, and updates the moment your data does. Training or fine-tuning earns its place when you need consistent behaviour or format that prompting cannot reach — and it is easier to justify once retrieval has shown you where the gaps are.
Which parts of a product are worth putting AI in?
- The steps where a human is currently reading something to decide what to do next — triaging a message, finding the right document, summarising a thread, drafting a first version. Those have a clear before-and-after you can measure. Features added because the category expects AI usually have no such measure, which is why they are the ones quietly removed a year later.
How do you keep inference cost under control?
- It is an architecture problem before it is a billing one. Retrieving the three passages that matter instead of stuffing twenty into the context window is the largest single saving; caching deterministic responses, routing simple requests to a smaller model, and trimming system prompts that grew organically all follow from there.
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