Dedicated AI Developer for your product and internal tools

Defining the feature and success criteria

Start with the user problem and the measurable outcome, not the model. For example: reduce the time support agents spend finding answers, or let users ask questions about their own data in plain English.

Write down what a good answer looks like, what the system must never do and the acceptable response time and cost per request. These criteria guide every technical decision that follows.

Retrieval and data preparation

Many AI features depend on retrieval: finding the right information from your documents or database and giving it to the model as context. Quality depends on how content is split, indexed and searched.

Your developer designs the retrieval pipeline, tests different chunking and search strategies against real questions and keeps the index in sync as data changes. Good retrieval often matters more than the choice of model.

Evaluation before every release

Traditional tests check that code works. AI features also need evaluation sets: realistic inputs with expected outputs or quality criteria. Running the set before each release reveals whether a change improved or degraded results.

Include difficult and adversarial cases, such as ambiguous questions or attempts to make the system ignore its instructions. Evaluation turns subjective impressions into evidence.

Security and responsible design

AI features introduce new risks, including prompt injection, data leakage between users and over-permissioned tools. Your developer applies least privilege, separates user data properly and treats model output as untrusted input in the rest of the system.

Where personal information is processed, design for the Australian Privacy Principles from the start, including clear purposes, access controls and retention rules.

Managing cost and performance

Model usage costs scale with traffic. Techniques such as caching, choosing smaller models for simpler steps, limiting context size and batching background work can reduce costs substantially without hurting quality.

Monitor latency as well. Users notice slow responses, so streaming output and optimising retrieval often improve the experience more than switching models.

Working inside your engineering team

A dedicated developer follows your repositories, branching strategy, code review and deployment processes. Treat them as a team member: include them in planning, give them context on the product and review their work as you would any engineer's.

Apex provides technical oversight and helps with architecture decisions, so you benefit from patterns we have used across other AI projects.

Monitoring AI features in production

Track usage, error rates, latency, cost per request and quality signals such as user feedback or escalation to a person. Log enough detail to investigate problems while respecting privacy.

Review these metrics regularly and feed real failures back into the evaluation set. That loop is how AI features improve after launch rather than slowly degrading.

An example: adding AI search to a SaaS product

A software company wants customers to ask questions about their own data in plain English. The developer designs a retrieval pipeline over each customer's records, enforces strict separation between customers and builds an evaluation set from real support questions.

The feature launches to a small group first. Monitoring shows which questions fail, and those cases join the evaluation set. After several iterations, accuracy and response time meet the agreed targets and the feature rolls out to all customers.

Questions to ask before engaging an AI developer

What user problem should the feature solve? What data will it use, and who can see it? What does a good answer look like? What latency and cost per request are acceptable? How do you deploy and review code today? These answers frame the work and help the developer contribute quickly.

When you need a managed agent instead

If the goal is an internal automation rather than a product feature, and you do not have an engineering team to own it, our custom AI agents service is often a better fit. A dedicated developer works best when there is a product roadmap and engineering processes to plug into.

Costs and timeline to expect

Dedicated AI development is scoped within Apex Managed based on seniority and hours. Most developers contribute to production code within the first weeks after onboarding to your repositories and processes.

Choosing the right model for each task

Not every step in an AI feature needs the most capable model. Classification, extraction and routing can often use smaller, faster and cheaper models, reserving the most capable model for complex reasoning or writing. Your developer tests options against the evaluation set and selects the combination that meets quality targets at the lowest sustainable cost, while keeping the design flexible enough to switch models as the market evolves.

Key terms explained

LLM

Large language model, the type of AI model that understands and generates text.

Vector database

A database that stores text as numerical representations so similar meaning can be searched quickly.

Prompt injection

An attempt to manipulate an AI system through crafted input that overrides its instructions.

Latency

The time between a request and the response, which affects user experience.