AI App Development: What Models Do Well and How to Ship Them

You have a product, or an idea for one, where a language model could do real work: answer questions from your documentation, pull key fields out of contracts, route incoming tickets, draft a first reply. AI app development done well starts from that job rather than from the model, and it ends with a feature that is measured, affordable and trusted by the people who rely on it.

Good looks unremarkable from the outside. The feature is right often enough to be worth using, it signals when it is unsure, it costs a predictable amount per task, and when it is wrong, a person can notice and correct it. This is our overview of how we build AI products at Inferne, including the cases where we recommend not using a model at all.

What language models do well, and where rules win

Language models are strong at work with messy, unstructured input, where an occasional imperfect answer can be caught by a person or a check. In practice that means:

• Turning free text into structure: an email into a ticket with a category and priority, a contract into a table of parties, dates and obligations.
• Finding and summarizing information across a large body of documents, with references back to the sources.
• Sorting items into many fuzzy categories, where writing a rule for every case would never end.
• Drafting text that a person reviews before it goes anywhere: replies, descriptions, summaries, translations.
• Understanding natural-language requests and mapping them onto actions your software already supports.

They are weak where the answer must be exact and repeatable. Calculating a fee, checking eligibility against a policy, validating an account number or matching a SKU are jobs for ordinary code. A rule is faster, costs almost nothing to run, behaves the same way every time and can be explained to an auditor.

If you can write the rule down, write the rule. We use a model for the part of a problem that resists rules and keep the rest deterministic. In discovery we will tell you plainly when a regular expression, a search index or a decision table solves your problem better than any model.

Product patterns we build

Most AI features fall into a handful of patterns. Naming the pattern early matters, because each has its own architecture, its own failure modes and its own way of measuring quality.

• Assistants. A conversational helper inside your product that answers questions about the user’s own data and can trigger a few well-defined actions. Its scope is deliberately narrow.
• Search and question answering. Retrieval-augmented generation (RAG): find the relevant passages in your content, then answer from them with citations. Common for help centers, policy libraries, legal research and internal knowledge bases.
• Extraction. Documents in, validated records out: invoices, claims, medical forms, contracts. Each field carries a confidence signal, and uncertain records go to a review queue.
• Classification and routing. Tickets, leads, transactions or user reports sorted into categories and sent to the right queue, with human overrides that feed back into evaluation.
• Generation. Drafts of text, and increasingly images, audio and video. Media generation brings its own concerns around GPU cost, safety and provenance, covered in generative AI development.

When a feature decides its own next steps and acts in your systems, it becomes an agent, which raises different questions about permissions and control. Our article on AI agent development covers when that is warranted and when a fixed workflow is the better design.

The architecture of an AI feature

The model call is usually the smallest part of the system. A production AI feature is ordinary software with one probabilistic component, and most of the engineering goes into everything around it. A typical request passes through these stages:

1. Input handling. Validate and normalize the request, and establish what the user is allowed to see.
2. Context assembly. Retrieve the relevant documents or records, filtered by the user’s permissions, and trim them to what the task needs.
3. Prompt construction. Fill a versioned prompt template that lives in the code repository, not in someone’s notes.
4. Model call. Go through a thin internal interface, so the provider or model can change without touching product code.
5. Output validation. Check the response against a schema and your business rules, then retry, fall back or hand over to a person when it fails.
6. Presentation. Show sources, allow editing before anything is sent, and make uncertainty visible instead of hiding it.
7. Logging and feedback. Record inputs, outputs, versions, latency and cost, and whether the user accepted, edited or rejected the result.

If you are adding a feature like this to an existing product, the practical details of provider abstraction, retrieval, structured outputs, caching and fallbacks are in LLM integration.

Evaluation: knowing whether it works

An AI feature without evaluation is a demo. Before we tune a single prompt, we build an evaluation set with your team: real inputs, anonymized where needed, each paired with what a correct result looks like.

What counts as correct depends on the pattern. Extraction is scored field by field. Classification is measured by how often each category is right and how often it is missed. Question answering is judged on whether each answer is supported by the sources it cites, and drafting against a written rubric.

Scoring combines three methods. Deterministic checks catch schema errors and exact-match fields. A second model can grade open-ended answers against the rubric, but only once its grades agree with human judgments. People review a sample, because some failures only a domain expert will spot.

The evaluation runs on every change to a prompt, a model or the retrieval pipeline, so regressions are caught in review rather than by customers. In production, sampled traces and user feedback keep adding hard cases to the set.

Cost, latency and privacy budgets

Three budgets shape the design as much as quality does, and we agree on them in discovery.

Cost

We estimate cost per completed task, not per call, because one task can involve retrieval, several model calls and retries. The main levers are smaller models for easy steps, tight prompts and context, caching repeated work and moving non-urgent jobs into background batches.

Latency

An inline suggestion and an overnight document review have very different tolerances. Interactive features stream their output so users see progress at once; slow work moves to a queue and notifies the user when it is done.

Privacy

Decide what data may leave your infrastructure and under what terms. That means reviewing the provider’s retention and training policies, choosing regional processing where it is offered, redacting personal data the task does not need, and enforcing access control during retrieval so the model never sees documents the user could not open. In MedTech, FinTech and LegalTech, where sensitive data is the norm, self-hosted open-weight models are an option, at the price of running GPUs and owning their operation.

How we run AI app development, from prototype to production

The stages are familiar; what happens inside them is specific to AI.

• Discovery. Our business analysts map how the job is done today, what data exists, what a wrong answer costs and which metric will show that the feature is worth having. This is where we recommend rules instead of a model, if that is the honest answer.
• Prototype. We test the core behavior on your real data against the evaluation set, in weeks rather than months. Some ideas stop here, cheaply, and that is a good outcome.
• Design. Our designers decide how the product presents suggestions, sources, confidence and corrections, so users know when to trust the output and how to fix it.
• Build and QA. Engineers integrate the feature with validation, fallbacks and monitoring. QA testers work from the evaluation set and from adversarial inputs, including attempts to make the feature ignore its instructions.
• Launch. The feature ships behind a flag, first to internal users, then to a growing share of customers, while we watch quality, cost and latency.
• Iterate. Failures are reviewed on a regular cadence, added to the evaluation set and fixed, and new models are evaluated on your data before anyone switches.

You get regular demos and written reports, and everything stays yours: code in your repository, prompts under version control, the evaluation set and its results, dashboards, and a runbook for the people who will operate the feature.

Frequently asked questions

Do we need to train our own model?

Rarely. Most products do well with a capable existing model, good retrieval and careful prompts. Fine-tuning helps when you have many examples of a consistent format or style and prompting has plateaued on your evaluation set. Training a model from scratch is almost never justified for a product feature.

Which model or provider should we use?

The one that performs best on your evaluation set within your cost, latency and data-handling constraints. Public benchmarks are a starting point, not a decision, and we keep the provider behind an internal interface so the choice can be revisited.

How do you stop the model from making things up?

You reduce it rather than eliminate it. Grounding answers in retrieved sources, requiring citations, validating outputs, letting the model say it does not know and keeping the scope narrow all help. The product should then make a wrong answer visible and cheap to correct.

Can our data stay private?

Yes, with the right setup: commercial terms under which the provider does not train on your data, regional endpoints, redaction before anything is sent, and self-hosted models where data must not leave your infrastructure at all.

Can you add AI to a product you did not build?

Yes. We start by reviewing the existing code, data and infrastructure, then add the feature as a well-bounded module rather than scattering model calls through the codebase.

If there is a job you think a model could do, the quickest way to find out is to test it on your own data. Tell us about your product, and we will tell you whether a model, a rule or a combination of both is the right tool.