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What do generative AI development services build?
Generative AI development services build products where generated content or reasoning is the product itself, not a side feature. The user comes to get a draft, a document, an answer, a plan or a conversation, and the quality of that output decides whether they stay and pay. This page is about that kind of product. If you want to add one AI feature to an app that already exists, AI integration services is the better starting point.
The products we are asked to build most often fall into five groups:
| Product type | What the user gets | Typical core technique | Main quality risk |
|---|---|---|---|
| Copilot inside a workflow | Drafts, suggestions and next steps while doing a task | Prompting with context from the app, sometimes tool calls | Suggestions that ignore context the user sees |
| Document generation | Contracts, reports, proposals, resumes from structured input | Templates plus section-by-section generation, schema checks | Missing or invented facts |
| AI writing tool | Rewrite, expand, shorten, change tone | Prompting with style rules and examples | Generic output that sounds like every other tool |
| Knowledge assistant | Answers from company or product documents | Retrieval (RAG) with citations | Confident answers without a source |
| Persona or companion | A conversation in a consistent voice | Fine-tuning (LoRA) plus retrieval memory | Voice drift and factual errors about the persona |
How is a generative AI product architected?
A generative AI product is a pipeline, not a single model call. The pipeline collects input, adds the right context, calls the model, checks the output and stores everything needed to improve it later. Each stage is ordinary software engineering, which is why generative AI development is closer to back-end work than to research.
The stages we build in almost every project:
- Input capture. Forms, uploads or chat, with validation so the model receives clean, structured input.
- Context assembly. Templates, user data, retrieved documents and examples, kept within a token budget.
- Model call behind an abstraction. A provider layer so OpenAI, Claude or a self-hosted open model can be swapped per task.
- Output checks. JSON schema validation for structured output, length and format rules, and a second pass for risky content.
- Storage and versioning. Prompts live in the database with a version history, so tone and instructions change without a release.
- Feedback and evaluation. User edits, ratings and a fixed test set that runs on every prompt or model change.
For AI Resume Master, our own product, that pipeline generates, rewrites and improves resume content for every section and creates tailored cover letters from the resume itself. Users produce a complete resume in 3 to 5 minutes. We built the product in about three months, and it reached 50,000 monthly active users.
Prompting, retrieval or fine-tuning: which does your product need?
Most generative AI products need prompting and retrieval; a minority also need fine-tuning. The choice depends on whether the problem is missing knowledge, which retrieval solves, or missing behavior, which fine-tuning can solve.
| Approach | Solves | Cost to build | Cost to change | Use when |
|---|---|---|---|---|
| Prompting with templates | Format, tone, task instructions | Low | Minutes | Always the first step |
| Retrieval (RAG) | Access to your documents and facts | Medium | Re-index the data | Answers must come from your content |
| Fine-tuning (for example LoRA) | Consistent voice, narrow task on a smaller model | Medium to high | Retrain the adapter | Prompting cannot hold the behavior, or you self-host |
The AI Grief Companion we built for a US startup shows when all three belong together. The product lets a user upload chat history with a person they lost and then talk to a companion in that person's voice. We parse ten chat-export formats, build a personality profile and a fact graph in Neo4j, and train LoRA adapters on Qwen2.5-7B with 4-bit QLoRA, which vLLM loads at request time. Retrieval supplies facts, the adapter supplies the voice, and the chat falls back to whichever stage is ready, so a conversation is possible about twenty minutes after upload and improves as training completes. For retrieval-heavy products, see RAG development.
How do we control quality in generative AI output?
Output quality in generative AI is controlled by measuring it, not by writing a better prompt once. Before a feature ships we build an evaluation set: real or realistic inputs with expected properties of a good output. Every change to a prompt, model or retrieval setting runs against that set.
What we check depends on the product. For document generation: every required section present, no facts that are not in the input, correct format. For writing tools: instructions followed, length respected, tone matches the requested style. For assistants: the answer cites a source, and says "I don't know" when the source is missing. Some checks are code, some use a second model as a grader, and a sample is always reviewed by a person. In production we log inputs and outputs (with personal data masked where required), track user edits and regenerations as quality signals, and review the worst cases every week in the first months.
What about safety, privacy and sensitive content?
Generative AI products that handle personal or emotional content need safety built into the data layer and the conversation design, not only a content filter. In the AI Grief Companion, every message is encrypted at ingest with per-message AES-256-GCM under a KMS envelope key, users can delete everything with one click, and the conversation design prompts toward professional help when a conversation needs it.
For business products the questions are more ordinary but just as important: which fields reach a third-party API, where the data is processed, how long logs are kept, and who can read them. We document the data flow for your security review and use masking or self-hosted models where the answer requires it.
What does generative AI development cost?
Generative AI development cost depends on the product type, whether fine-tuning or self-hosting is needed, and how much surrounding product (accounts, billing, mobile apps) must be built. As a rule of thumb, a generative feature in an existing product takes 4 to 8 weeks, a new generative AI MVP on hosted models 8 to 14 weeks, and a product with fine-tuning, self-hosted models or mobile apps 4 to 8 months. Monthly running cost is separate: tokens or GPU hosting, vector database, storage and monitoring.
We give a fixed quote after a short scoping call. A generative AI feature in an existing product starts from $5,000 with us, and our guide on AI app development cost breaks down both build and run costs.
Why teams pick us as their generative AI development company
Generative AI development companies are easy to find; teams that have shipped generative products to real users and kept them running are fewer. We have built generative AI both as a vendor and as a product owner, which means we have paid the token bills, handled user complaints about bad outputs and rewritten prompts under real traffic. Our main back-end stack is PHP/Symfony, with React or Vue on the web, React Native or Flutter on mobile, and Python where model training or serving requires it. Senior engineers lead every project, and we use AI coding agents in our own delivery process to ship faster. When the product idea itself is still forming, we usually start with AI product development discovery rather than jumping into a build.
Next step
Send us a short description of what your product should generate, for whom, and from what input. We start with a short scoping phase that ends with a tested prompt or prototype on your real examples, a cost-per-generation estimate and a fixed quote for the build. Book a generative AI scoping call.
