How to Build an App Like ChatGPT: A Guide for Domain-Specific AI Products

Most founders building an LLM assistant are not trying to compete with OpenAI. They are a legal technology firm whose clients ask specific questions about jurisdiction-specific contract clauses. They are a healthcare operator who cannot send patient data to a shared API. They are a SaaS company that wants to add AI as a feature — not launch a new AI company. The market for domain-specific AI assistants is growing faster than the general-purpose chatbot market, and the technical bar to build one has dropped sharply. What has not dropped is the cost of building one badly.

These numbers assume an external development team. Internal teams with strong ML experience can reduce cost but rarely reduce timeline by more than 20%.

How does ChatGPT make money — and what does that mean for your product?

ChatGPT monetizes on subscriptions ($20/month for Plus, $25/month for Teams, and custom enterprise pricing) and API usage fees. The API model is the one most relevant to founders building on top of it.

Your options differ. You are building a vertical product for a defined audience, not a general product for millions of users. That changes the economics entirely.

The most common monetization models we see:

• Per-seat SaaS subscription — $19–$99/user/month, billed annually. Works well when users interact with the assistant daily and the value is clear per person.

• Consumption-based pricing — per query or per token, often used when usage varies significantly across customers.

• Enterprise license — annual contract for a defined seat count and API call volume, usually custom-priced above $50K/year.

• White-label — build the assistant and license it to other businesses who rebrand it for their customers.

The unit economics are real. At 500 enterprise seats at $49/month, you have $24,500 MRR. Cloud LLM inference costs (GPT-4o via API) typically run $0.02–$0.10 per conversation depending on context length. At $0.05 average, 500 users making 10 queries per day costs roughly $750/month in inference — leaving healthy margins even before optimization.

According to Grand View Research, 2024, the conversational AI market is projected to reach $32.6 billion by 2030, growing at 23.6% annually. The growth is not in general-purpose chatbots — it is in vertical AI tools where domain accuracy matters.

Who actually builds a custom LLM assistant?

Not startups trying to out-ChatGPT ChatGPT. Four categories of companies should be building their own.

Legal technology firms where the generic model produces hallucinations on jurisdiction-specific contract clauses. Their case libraries — precedents, clauses, jurisdictional interpretations — exist in documents a general model has never seen. They need an assistant grounded in their own case archive, with retrieval tied to their actual document corpus.

Healthcare operators where HIPAA compliance rules out sending patient data to OpenAI's shared API. A clinical documentation assistant, a pre-visit intake summarizer, or a care-gap identifier cannot route through a vendor without a signed Business Associate Agreement and guaranteed data isolation. Private deployment or a HIPAA-compliant API endpoint is not optional.

Enterprise companies that deployed Microsoft Copilot and hit a wall. Copilot handles generic knowledge worker tasks well. It fails when employees ask about the company's internal pricing logic, proprietary workflows, or products not indexed anywhere on the public web. These companies need RAG over their own knowledge base — not a general model with internet access.

SaaS companies adding AI as a product feature. A property management SaaS adding AI-powered tenant communication drafts. A recruiting platform adding AI-generated interview summaries. A CRM adding AI deal-coaching. These are not standalone AI products — they are AI capabilities embedded in an existing product, and they require the AI to know the platform's data model, not just the open internet.

Build vs. ChatGPT: when does a custom build actually win?

A custom build wins when your data cannot leave your infrastructure, or when ChatGPT's answers in your domain are wrong more than 20% of the time. Getting this wrong in either direction costs real money — over-building a custom system you did not need, or under-building one you should have had.

Keep using ChatGPT (or the API) when:

Your use case is general-purpose: writing, summarizing, brainstorming. You have fewer than 200 users and no specialized knowledge base — the infrastructure overhead of a custom deployment is not justified. Compliance is not a concern for your data, and you can accept OpenAI's standard data-use terms. Or you need a quick internal tool that employees will use alongside other general AI tools.

Build your own when:

Your data cannot leave your infrastructure. Healthcare, legal, and financial services companies often have regulatory or contractual obligations that prevent sending data to a third-party API. ChatGPT's answers in your domain are wrong or generic 20% or more of the time — this threshold matters, because systematic inaccuracy in your domain destroys user trust faster than any other product issue. You need the AI to know your company's specific products, prices, policies, or workflows. And if AI is your core differentiator, depending on a competitor's product is a strategic liability.

What features should a domain-specific AI assistant include in V1, V2, and V3?

Getting the phasing wrong is one of the most expensive mistakes in LLM app development. Teams that try to build everything in V1 spend $200K to learn things they could have learned for $80K.

V1 — Launch (12–16 weeks, $60K–$110K)

V2 — Growth (24–32 weeks total, $180K–$320K)

V3 — Scale (36–48 weeks total, $400K+)

What engineering problems will eat your LLM app budget?

Two failure modes account for most wasted spend in LLM app projects. Both are avoidable with upfront investment — and expensive to fix after launch.

RAG quality is harder than teams expect. The failure mode we see most often: teams building RAG on top of a poorly curated document corpus. They ingest 50,000 documents without a chunking strategy, metadata tagging, or relevance testing. The retrieval layer surfaces wrong passages 30% of the time. Users encounter wrong answers in the first week and stop using the product. The fix — clean document processing, smart chunking, metadata tagging, and retrieval testing — adds 3–4 weeks of data engineering work upfront. Skipping it means launching a product with a hallucination reputation that is nearly impossible to recover from. According to Stanford HAI's AI Index, 2024, RAG systems with poorly structured retrieval corpora produce significantly more factual errors than those with curated, well-chunked knowledge bases. The index matters more than the model.

Model cost surprises arrive after launch, not before. Teams that build with GPT-4-class models for all queries discover at scale that inference costs run 5–10x what they modeled in a spreadsheet. A query that costs $0.05 in testing becomes a $5,000/month line item when 200 users generate 50 queries per day. Routing simple queries to cheaper models like GPT-4o Mini or Claude Haiku, and reserving expensive models for complex reasoning, requires architectural planning from day one. Retrofitting a model router into an existing product takes 4–6 weeks and often requires reworking the prompt structure throughout the codebase. According to Andreessen Horowitz, 2024, inference costs are the top margin pressure for AI product companies — not development costs.

What does a real LLM app build actually look like?

Two patterns repeat across every successful LLM app we have built.

Teams that ship on time invest 20–25% of their development budget in data engineering before writing a single line of product code. They build the ingestion pipeline, test retrieval accuracy against a set of real user questions, and do not start the chat UI until retrieval is working. Teams that skip this step spend the same money fixing it later — usually under production pressure with users already waiting.

The second pattern: teams that set a model cost budget before choosing models, not after. They pick models based on what they can afford at their projected query volume, then test whether quality is acceptable at that price. Most find that a well-tuned smaller model with good retrieval beats a large model with poor retrieval at one-fifth the inference cost.

"The mistake we see most often is teams treating the LLM as the product," says Ashit Vora, co-founder of RaftLabs. "The LLM is just the reasoning layer. The product is the quality of the knowledge you give it access to. Teams that invest in data architecture ship products that work. Teams that invest in model selection first ship products that are expensive and wrong."

How RaftLabs approaches a domain-specific AI assistant build

We start every LLM app engagement with a data audit before writing any product code. That means reviewing your existing document corpus, identifying gaps, and building the chunking and metadata strategy that makes retrieval accurate before the chat interface exists.

Most projects need 2–3 weeks of data engineering work that clients do not initially budget for. That investment is what separates products that earn user trust from products that get abandoned in month two. From there, we build the model routing layer, the chat interface, and usage monitoring that tracks token consumption per query type from day one. By launch, you know what your inference costs will look like at 5x your current user volume — not as a surprise after the fact.

If you are building a domain-specific AI assistant and want to know what your specific use case would cost and how long it would take, book a 30-minute scoping call with our team.