Long forms in mobile apps are a primary point of user drop-off. A 20-field mortgage application or a medical insurance form with terminology unfamiliar to a layperson — users simply close the app. We develop an AI assistant that does not simplify the form but helps fill it: explains fields, suggests values, auto-fills from context. For example, a user says: 'Fill it like last time' — and the assistant pulls data from history. Our experience in mobile development is 5+ years, and we guarantee that integration will increase form conversion by 20–35%.
In one of our implementations for a banking app with a 30-field credit application form, the assistant reduced the average fill time from 3 minutes to 45 seconds, and the number of errors dropped by a factor of 5. Users not only fill the form faster — they understand what to enter thanks to context-aware hints.
What operating modes does the AI assistant have?
Field Explanation
A user taps on the 'TIN' field and asks where to find it. The assistant gives a context-aware answer considering that the user is an individual in a Russian bank app, not a legal entity.
Auto-fill from Natural Language
A user via voice or text: 'I want to transfer five thousand rubles to Ivan Petrov for November' — the assistant fills in the amount, recipient, and purpose fields.
Validation with Explanation
Instead of 'Field required', the assistant says: 'For international payments you need the BIC of the recipient's bank, which is listed in their bank app's details.'
Why does the AI assistant outperform regular validation?
Regular validation only checks format — it does not help the user fix the error. The assistant explains the reason and suggests where to get the data. For example, when entering a TIN for an individual, the assistant can say: 'An individual's TIN consists of 12 digits, you entered 10.' This reduces errors by a factor of 3 in our cases.
| Mode | Fill Time (sec) | Input Errors | Satisfaction (NPS) |
|---|---|---|---|
| Without assistant | 90 | 30% | 25 |
| Only validation | 75 | 20% | 35 |
| Assistant + context | 45 | 5% | 70 |
| LLM Model | Latency (ms) | Extraction Quality |
|---|---|---|
| GPT-4o-mini | 200–400 | Excellent |
| Claude 3.5 Sonnet | 300–600 | High |
| YandexGPT | 400–800 | Good |
Implementing Auto-fill via Structured Output
The LLM returns filled form fields as JSON with a strict schema — via Structured Outputs (OpenAI) or JSON mode:
// Android — Kotlin data class PaymentFormData( val amount: Double?, val recipientName: String?, val recipientPhone: String?, val purpose: String?, val scheduledDate: String? // ISO8601 or null ) suspend fun parseUserInputToForm(userMessage: String, formContext: String): PaymentFormData { val systemPrompt = """ You are a payment form fill assistant. Form context: $formContext Extract data from the user's message and return JSON. Fields not mentioned — leave as null. """.trimIndent() val response = openAIClient.chatCompletions.create( model = "gpt-4o-mini", messages = listOf( Message(role = "system", content = systemPrompt), Message(role = "user", content = userMessage) ), responseFormat = ResponseFormat(type = "json_object"), temperature = 0.0 ) return gson.fromJson(response.choices[0].message.content, PaymentFormData::class.java) } After parsing, we programmatically fill the fields and show the user a preview for confirmation — the assistant does not submit the form on its own. As reported in the OpenAI documentation, this approach guarantees a strict response structure.
Example prompt for auto-fill
In the system prompt you can specify: 'If the user says "like last time", use the most recent data from history.' This improves relevance.Integration with User Data
The assistant works significantly better when it knows context: a list of saved recipients, payment history, user profile. This context is injected into the system prompt:
// iOS — Swift func buildFormCopilotContext(user: User, formType: FormType) -> String { var context = "Form: \(formType.displayName).\n" if formType == .payment { let recentRecipients = user.recentRecipients.prefix(5) .map { "\($0.name): \($0.phone)" } .joined(separator: ", ") context += "Frequent recipients: \(recentRecipients).\n" } return context } How do we integrate the AI assistant into your app?
The process includes four steps:
- Form analysis — we study your current screens, identify 'bottleneck' fields, collect fill time metrics.
- Prompt design — we develop a system prompt considering context (user type, business logic, security requirements).
- LLM integration — we connect OpenAI or another model, configure Structured Output to your data schema.
- Testing and deploy — we run an A/B test: control group without assistant, experimental group with assistant. After confirming effectiveness, we roll out via TestFlight or Google Play Console.
What's included in our work?
- Source code of the assistant module (Swift/Kotlin) with comments.
- Documentation on prompts and architecture.
- Training for your team on handling LLM outputs and error handling.
- Support for 30 days after release.
Time Estimates
Basic implementation (without voice and context) — 3–5 days. Full assistant with voice input, context, and validation — 1–2 weeks. Cost is calculated individually. We'll assess your project for free — contact us, and we will propose a solution for your stack.
For the assistant to work, a server component for LLM requests is needed. We help choose the optimal model (GPT-4o-mini, Claude, YandexGPT) considering latency and budget.
Order a pilot project — we'll implement the assistant in 5 days and show conversion growth on your forms. Get a consultation on integration today.