Your app is only half of what you're shipping. The APIs, real-time services, auth systems, AI pipelines, and data layer that make it work are all designed for a phone from the start (including the LLM endpoints, RAG retrieval, and on-device ML) rather than borrowed from a web API and patched to fit.
Backend and AI tools we pick for reliability.
A web API written for a desktop on a wired connection is the wrong fit for a phone on 4G that drops out in a tunnel. Your mobile backend has to keep responses small, tolerate going offline, and push reliably, so it's designed for the phone from the beginning.
The API was built for the admin dashboard. The mobile app calls those same endpoints and gets payloads ten times larger than it needs. Anyone on a metered connection feels it, and the app comes across as slow.
Every action needs a live connection. Indonesian users on patchy 4G get spinners and errors instead, and a fair number of them move to a competitor's app that keeps working offline.
They were wired up in a single sprint with a library nobody fully understood. Now notifications show up late, arrive twice, or never appear on certain Android versions. People just switch them off.
Tokens are stored insecurely, session handling has gaps, and password reset can be replayed with an old token. A security audit would surface all of it on day one.
A chatbot was added without RAG, without caching, and without a rate limit. Every chat sends 8k tokens to the LLM, the bill spikes, and on a 4G connection the user waits four seconds for the first character to land.
Each step has a clear deliverable and a written handoff, and we get your sign-off before moving to the next one.
Before any code, we settle the endpoints, request and response shapes, pagination, error formats, versioning approach, and auth flow. Your mobile team reviews and signs off on the contract before backend work starts.
We design the AI layer end to end: model selection, prompt engineering, RAG with a vector store for your app data, token budgeting, streaming responses for low perceived latency, caching, rate limiting, and fallback behaviour when the model is slow or down. On-device ML with Core ML or TensorFlow Lite is wired in where it makes sense for privacy or offline.
Standards-based auth using JWT with refresh tokens, OAuth2, or Firebase Auth. We add role-based access control, device binding where it makes sense, and guidance on how the mobile client should store tokens.
REST or GraphQL with input validation, rate limiting, sensible error codes, and structured logging. Response payloads are kept to what the phone actually needs and nothing more.
WebSocket or SSE for real-time features, push infrastructure across APNs and FCM with delivery tracking, and offline sync with conflict resolution that fits your data model.
We load-test the API at production-grade volumes before store submission, then hand over an OpenAPI spec, documentation, and a runbook your team can work from.
Real deliverables you can point to and outcomes you can measure. Not a slide deck.
Response size is treated as a design constraint from the first day, not something we look at later.
Offline-first patterns with sync and conflict resolution keep the app usable in lifts, tunnels, and areas with poor coverage.
APNs and FCM with delivery tracking and retry logic, tested across iOS and Android versions before launch.
It's documented in OpenAPI, versioned, and tested, so adding a feature doesn't mean booking a backend meeting for every integration question.
We treat AI as real engineering rather than a chat widget. We pick the model (OpenAI, Anthropic, Gemini, or self-hosted Llama) based on cost, latency, and privacy. We add RAG with a vector store so the LLM has your app data as context, stream responses so the user sees the first token quickly, cache aggressively, rate-limit per user, and design a fallback for when the model is slow or down. On-device inference with Core ML or TensorFlow Lite is added for offline cases or anything that has to stay on the device.
Firebase makes sense when you need to move quickly, have little business logic, and can live with being tied to one vendor. A custom backend makes sense when you need complex queries, full ownership of your data, predictable costs as you grow, or freedom over your infrastructure. We'll give you a straight recommendation based on your situation.
We design the sync strategy around your specific data model. Straightforward cases can use last-write-wins. Trickier ones, like collaborative editing or a shared cart, need their own logic. We settle this at the architecture stage rather than waiting for it to cause a production bug.
Yes. We review the existing API for how well it suits a mobile client, document what's there, point out the gaps, and then either extend it or add a lightweight backend-for-frontend layer that reshapes it for the app.
Both, and we pick based on your app. REST is simple and cache-friendly and fits most apps well. GraphQL helps when screens need very different shapes of data and you want to avoid over-fetching on a metered connection. Either way we keep payloads small, version the API, and document it in OpenAPI or the GraphQL schema.
We use WebSocket or SSE for live data, with reconnection logic and a fallback for flaky mobile connections. For chat, presence, or live order tracking, messages queue while offline and sync when the connection returns, so the feature degrades gracefully instead of breaking on patchy 4G.
We send through APNs for iOS and FCM for Android, with a server-side layer that tracks tokens, handles delivery and retries, and reports failures. We test across OS versions, handle permission prompts correctly, and support rich and silent notifications where your app needs them.
Standards-based auth: JWT with refresh tokens, OAuth2 for social and third-party login, or Firebase Auth when it fits. We add role-based access control, secure token storage on the device (Keychain on iOS, Keystore on Android), and optional device binding. We also support biometric unlock tied to a securely stored session.
Yes. We integrate LLMs (OpenAI, Anthropic, Gemini, or self-hosted Llama) with RAG over your data, streaming responses for low perceived latency, caching, and per-user rate limits. For anything that must stay private or work offline, we run on-device inference with Core ML or TensorFlow Lite. Cost and latency are designed in from the start, not patched later.
We design for horizontal scaling from the start: stateless services behind a load balancer, a database with read replicas and sensible indexing, caching with Redis, and async jobs for heavy work. We load-test at production-grade volumes before launch so the first traffic spike is something we have already rehearsed.
Tell us where the project stands right now. Within one working day we'll come back with a straight read on scope, timeline, and cost. There's no commitment in asking.
Office
Surabaya, Indonesia
Starting price
From USD 4,000
Typical projects: USD 4,000–25,000
