Cross-platform mobile studio · AI inside
Polished React Native, iOS, and Flutter apps—each with real AI inside: an in-app assistant, smart search, recommendations, voice, or on-device ML. Plus a growing AI-agents & automation practice, and the whole product around the app. From spec to store, in weeks, not quarters.
MunawarTech is a senior mobile-engineering studio that builds polished cross-platform apps—React Native (Expo) first, with deep Flutter and native iOS—and gives every app a signature AI touch: an in-app AI assistant/chatbot, plus features like smart search, recommendations, voice, or on-device ML. We deliver the whole product around the app too as a single, accountable build.
For mobile we work in React Native (Expo) by default, where 14 years of native iOS make the native-bridge and on-device-ML layer a strength. Our strong, growing second specialty is AI agents & agentic automation—LLM pipelines, RAG, chatbots, tool-calling frameworks—so we can build a standalone agent or pair one with your app. And we use AI-assisted, agentic development to move at a pace traditional teams can't match. The result is founder-speed delivery at senior-engineering quality.
Polished React Native / iOS / Flutter apps on a 14-year native-iOS foundation—native modules, the native bridge, camera pipelines, on-device ML. The hard parts most app devs avoid.
An in-app assistant, smart search, recommendations, voice, or on-device ML—built to genuinely help your users, not AI theater. The signature that sets our apps apart.
Shipped AI agents, automation, LLM pipelines, RAG, and chatbots—delivered standalone or paired with the app, with the honesty to tell you where AI adds value and where it doesn't.
Every engagement starts with a short PRD and design system, then runs in milestones you can see and test—founder-speed timelines, not a black box.
Polished cross-platform builds in React Native (Expo) by default, backed by 14+ years of native iOS—so the hard part of RN (native modules, the native bridge, camera, on-device ML) is our strength. Flutter and native first-class for deep hardware/native work (external cameras, on-device OCR, DSP). Our core lane.
Every app we build ships with a real AI touch inside: an in-app AI assistant/chatbot, plus popular AI features like smart search, recommendations, voice (STT/TTS), or on-device ML. The signature that makes our apps feel genuinely intelligent—real, useful AI, not a wrapper.
Real AI agents and automation, built to ship: multi-LLM pipelines with human-in-the-loop review, RAG over pgvector with grounded citations, chatbots and voice agents, tool-calling / function-calling agent frameworks (MCP-style), natural-language-to-SQL, multimodal ingestion (voice, image, document), and workflow automation—standalone or paired with the app. Genuine, code-backed AI.
Backends and infrastructure that hold up in production: FastAPI/Node/Supabase services, PostgreSQL with row-level security and full-text search, Firebase, AWS serverless authored in Terraform, Dockerized deployments with TLS, and CI/CD pipelines.
Escrow and subscription payments (Stripe, Moyasar, RevenueCat, StoreKit), authentication and RBAC, biometric and government-ID verification, face-liveness anti-spoofing, and GDPR-compliant data handling.
Client engagements and self-built products across mobile, AI, and cloud—each labelled with its honest status: in development, completed & client-reviewed, or a self-built engineering proof.
A complete Arabic/English event-services marketplace connecting customers with vendors—built end-to-end by one accountable team.
Build Flutter mobile app (255 Dart files, clean architecture), a Next.js 16 / React 19 admin & vendor portal with a full RBAC matrix, and a Supabase backend of ~35 tables across 24 row-level-security migrations plus Deno Edge Functions.
Standouts Moyasar authorize/capture escrow with 3DS, two-way WhatsApp Cloud API RSVP with HMAC-verified webhooks, scheduled push via a hand-built service-account JWT, and Arabic full-text search—full RTL/EN.
Outcome Milestones M1–M3 delivered on schedule with 121 user stories tracked—a full marketplace owned end-to-end, in active development.
A privacy-first, verified dating product with two genuine AI/ML systems and strong compliance.
Build An OpenAI-embedding + pgvector (HNSW) semantic compatibility engine with a two-layer cosine matching function, validated by SQL tests. Flutter client with native Swift/Kotlin liveness bridges and a Cloudflare Worker web fallback over a Supabase backend.
Standouts AWS Rekognition face-liveness anti-spoofing plus Veriff government-ID verification with GDPR cascade deletion.
Outcome A documented, working architecture combining real AI matching, real anti-spoofing, and compliance—an AI-plus-security build few can deliver end-to-end.
A fintech investor-demo MVP built fast to a senior bar—clean architecture and price-lock logic.
Build A SwiftUI app on clean MVVM with protocol-oriented dependency injection, a full design system, and price-lock logic—delivered on an accelerated timeline for an investor demo.
Client review "Over-delivered on every milestone, communicated clearly… an excellent developer."
An iOS Swift Package for secure image blur and biometric-gated encryption, shipped with a documented threat model.
Build Metal / Core Image GPU blur, biometric-gated AES-GCM decryption backed by the Secure Enclave, a revoke() path, and XCFramework packaging with CI.
Standouts A documented threat model and a clean, versioned Swift Package API—security engineering delivered as a reusable primitive.
A production iOS app ported to native Android for a long-standing repeat client—full feature parity across both codebases.
Build Ported a production iOS app to native Android over 313 tracked hours, preserving feature parity and platform-native behaviour across both platforms.
Client review "I've worked with Owais on numerous iOS and Android projects over the years… consistently delivered excellence."
The deep native/hardware work most mobile teams won't touch—a commercial C++ OCR SDK bridged into Swift with an external-camera pipeline.
Build An iOS accessibility magnifier bridging a commercial C++ OCR SDK (iDRS 15, 331 headers) into Swift—corner detection and perspective correction—plus a multi / external USB-camera AVFoundation pipeline (iOS 17 .external) with dual-camera split view.
Outcome Deep native and hardware integration delivered—the work that separates senior mobile engineering from app assembly.
Production-grade AI infrastructure—a published SDK and an agent framework with RAG and tool-calling.
Build ticketkit, an npm SDK with pluggable Postgres/SQLite adapters and a workflow state machine, backed by cross-OS matrix CI, Codecov, and auto-changelog.
Standouts @kiara/botkit—six platform adapters, three LLM providers, voice STT/TTS, pgvector RAG, function-calling tools, and cron—plus an agentic tool-calling loop with human-in-the-loop write gating.
Outcome Open-source-grade AI engineering—reliable primitives and agent infrastructure, not demos.
A refined native iOS cloud-storage app on a serverless AWS backend authored entirely as infrastructure-as-code.
Build Native SwiftUI client (~25k LOC) with StoreKit 2 subscriptions, Cognito/Apple/Google auth, and adaptive bandwidth sync.
Standouts The complete serverless backend authored in Terraform—DynamoDB, Node Lambdas, API Gateway, S3, SES.
Outcome Live—a shipped product proving ownership of both a refined native client and provisioned cloud infrastructure in one build.
A location-based public-safety alert app driven by live government geospatial data.
Build Flutter app backed by a Node 20 Firebase Cloud Function running every minute—ingesting the Phoenix Fire Dept ESRI ArcGIS feed, reprojecting coordinates (EPSG:2868 → WGS84), matching incidents to user-selected stations, and fanning out FCM push alerts.
Standouts RevenueCat IAP, Mapbox mapping, Codemagic → TestFlight delivery.
Outcome Shipped to store testing—a real release built on live government geospatial infrastructure.
Converts unstructured input—voice, screenshots, documents—into structured product work through a multi-LLM pipeline.
Build Multimodal ingestion (Whisper, vision, OCR) feeding a multi-provider LLM pipeline (OpenAI, Gemini, Anthropic) that proposes backlog actions with confidence scores and human-in-the-loop review. FastAPI + PostgreSQL/SQLAlchemy + Celery/Redis; Next.js 14 PWA front-end.
Standouts Deployed as a five-service Docker Compose stack with nginx + certbot TLS on a VPS.
Outcome A complete, self-hosted AI product—ingestion to reasoning to UI to production deploy—demonstrating productized, agentic AI engineering.
Also on request: TryVocals (on-device ML vocal coach, CoreML) · News (encrypted messenger) · Budget App · SalesLog—a mix of client builds and self-built engineering proofs.
Scoped, milestone-based delivery for well-defined products. Ideal for MVPs where you want a clear price and a launchable result.
Continued development, maintenance, and iteration—senior engineering capacity you can rely on past launch.
A paid first engagement: PRD, architecture, and a build plan you own—regardless of who ultimately builds it.
Best fit for founders and companies needing a polished cross-platform app—React Native / iOS / Flutter—with a real AI touch inside, delivered as one integrated system. AI agents and automation too, standalone or paired with the app.
info@munawar.tech →