ScalaCode builds and deploys production AI applications — mobile-first AI experiences, web AI platforms, multi-tenant SaaS, vertical AI tools, and AI-native enterprise apps — for clients across 45+ countries. With 13+ years of full-stack engineering experience plus deep AI/ML, our teams ship AI apps end-to-end: from model selection and fine-tuning through native iOS/Android, scalable backends, payment integration, and the observability infrastructure that keeps AI products dependable in production.
Whether you need an iOS app with real-time AI virtual try-on for fashion eCommerce, a multi-tenant AI SaaS for tech recruitment, a computer-vision-driven web platform for AEC takeoff, or an AI-powered fleet optimization dashboard at 10,000+ vehicle scale, our AI app engineers architect solutions that move the metrics that matter — time-to-market, conversion rate, cost-per-AI-call.
Our AI app development services span the complete stack — from user-facing mobile and web apps to the AI/ML infrastructure that powers them. Below are the service lanes we ship most often in 2026.
Swift/SwiftUI for iOS, Kotlin/Jetpack Compose for Android, React Native and Flutter for cross-platform. Every AI-native mobile build includes on-device inference where privacy demands it, cloud inference where capability demands it, and a smart orchestration layer that chooses between the two per query.
React, Next.js, Vue, SvelteKit, Angular — with AI capabilities exposed through streaming interfaces, Server-Sent Events, and WebSocket-driven real-time UI. Edge runtime deployments on Vercel, Cloudflare Workers, and AWS Lambda@Edge for sub-100ms LLM interactions.
iOS Core ML, Apple Intelligence foundation models, Android LiteRT (formerly TF Lite), ONNX Runtime Mobile, MLC LLM, llama.cpp mobile builds. We fine-tune and quantize open-source models (Llama 3.3, Phi-4, Gemma 3, Qwen 3) for the 4–8GB RAM budget of modern phones — delivering sub-second local inference without burning battery.
Chat, search, summarization, draft-assist, creative generation, voice-to-action, image and video generation, and document understanding — built on GPT-5, Claude, Gemini 2.5, Llama 3.3/4, Mistral Large, and domain-fine-tuned open-source models. See our generative AI development services for the underlying foundation-model layer.
In-app AI agents that plan multi-step tasks — book a flight, reconcile a bill, draft a proposal, onboard a new hire — using tools, retrieval, and self-critique. Built on OpenAI Assistants API, CrewAI, LangGraph, and emerging Model Context Protocol (MCP) standards. See our AI agent development services.
Always-on voice assistants, multilingual speech interfaces, barge-in and streaming TTS, and real-time translation — built on Whisper, Deepgram, ElevenLabs, Sesame, and custom on-device STT for privacy-critical contexts.
Vision-language apps that reason across image, video, audio, and text simultaneously — product search from a photo, medical imagery triage, document QA from scans, video understanding. Built on GPT-5 Vision, Claude Vision, Gemini 2.5 Multimodal, SigLIP, CLIP-L, and fine-tuned multimodal transformers.
In-app recommendation surfaces — product suggestions, content feed ranking, session-based discovery, cold-start handling, contextual personalization. See our AI recommendation engine services for the ranking stack that powers these surfaces.
Chat-native copilots embedded in CRM, HRIS, ERP, help desk, and workflow tools. Typically grounded via RAG development against enterprise knowledge bases so the copilot’s answers are backed by your real data, not generic web-crawl training.
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Apple Intelligence’s on-device 3B model, Google’s Gemini Nano, Samsung’s Galaxy AI stack, and open-source Llama 3.3/Phi-4/Gemma 3/Qwen 3 quantized builds are making real local LLM inference practical for the first time. Apps that combine on-device first + cloud fallback deliver privacy and responsiveness at lower cost than pure cloud architectures.
Instead of users navigating menus to complete a multi-step task, an in-app agent plans the steps, uses tools to execute, and returns with the result. Travel apps book trips, finance apps reconcile expenses, enterprise apps onboard employees — all from a single natural-language ask.
Camera + voice + text input is expected, not novel. Apps that demand users type are leaving user value on the table. Point-and-ask, voice-first, and gesture-triggered interactions are the 2026 norm for consumer and prosumer apps.
Token-by-token streaming is table stakes. Advanced patterns include partial tool-use streaming, interactive partial results (let the user click a streamed element before the response completes), and streaming multimodal outputs.
Apps that remember user context across sessions — preferences, history, ongoing tasks — using vector memory stores, summarization-based memory, and structured profile stores. Memory changes the product from stateless assistant to personal collaborator.
Always-listening voice interfaces with barge-in, low-latency streaming TTS (ElevenLabs Turbo, Sesame, Deepgram Aura), and multilingual handling. Especially relevant for field apps, automotive, healthcare, and accessibility use cases.
MCP is standardizing how apps and agents connect to tools and data sources. Apps that adopt MCP get immediate access to the broader ecosystem of MCP-compatible tools — and become interoperable with any MCP-aware LLM. See our AI integration services for MCP-native implementation patterns.
Not everything needs a 70B-parameter model. Classical ML for classification, search, ranking, and anomaly detection — with LLMs reserved for the reasoning and generation steps — delivers dramatically lower cost and latency without quality compromise.
Need AI app specialists on your own roadmap? Our staff augmentation program places senior AI-fluent app engineers into your team.
Most AI app prototypes fail in production for the same few reasons: poor fit between on-device and cloud, weak handling of low-connectivity states, brittle prompt layers, no observability, and retrofit integrations that break under real load. Our method addresses each in the architecture phase.
Before a single prompt is written, we map the capability surface: what the user wants to do, what the device can support, what data must stay on-device, what latency budget the experience demands, what happens offline, and what happens under rate limits. The output is an architecture one-pager with clear decision rules for on-device vs. cloud.
Models are benchmarked against the actual app scenarios — not generic MMLU scores. We select a primary model, a cost-optimised secondary, and a local fallback for connectivity gaps. Smart routing picks the right model per query based on complexity, latency, privacy, and cost. Savings vs. single-model deployments: typically 30–60% on inference spend.
Streaming tokens, thinking indicators, partial-result UIs, confidence badges, citation surfaces, regeneration controls, and graceful error fallbacks. These aren’t polish — they are the difference between an AI feature that users trust and one they abandon after two tries.
Prompts are versioned, tested, and evaluated like code. We use structured outputs (OpenAI Structured Output, Anthropic Tool Use, JSON mode) to guarantee parseable responses. Prompt injection defenses, refusal handling, and bias guardrails are baked in — not retrofitted post-incident.
Function calling, tool use, and MCP (Model Context Protocol) connect the model to your app’s real services — booking systems, CRMs, payments, search. Tool schemas are validated, tool outputs are checked, and failures fall through to sensible retry or human-handoff patterns.
Every AI surface ships with a golden-set evaluation harness (RAGAS, TruLens, DeepEval, LangSmith, Arize Phoenix) and production monitoring. Faithfulness, answer quality, safety, and cost per interaction are tracked per cohort. Drift alerts fire when quality slips below threshold.
Prompt caching, response caching, speculative decoding, batching, distillation to smaller models, and hybrid on-device/cloud routing. Most apps we work on see 40–70% inference cost reduction between the first production version and the fifth.
PII masking, data minimization, on-device inference for sensitive contexts, differential privacy for analytics, SOC 2, HIPAA, GDPR, and India DPDP compliance wiring where applicable. The compliance architecture is part of the app design — not bolted on after launch.
We pair senior mobile/web app engineers with AI/ML specialists on every engagement. The intelligence layer is co-designed with the user experience — not handed off between siloed teams.
Every AI app ships with evaluation harnesses, observability, guardrails, cost dashboards, and on-call runbooks. Prototypes live in Jupyter notebooks — we don’t.
Few agencies are equally fluent in Core ML / LiteRT quantized deployments AND cloud LLM orchestration. That dual fluency drives architecture decisions that unlock privacy, latency, and cost simultaneously.
HIPAA, SOC 2, GDPR, India DPDP, CCPA/CPRA — our apps ship with audit logs, PII masking, consent workflows, and data residency controls appropriate to your regulatory posture.
We measure inference cost per user per month and optimize ruthlessly — prompt caching, model routing, distillation, batching. Our apps typically run 40–70% cheaper than first-pass builds by month 6.
Design, engineering, model fine-tuning, infra, deployment, operations — under one roof. No handoffs that break context. No vendor chains that slow decisions.
Clinician co-pilots, patient-facing symptom triage, medical imagery analysis, clinical note summarization, medication reconciliation — all HIPAA-aligned with PHI isolation and audit logging.
Visual product search, conversational shopping, personalized discovery, review synthesis, AR try-on paired with AI styling. See our recommendation engine services for the personalization stack.
Embedded copilots in CRM, HR, finance, project management, support ticketing. RAG-grounded responses make copilots defensible for enterprise compliance teams.
Capability audit, model benchmark, architecture recommendation, cost/latency model, phased roadmap. Starting at $20k–$45k.
Production-ready MVP on one core use case with evaluation harness, observability, and stakeholder acceptance. Ideal for enterprises validating the business case with real users.
Complete AI-native mobile or web app with full feature set, compliance hardening, multi-platform delivery, and 90-day post-launch support.
Embedded squad — app engineers, AI/ML engineers, prompt engineer, MLOps engineer, QA, designer — running with your product org for 6+ months.
Post-launch operations: model upgrades, prompt tuning, evaluation monitoring, cost optimization, new feature integration, security patching. SLA-backed.
AI-native mobile banking app with on-device fraud detection. App Store rating 4.8, session time +47%, fraud loss rate -38% in month 6.
Clinician copilot embedded in clinical workflow. Documentation time -52%, clinician satisfaction +3.1 points on NPS.
Embedded AI copilot across CRM + ticketing. Tier-1 ticket deflection 54%, customer onboarding time -41%.
Multimodal shopping app with visual search and conversational discovery. CVR +29%, session depth +35%, return rate -18%.
Adaptive tutoring app with on-device inference for K-8 audience. Daily active use +62%, parent trust score +41% after on-device deployment.
Voice-first technician assistant with low-connectivity operation. First-time fix rate +28%, mean time to resolution -34%.
Traditional app development builds static features with deterministic logic. AI app development builds apps where core user value comes from learned, generative, or agentic capabilities — natural-language interaction, personalized recommendations, multimodal understanding, autonomous workflows. Architecturally, AI apps require model selection and routing, prompt/evaluation frameworks, observability for non-deterministic outputs, graceful fallback handling, and cost optimization disciplines that traditional apps don’t need.
On-device wins when privacy is non-negotiable, latency must be sub-200ms, the user is offline, or cost per interaction matters at scale. Cloud wins when capability matters more than privacy, when answers need access to live web or enterprise data, or when models are too large to fit on device. The right architecture uses both — a hybrid router picks the right side per query. Apple Intelligence, Gemini Nano, and quantized open-source models (Llama 3.3, Phi-4, Gemma 3) have made on-device LLMs practical for the first time in 2026.
AI app discovery sprints start at $20k–$45k. AI app MVPs land $75k–$200k over 6–12 weeks. Full production AI apps range $200k–$800k+ depending on platform count, model fine-tuning, compliance requirements, and integration scope. Ongoing inference costs scale with user activity — typical ranges are $0.50–$8.00 per active user per month, with heavy optimization opportunities as volume grows.
All four. Native iOS (Swift / SwiftUI) and Android (Kotlin / Jetpack Compose) for apps where platform integration (Apple Intelligence, Galaxy AI, Core ML, LiteRT) and performance are non-negotiable. React Native or Flutter when time-to-market and shared codebase matter more than deepest platform optimization. Next.js / React / Vue for web and PWA deliveries. We help you choose based on audience, required platform APIs, and release cadence.
Layered defenses: (1) strict input sanitization and rate limiting, (2) system-prompt hardening with explicit refusal triggers, (3) output validation (structured outputs, regex guards, content classifiers), (4) tool-call validation so the model cannot trigger dangerous actions without human approval, (5) red-teaming during development, and (6) production monitoring for prompt-injection signatures. Prompt injection is not a solved problem — it is a managed risk, and the management discipline should be part of the app’s design.
Generative features produce content — a draft email, a summary, an image. They are single-turn and stateless. Agentic features plan multi-step tasks and execute them using tools — booking a flight, reconciling a report, onboarding an employee — often across multiple turns and with memory. Agentic apps use OpenAI Assistants API, CrewAI, LangGraph, or similar frameworks and typically connect to MCP-compatible tools. Most modern AI apps blend both: generative capabilities for content, agentic capabilities for workflows.
Seven levers: (1) prompt caching to avoid re-computing common context, (2) semantic caching for repeat questions, (3) model routing — cheaper models for easier queries, (4) distillation to fine-tune smaller open-source models on your domain, (5) streaming + early termination, (6) batching for non-real-time workloads, (7) on-device inference where latency and privacy allow. A production AI app that is 6 months past launch should cost 40–70% less per interaction than its first production build.
Yes. We run fine-tuning engagements for OpenAI, Anthropic, Google, and open-source models (Llama, Mistral, Qwen, Phi, Gemma). Fine-tuning is the right move when prompting hits a quality ceiling, when your domain vocabulary is unusual, or when latency budgets demand a smaller model. We start with a golden-set evaluation to measure whether fine-tuning improves quality meaningfully before committing to the full training run. See our LLM development services for the deeper model-engineering lane.
A focused MVP with one core AI use case typically reaches production in 8–12 weeks. Multi-feature enterprise AI apps with compliance hardening and multi-platform delivery run 4–6 months. The fastest credible path to first production value is 5–7 weeks if your backend APIs are ready and scope is kept to one user surface. Complexity spikes come from on-device model fine-tuning, multi-tenant isolation, and regulated-industry compliance certification.
Yes. Enterprise integration is our core discipline, not an afterthought. We wire AI apps to Salesforce, HubSpot, SAP, Oracle, Workday, ServiceNow, Zendesk, Jira, and custom APIs using native connectors, event-driven pipelines, MCP servers where available, and RAG-grounded data access where appropriate. See our AI integration services for enterprise-grade integration patterns and RAG development for knowledge-grounded response layers.
They delivered on time and even offered ideas I hadn’t considered. Their proactive input and execution exceeded my expectations.
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