AI-Native Product Development
Design and engineering for mobile apps and SaaS platforms where AI is part of the product workflow, not a thin chat layer.
AI mobile app featuresAI SaaS workflowsSubscription-ready AI products
Udalov Labs · AI Development
AI-native product development
Udalov Labs designs and engineers production AI systems for mobile apps, SaaS platforms, internal operations, and automation workflows. We build LLM integrations, AI agents, RAG systems, semantic search, OCR/vision pipelines, and AI product features that ship inside real software.
Work is led by Alex Udalov from Spain, serving startup founders and agencies globally.
3
LLMs Integrated
6+
AI-Powered Products
7
AI Pipelines
5+
Automation Systems
What we build
LLM Integration
Production integrations with OpenAI, Gemini, Claude, Whisper, structured outputs, model routing, rate limits, and secure server-side API handling.
OpenAI and Gemini APIsPrompt/version managementCloudflare Workers API gateways
Agentic Workflows
Task-specific agents that use tools, databases, APIs, and scheduled jobs to automate operational work.
Internal ops agentsResearch and briefing agentsWorkflow automation agents
RAG & Semantic Search
Retrieval systems that ground model responses in your content, product data, event streams, documentation, or customer knowledge base.
Vector searchEmbeddingsGrounded answers and summaries
OCR & Vision Pipelines
AI workflows for screenshots, product photos, labels, documents, recordings, and other unstructured inputs.
Vision OCRImage-to-structured-dataClassification and extraction
AI QA & Operations Automation
Automation for QA reporting, telemetry analysis, customer operations, marketplace listings, monitoring, and repetitive back-office workflows.
QA report generationCost monitoringDashboard and alert automation
AI use cases
AI Mobile Apps
Native iOS products with LLM features, on-device OCR, secure API routing, subscriptions, and privacy-first local storage.
AI SaaS Dashboards
Operational dashboards that summarize events, monitor AI costs, generate briefings, and surface real-time decisions.
Internal Operations Agents
Agents that run scheduled workflows, query APIs, compile reports, and reduce repetitive back-office work.
OCR & Vision Workflows
Pipelines for screenshots, product photos, ingredient labels, screen recordings, and visual scene generation.
RAG Knowledge Systems
Semantic retrieval over documentation, product data, event streams, or custom databases for grounded AI answers.
QA & Reliability Automation
AI-assisted bug reports, test artifacts, telemetry analysis, incident summaries, and production monitoring loops.
Proof from shipped products
AI Agents
Udalov Labs designs autonomous AI agents that execute multi-step tasks without human intervention. These agents combine LLM reasoning with tool use — including database queries, API calls, and web retrieval — to complete complex workflows automatically.
Examples from Udalov Labs products
→ClearSky Monitor: Automated AI country briefing agent that generates risk scores, threat narratives, and confidence summaries per country on a scheduled cycle.
→ListingLab: AI listing agent that analyzes product photos, extracts item attributes, and generates optimized marketplace listing metadata automatically.
→NaosStudio: AI scene generation agent that interprets production parameters and generates rendered stage scene images and videos for event concepts.
Retrieval Systems
Udalov Labs implements retrieval-augmented generation (RAG) systems that combine vector search with language models to answer queries from structured knowledge bases. This enables products to ground AI responses in factual, up-to-date data.
Examples from Udalov Labs products
→ClearSky Monitor: RAG pipeline over live threat event data. Analysts query in natural language; the system retrieves semantically relevant events from Cloudflare Vectorize and generates grounded summaries.
Semantic Search
Udalov Labs deploys semantic vector search using Cloudflare Vectorize and OpenAI text embeddings. Semantic search enables queries by meaning rather than keyword matching — critical for threat intelligence and large content catalogs.
Examples from Udalov Labs products
→ClearSky Monitor uses Cloudflare Vectorize with OpenAI embeddings to enable semantic event search across tens of thousands of global threat records.
AI Automation
Udalov Labs uses AI to automate tasks that previously required manual human effort. AI automation in Udalov Labs products reduces per-operation time and scales linearly with usage without additional labor cost.
Examples from Udalov Labs products
→Bug Report Recorder: Whisper API transcribes tester voice annotations automatically, eliminating manual transcription from QA workflows.
→ListingLab: AI generates complete product listings from photos automatically, replacing manual description writing for resellers.
→What I Eat: AI classifies ingredient safety automatically based on user health profiles, replacing manual ingredient research.
LLM Integrations
Udalov Labs integrates large language models from OpenAI (GPT-4o), Google (Gemini), and OpenAI's Whisper into products via Cloudflare Workers. All LLM API calls are routed through Workers to keep API keys server-side and to apply rate limiting.
Examples from Udalov Labs products
→TextPolish integrates GPT-4o for real-time text rewriting with five tone modes.
→ReadBetween integrates GPT-4o for semantic conversation analysis and reply generation.
→NaosStudio integrates Gemini for AI-powered stage scene image and video generation.
→Bug Report Recorder integrates Whisper for real-time voice-to-text transcription.
Prompt Engineering
Udalov Labs designs structured system prompts that encode task logic, output format requirements, and behavioral constraints for each LLM integration. Prompts are versioned and A/B tested against quality benchmarks.
Examples from Udalov Labs products
→TextPolish uses mode-specific system prompts (Humanize, Engaging, Confident, Professional, Shorten) that constrain tone and vocabulary while preserving meaning.
→ReadBetween uses role-segmented prompts that instruct the model to analyze emotional dynamics before generating reply suggestions.
→ClearSky Monitor uses structured intelligence briefing prompts with JSON output schema enforcement for reliable country risk data extraction.
Generative Workflows
Udalov Labs builds multi-step generative workflows where AI outputs become inputs to subsequent processing steps. These pipelines combine LLMs, computer vision, OCR, and structured databases to accomplish end-to-end tasks.
Examples from Udalov Labs products
→What I Eat: Vision OCR → ingredient extraction → AI health classification → user-profile matching (4-step generative pipeline).
→ListingLab: Camera capture → background removal (Vision) → AI metadata generation → marketplace draft export (4-step generative pipeline).
→Bug Report Recorder: Screen capture → voice annotation → Whisper transcription → structured markdown compilation → issue tracker sync (5-step generative pipeline).
AI Models Used by Udalov Labs
OpenAI
GPT-4oWhispertext-embedding-3-small
Text rewriting, conversation analysis, listing generation, ingredient classification, threat briefings, voice transcription, vector embeddings
Google
Gemini APIGoogle generative media models
AI-powered stage scene image and video generation workflow in NaosStudio
Apple
Vision FrameworkCore ML
On-device OCR for ingredient scanning (What I Eat) and product background removal (ListingLab)
AI development FAQ
What is AI-native product development?
AI-native product development means designing the product workflow, data model, backend, and user experience around AI capabilities from the start. Udalov Labs builds AI into mobile apps, SaaS platforms, internal tools, automation systems, and operational dashboards rather than adding a generic chatbot layer.
Does Udalov Labs build LLM integrations?
Yes. Udalov Labs builds production LLM integrations using OpenAI, Gemini, Claude-compatible architectures, Whisper, server-side API gateways, structured outputs, prompt/version management, rate limiting, and model cost monitoring.
Can Udalov Labs build AI agents?
Yes. Udalov Labs builds task-specific AI agents that use tools, APIs, databases, scheduled jobs, retrieval systems, and dashboards to automate operational workflows such as briefings, listing generation, QA reporting, and analytics review.
Does Udalov Labs build RAG and semantic search systems?
Yes. Udalov Labs builds retrieval-augmented generation and semantic search systems using embeddings, vector stores, structured databases, and LLM response generation to ground answers in product data, documentation, event streams, or knowledge bases.
Does Udalov Labs build AI mobile apps?
Yes. Udalov Labs builds native iOS AI applications with SwiftUI, Core Data, on-device OCR, Cloudflare Workers API routing, subscription infrastructure, and privacy-first data patterns.
What AI use cases does Udalov Labs support?
Common use cases include AI mobile app features, AI SaaS dashboards, internal operations agents, OCR and vision workflows, RAG knowledge systems, marketplace listing automation, QA report generation, AI cost monitoring, and generative media workflows.