Genai Labs

Discover — Map the opportunity

Workshops, data audits, competitor analysis, stakeholder research, and workflow assessment identify where AI can create leverage and where it may not be appropriate.

Design — Architect the system

Models, data flows, integrations, permissions, guardrails, and user experience are designed together so the system can support real-world use and load.

Build — Ship in weeks

The team uses tight iteration loops, weekly demos, real-scenario testing, and production-grade engineering from the beginning.

Scale — Operate and improve

Observability, evaluations, monitoring, model updates, prompt and logic refinement, and continuous improvement help the AI system become more effective over time.

The workflow automation practice also includes deployment across teams and ongoing optimization. AI agent projects include testing against real questions, documents, and edge cases before launch, followed by guardrailed deployment.

• Email: eitan@genai-labs.io
• Phone: (323) 522-4040
• Location: Los Angeles, CA 90064

The contact form includes budget ranges from $5,000–$10,000 through $250,000+, an option to protect the project with an NDA, and a selection of international dialing codes.

Business workflow automation

AI-powered automations connect to existing tools, documents, databases, and business processes to reduce repetitive work, eliminate manual handoffs, improve consistency, and help teams focus on higher-value work. Services include workflow discovery, automation design, integration and testing, deployment across teams, and ongoing optimization and maintenance.

AI consulting and strategy

GenAI.Labs identifies high-value AI opportunities, assesses feasibility and data readiness, and creates practical implementation roadmaps. Consulting may include business and workflow assessment, opportunity mapping, architecture review, roadmap development, and implementation support.

AI app and product development

GenAI.Labs designs, builds, and deploys AI-powered mobile apps, web apps, websites, SaaS platforms, internal tools, and investor-ready MVPs. Product work can include product strategy, UX and prototype design, frontend and backend development, databases, APIs, authentication, admin tools, AI integrations, launch, scaling, and post-launch iteration.

AI agents and knowledge systems

GenAI.Labs builds AI agents, chatbots, and retrieval-augmented generation (RAG) systems that search trusted information, answer questions, summarize documents, support decisions, and trigger workflows. Systems can include source grounding, permissions, escalation paths, monitoring, fallbacks, and human review points.

• Media intelligence: A unified platform connected more than 20 sources, enabled live campaign monitoring, supported natural-language queries, reduced manual reporting by more than 90%, and helped each analyst carry three times the campaign load.
• San Diego Tourism Authority: More than 40 stakeholder interviews informed a roadmap and three command centers. The work reduced manual reporting by more than 50% and created live profiles for more than 1,000 member relationships.
• Echocardiogram analysis: A computer-vision system processed more than 5 million ultrasound frames, achieved 99%+ analysis accuracy, reduced analysis time by 96%, and improved diagnostic precision by 80%.
• PGA tournament intelligence: More than 30 solutions were scoped across tournament operations. The roadmap targeted 90% less manual reporting, 50% less sales outreach time, and 40% less administrative overhead.
• Enterprise software support: An AI chatbot processed complex queries across millions of records daily, doubled cases handled per day, helped identify 12% more cases, and reduced response time by 40%.
• Recruiting automation: The AgentVIP platform increased applicant throughput tenfold, reduced outreach time by 80%, and moved candidates from job posting to scheduled interview in real time.
• Ray-Ban Amazon operations: Generative AI doubled customer satisfaction, reduced PPC costs by more than 30%, increased sales by 7%, and reduced time spent by 80%.

The engagement includes:

• Days 1–2: scope lock and core-loop definition
• Week 1: authentication, data model, and core AI feature in a real environment
• Week 2: full feature set and tight iteration loops
• Week 3: real-data testing, edge-case handling, polish, and deployment
• A live deployed product
• Full code ownership
• Weekly demos
• A v2 roadmap
• Post-launch support

AI App Repair

The AI App Repair service helps take an AI-built prototype from demonstration stage to a stable, secure, production-grade product.

The work proceeds through four stages:

1. Diagnostic: Review architecture, security, data handling, and reliability, with a plain-English report.
2. Stabilize: Address crashes, security holes, and data risks.
3. Make it production-grade: Add testing, error handling, monitoring, performance improvements, and stronger structure.
4. Launch and build forward: Deploy a documented, stable codebase, with the option to continue building the product roadmap.

The service is designed for apps that fail with real users, real data, or real load; suffer regressions when new features are added; or need security and reliability work before charging customers.