Lua Global Inc

What do they actually do

Lua provides a developer platform to build conversational AI agents that call real APIs and take actions (e.g., order lookup, bookings, checkout). Engineers use a TypeScript-first CLI to implement "tools," group them into skills, test locally, and deploy; the agent is then exposed via an embeddable chat widget and channels like WhatsApp and Instagram docs overview, chat widget.

The platform offers optional helpers (e.g., e‑commerce, vector search) while emphasizing “your APIs first,” and the website highlights enterprise readiness (auth/session isolation, GDPR/SOC 2, data residency, SLAs) overview, homepage. Lua also runs public WhatsApp/web agents and brand pilots, as referenced in its Terms and social updates Terms, Instagram, LinkedIn.

Who are their target customer(s)

• Mid-market retail and e‑commerce brands (online + stores): High volumes of order/status/cart questions across web and messaging lead to lost sales when customers can’t check inventory or complete checkout quickly; they need actions like order lookup and payment without heavy engineering work.
• Restaurant and hospitality groups with multiple locations: Peak-time and delivery/booking messages overwhelm staff on WhatsApp and web, causing missed orders and long waits; they need reliable, data‑connected flows that can place bookings or orders end‑to‑end.
• Customer support teams at mid‑market companies: Agents spend time copying order IDs, fetching tickets, and repeating troubleshooting steps, slowing response and raising costs; they need assistants that pull context and update records in existing systems.
• Engineering teams responsible for customer‑facing systems: They’re asked to ship bots that safely call production APIs and meet privacy/compliance needs, but lack a repeatable developer workflow, staging/versioning, and deployment tooling to run agents in production.
• Marketing and commerce teams running multi‑channel campaigns: They want consistent, interactive flows across web, WhatsApp, and Instagram, but rely on separate vendors and manual handoffs; launches are slow and analytics are fragmented without reusable templates/connectors.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led pilots with known restaurant/retail partners using the Lua CLI and LuaPop widget to wire 2–3 core tools (order lookup, booking, checkout) and prove impact on time‑to‑answer and completed transactions; convert pilots via a simple pilot‑to‑subscription path docs overview, chat widget.
• First 50: Package what worked into 1–2 week templates (Shopify/cart, booking, WhatsApp flow, integration checklist) and use case studies to target similar mid‑market customers; onboard local agencies and WhatsApp BSPs to implement with prebuilt connectors Shopify integration, chat widget.
• First 100: Launch a self‑serve template marketplace and tiered plans (self‑serve, managed onboarding, enterprise SLA/compliance). Recruit regional SIs, commerce partners, and messaging resellers; standardize onboarding and training so partners can reuse work overview.

What is the rough total addressable market

Top-down context:

Analysts size conversational AI at about $11.6B in 2024, growing to roughly $41.4B by 2030, with retail/e‑commerce and customer service among leading adopters Grand View Research, retail adoption note.

Bottom-up calculation:

Illustratively, if Lua sells into 5,000–10,000 mid‑market accounts across retail, hospitality, and support at $10k–$40k ACV for multi‑channel, API‑connected agents, that implies roughly $50M–$400M in ARR potential. This excludes very small businesses and do‑it‑yourself enterprise builds to stay realistic.

Assumptions:

• 5,000–10,000 relevant mid‑market accounts globally in retail/e‑commerce, hospitality, and support that buy third‑party agent platforms.
• Average ACV ranges $10k–$40k based on multi‑channel deployments with API integrations and basic support/SLA.
• Penetration excludes micro‑SMBs and large enterprises building in‑house, focusing on mid‑market buyers.

Who are some of their notable competitors

• Botpress: Open‑source, developer‑first bot platform with a TypeScript client and embeddable webchat; overlaps for teams building and hosting data‑connected chat agents themselves docs.
• Rasa: Enterprise‑grade, open‑source conversational platform for backend integrations and messaging channels, with options for on‑prem/compliant deployments and managed services channels/enterprise.
• Ada: Omnichannel customer‑service automation for support/commerce teams (no‑/low‑code flows, prebuilt integrations), competing where non‑engineering teams want managed automation across web and messaging platform.
• LivePerson: Conversational cloud provider bundling WhatsApp/web messaging, commerce workflows, orchestration and managed services; strong with buyers prioritizing turnkey integrations and SLAs WhatsApp/commerce.
• LangChain (+ LangSmith): Developer framework and observability stack for agent/tooling pipelines; overlaps with Lua for custom agent logic but focuses more on framework/infra than widgets + channel delivery overview.