Lilac Labs

What do they actually do

Lilac Labs makes a voice agent that takes drive‑thru and phone orders. It listens at the speaker, handles menu navigation and modifiers, confirms the order, offers add‑ons, and then sends the finalized ticket into the restaurant’s POS and kitchen systems. It’s delivered as an on‑prem + cloud setup that works with existing drive‑thru speakers/headsets where possible, and includes menu management, analytics, and a human fallback when the AI is uncertain [drive‑thru product pages: https://www.drive-thru.ai/ and https://www.lilaclabs.ai/solutions/drive-thru].

In practice, Lilac processes noisy lane audio, extracts items/modifiers, confirms with the guest, and pushes orders to integrations such as Toast, Square, and PAR/Aloha, with hardware partners like HME/PAR. Store managers can view performance metrics such as order patterns, peak times, and upsell impact in a dashboard [https://www.drive-thru.ai/]. The company publishes an implementation timeline of 2–3 weeks and reports outcomes like >95% order accuracy and 15–20% revenue lift from upsells; these are company‑reported figures and should be validated in broader rollouts [https://www.drive-thru.ai/; YC launch: https://www.ycombinator.com/launches/LRU-lilac-labs-drive-thru-order-taking-with-voice-ai].

Today they are in pilots and early production with QSR operators and are focused on multi‑site franchise rollouts. They actively invest in reliability and QA (e.g., automated test calls and production monitoring with third‑party tooling) to improve performance across accents and noisy environments [YC company page: https://www.ycombinator.com/companies/lilac-labs; Hamming case study: https://hamming.ai/blog/lilac-labs-customer-spotlight].

Who are their target customer(s)

• Multi‑site franchise operators (area managers/franchisors): Need to reduce order errors and labor variability across many locations, with deployments that plug into existing POS and kitchen systems and can roll out repeatably [https://www.ycombinator.com/launches/LRU-lilac-labs-drive-thru-order-taking-with-voice-ai; https://www.drive-thru.ai/].
• Independent single‑store owners: Struggle to cover peak drive‑thru demand and avoid mistakes without adding staff; need a hands‑off way to cut labor load while maintaining accuracy [https://www.drive-thru.ai/].
• Store/shift managers running drive‑thru lanes: Noisy lanes, accents, and complex orders slow throughput and create incorrect tickets; they need reliable human fallback when the AI can’t resolve an order [https://www.drive-thru.ai/; https://hamming.ai/blog/lilac-labs-customer-spotlight].
• Corporate IT/operations at chains: Have to integrate and maintain reliable connections to varied POS/KDS and hardware across sites; need a secure, enterprise‑fit deployment [https://www.drive-thru.ai/].
• Phone‑order teams and outsourced call centers: High call volumes and inconsistent order taking drive staffing needs and accuracy issues; they need automation with smooth handoff to humans when needed [https://www.drive-thru.ai/].

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

• First 10: Convert YC‑era pilots and local independents into paid reference sites using free/discounted 2–3 week pilots, founder‑led installs, and measured accuracy/upsell results to close [YC launch: https://www.ycombinator.com/launches/LRU-lilac-labs-drive-thru-order-taking-with-voice-ai; https://www.drive-thru.ai/].
• First 50: Co‑sell with POS/hardware partners (e.g., Toast, Square, PAR, HME) and use a short deployment playbook plus dedicated onboarding to keep installs within 2–3 weeks; publish several case studies to aid outbound [https://www.drive-thru.ai/].
• First 100: Scale via channel partners and franchise operators with pilot‑to‑contract discounts and SLA‑backed pricing; add a small ops team for multi‑site rollouts and use analytics from early customers to build a repeatable ROI pitch [https://www.drive-thru.ai/; https://www.ycombinator.com/companies/lilac-labs].

What is the rough total addressable market

Top-down context:

Lilac/YC cite roughly 200,000 U.S. drive‑thrus handling ~6 billion visits/year and estimate about $100,000 of annual operator value per location from labor savings, upsells, and fewer errors [https://www.ycombinator.com/companies/lilac-labs].

Bottom-up calculation:

Economic TAM ≈ 200,000 locations × $100,000/location = ~$20B/year of operator value (not Lilac’s revenue). A visit‑level check (~30k visits/location/year) implies ~$3.33 value per visit under those assumptions [https://www.ycombinator.com/companies/lilac-labs].

Assumptions:

• Figures (200k locations, 6B visits, $100k/location) are company‑reported via YC.
• U.S. drive‑thru locations only; excludes other channels and geographies.
• Assumes full adoption and that each site realizes the cited value; vendor revenue is a fraction of this.

Who are some of their notable competitors

• Presto Automation: Restaurant automation vendor offering a drive‑thru voice product (Presto Voice) aimed at QSR order taking.
• SoundHound AI: Voice AI platform with restaurant solutions for drive‑thru and phone ordering, positioned for large‑scale deployments.
• Hi Auto: Specializes in drive‑thru voice automation focused on noisy environments and order accuracy.
• ConverseNow: Automates restaurant voice ordering, historically strong in phone channels and expanding into drive‑thru.
• OpenCity (Tori): Offers a drive‑thru voice assistant (Tori) for QSRs to automate order taking and upsells.