Investor note
Bazas — investor note
Pre-Seed · Last updated July 21, 2026
For informational purposes only. Nothing here constitutes an offer to sell or a solicitation to purchase securities.
Executive summary#
Service businesses pay for leads through Yelp, Thumbtack, Google, and other channels, then lose a share of them because no one answers fast enough. Bazas is an AI front desk that answers inbound calls and messages, qualifies the job, books within owner-defined rules or escalates to a person, and keeps every interaction in one CRM record. The wedge is inbound; the expansion is the operating memory and workflow for the business.
Problem#
A customer calls or messages while the owner is busy. The response is delayed, the customer moves to the next provider, and the business loses both the lead cost and the job. The pain is concentrated in three places: missed calls, fragmented inboxes across channels, and inconsistent follow-up.
Product & status#
One AI front desk across calls, SMS, email, and lead-source channels. It collects job details, applies the owner's service-area, availability, and acceptance rules, and either books or hands off with full context — with human takeover available at any time.
The product is in private beta with founder-led onboarding, and access to beta features is limited to selected pilot customers. See the generally-available / private-beta / planned breakdown.
Why now#
- Customers increasingly expect an immediate response and move on quickly when they don't get one.
- Service businesses buy more of their leads through paid digital channels.
- Inbound demand is fragmented across calls, texts, email, and lead marketplaces.
- AI voice and language quality has improved enough for real customer conversations.
- Modern APIs let voice, messaging, scheduling, and CRM connect in one workflow.
- Labor and administrative costs for a dedicated front desk remain high.
Each external statistic is shown with its primary source rather than as a round number.
Traction & methodology#
We do not publish unverified figures. Each metric we share carries a defined period, an as-of date, and a methodology (for example, “unique inbound inquiries processed in pilot workspaces”). Current figures are available to qualified investors in the data room.
Business model#
A monthly subscription covers the AI front desk. On top of it, AI, voice, and messaging usage is resold above provider cost — so revenue grows with every AI interaction, not just with new seats.
Current: Monthly subscription per business, AI usage — resold above provider cost (margin on every AI interaction), Voice & SMS usage. Near-term: Additional locations, Additional team members, Premium workflows. Long-term: Financial operations module, Agency & franchise plans, White-label, Enterprise integrations. Investor-facing economics (blended ARPA, gross margin, retention) are in the data room.
Market#
Bazas initially targets U.S. home-service businesses with 1–20 field workers that already pay for leads and depend on fast customer response.
Sizing is bottom-up: Target businesses × expected annual revenue per account = serviceable revenue opportunity. Each input carries a source, date, and confidence and is shared with the model. We do not claim the entire SMB or global AI market.
Go-to-market#
Phase 1 — Founder-led (Live); Phase 2 — Repeatable vertical motion (Planned); Phase 3 — Channel expansion (Planned). A relationship counts as a partnership only when it is live, not when there is potential interest.
Competition & defensibility#
Bazas competes with FSM suites, CRMs, AI receptionists, answering services, DIY stacks, and in-house staff. Mature suites lead on breadth, invoicing, payments, and integrations. Our intended edge: AI-native inbound workflow, Unified context across calls and messages, Owner-defined operating rules, Faster configuration for lead handling. These advantages are being built and are not yet a proven long-term moat.
Roadmap & milestones#
Now: reliability, onboarding, and measurement in private beta. Next: repeatable onboarding, channel integrations, reporting, and paid growth. Later: multi-location, agencies, franchises, and financial operations. Milestones and their success metrics are shown on the homepage roadmap; dated targets are founder-provided.
Team & corporate structure#
Bazas is founder-led today (Nikita Lylov, Founder & CEO). The operating company is Bazas AI LLC; the definitive issuing entity, ownership, and IP assignment are set out in the offering documents and the data room.
Fundraising & use of funds#
Bazas is raising a Pre-Seed round to prove reliable inbound workflows, measurable customer outcomes, a repeatable acquisition channel, and unit economics within target. Round terms are shared with qualified investors after securities-counsel review. Use-of-funds categories are shown on the homepage; exact allocation is shared with investors.
Risks#
- Product reliability. AI can misunderstand a customer, make a mistake, or miss important information.
- Customer adoption. Some owners may not trust AI to interact with their customers.
- Competition. Mature FSM and AI-receptionist companies could add similar capabilities.
- Unit economics. Voice, SMS, AI, and support costs could compress gross margin.
- Platform dependency. Bazas depends on third-party APIs, telecom, messaging, and lead-source integrations.
- Execution risk. A Pre-Seed company has limited team and capital.
- Data & privacy. The system processes conversations and customer data.
- Fundraising risk. A future round may not close on the desired timeline or terms.
Mitigations reduce, but do not eliminate, these risks.
Founder letter#
Bazas exists because the missed-lead problem is one the founder lived: paid leads arriving while the team was busy, customer context scattered across phone, text, and marketplaces, and good jobs lost because nobody answered in time. Existing tools managed records but didn't answer the phone at 8 PM or keep the thread together across channels.
The hardest part has been reliability and trust — owners rightly want control over what an AI says to their customers. That shaped the product: owner-defined rules, human takeover, and safe defaults first. What we must prove next is that this converts inbound into booked work repeatably, at healthy unit economics, across more than one kind of service business.
Founder: Nikita Lylov, Founder & CEO — New York City · LinkedIn.
Last updated & changes#
- July 21, 2026 — Initial investor note published.
This website is provided for informational purposes only. Nothing on this website constitutes an offer to sell, a solicitation of an offer to buy, or a recommendation regarding any security. Any offering, if made, will be conducted only through definitive offering documents and in accordance with applicable law.