Founded: 2026
Incorporated: Not publicly disclosed
Status: Active; YC Summer 2026
Funding history: Y Combinator and SV Angel publicly identified as backers; no round amount publicly disclosed
Funding amount: Not publicly disclosed
Acquisition: None publicly disclosed
Founder LinkedIn: Luca Hadife — https://www.linkedin.com/in/luca-hadife/
Founder LinkedIn: Mohamed Hosny — https://www.linkedin.com/in/mohamed-hosny-hussein/
Founder background: Luca Hadife — UC Berkeley Data/ML; previously built multimodal AI systems at Microsoft and worked at Amata. Public LinkedIn activity references Beirut and Lebanese founders; no independently verified family-origin details found.
Founder background: Mohamed Hosny — taught Computer Architecture and Signal Processing at UC Berkeley EECS; previously worked as a software engineer on data-center switches at Arista Networks. Personal city, schooling, family-origin details, health issues, and legal issues were not reliably disclosed in reviewed sources.
Public issues: No reliable public health or legal issues identified for either founder.

UNIT METRICS
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Metric | Number
Team Size | 2 people
States Live | 3 states
YC Batch | 2026
Demo Length | 30 minutes
Employees | 2 people
Revenue | N/D
ARR | N/D
Retention | N/D
Gross Margin | N/D
Claims Recovery | N/D

Problem Statement
Poor field documentation -> incomplete operational data -> back-office delays, missed warranty recovery, inaccurate parts ordering, slower billing, and weaker customer follow-up.
Impact if unsolved: administrative labor remains high; revenue leakage and service-cycle delays persist.
Alternatives: manual technician reports; existing FSM/CRM workflows; phone, SMS, email, spreadsheets, and outsourced back-office teams.

Due Diligence Segment
----------------------
Market & Sector Thesis: Field-service businesses remain operationally fragmented across technicians, FSM systems, CRMs, supplier portals, and accounting tools. AI agents become more useful as reliable field evidence and company-specific operating context accumulate.
Market Sizing: Targets B2B field-service operators, initially including HVAC, home services, facility management, inspections, and construction-risk workflows; company-specific TAM not publicly disclosed.
Operating Traction: Kebra states it is working with field-operations companies across Florida, Pennsylvania, and California; customer count and financial metrics are not disclosed.
Direct Competition: ServiceTitan, Housecall Pro, Jobber, FieldEdge, and Workiz; these are larger field-service software platforms rather than directly comparable early-stage AI-agent companies.
MOAT Durability: Potential moat is proprietary company-specific operating memory built from job evidence, asset histories, workflows, and outcomes, combined with integrations into operational systems. The moat could be weakened by incumbent FSM vendors adding comparable AI agents or by general-purpose AI platforms developing reliable field-service integrations.
Plausible probable AI models used: Not publicly disclosed.
Verified technology stack: AI models, hosting, storage, search, transcription, communications, and integrations are referenced generally in Kebra's privacy policy, but specific vendors, model names, frameworks, and databases are not disclosed.

Sources:
Y Combinator company profile — https://www.ycombinator.com/companies/kebra — last updated: not disclosed; accessed 2026-08-28
Kebra official website — https://www.kebra.com/ — last updated: not disclosed; accessed 2026-08-28
Kebra Privacy Policy — https://www.kebra.com/privacy — last updated 2026-08-02; accessed 2026-08-28
Kebra official LinkedIn company page — https://www.linkedin.com/company/kebra-com/ — last updated: not disclosed; accessed 2026-08-28
Dealroom company profile — https://app.dealroom.co/companies/kebra_1 — last updated: not disclosed; accessed 2026-08-28