$31K

Pays Restoration Management $30,623.50 to remove asbestos flooring at Sequoia Elementary

Restoration Management Company will identify, remove and safely dispose of asbestos-containing materials in the kitchen and cafeteria areas of Sequoia Elementary School, ahead of planned equipment installation or flooring replacement. Work is scheduled for June 1 to June 30, 2026.

Plain-English summary written by AI from the official text. AI summary · amounts checked against official text

Action
New agreement
Category
School buildings & construction
Term
Jun 1, 2026 – Jun 30, 2026
This vote
$30,623.50
Schools
Sequoia Elementary School
Funding
KIT Funds
Resource / site
7032 991
Vendor no.
008214
Presented by
Deputy Chief, Facilities Planning and Management
File
26-0874 · agenda O.-10
Outcome
Adopted on the General Consent Report, May 13, 2026

Official text

Approval by the Board of Education of Construction Agreement Between Owner and Contractor by and between the District and Restoration Management Company, Livermore, CA, for the latter to provide construction services including safely identifying, abatement, and properly dispose of asbestos-containing materials (ACMs) within school kitchen and cafeteria areas in support of planned modernization efforts, including equipment installation and/or flooring replacement, in accordance with all applicable regulatory requirements, for the Sequioa Elementary Asbestos Floor Project, as described in the proposal dated March 19, 2026, attached to this Agreement as Exhibit A, in the amount of $30,623.50, with the work commencing June 1, 2026, and being completed by June 30, 2026, via Division of Facilities Planning and Management.

History

  1. Adopted on the General Consent Report

This is an independent project created to provide transparency to the OUSD Community, and is not affiliated with Oakland Unified District. All data used to create the Consent Report Tracker is made public by Legistar, OUSD's official platform for posting legislative records. Plain-English summaries are written by AI with narrow parameters based upon the official text of the reports. Read about how all this works.

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