How a Facilities Firm Cut RFP Turnaround 50-70%

A facilities firm's team rebuilt every RFP response from a blank page. AI now drafts from the proposal archive, cutting turnaround 50-70%, and an agent resolves up to 60% of calls.
50-70%
Sped up RFP and proposal drafts
4–6 months
Implementation Time
$100K – $250K
Project Cost

the challenge

The firm services commercial buildings, and its operational data was scattered: building sensor feeds, payroll and timekeeping that differed by site, supply and inventory records, an archive of past proposals, and reporting that stitched it together by hand. Every RFP response started from a blank page even though the firm had answered the same questions many times before, and every inbound call needed a person.

what they built

ModernEdge started with a full operational audit across sensor feeds, payroll and timekeeping, supply and inventory, the proposal archive and reporting, and mapped it into a single strategic roadmap. Four priority systems were scoped and delivered as independent sprints so each could prove itself before the next began. Proposal and RFP responses are now auto-assembled from the firm's existing proposal archive: the system finds the closest prior answers, adapts them to the new building and scope, and produces a draft for the team to finalize. Payroll and timekeeping were normalized into one processing layer across every building. An operational analytics layer turns the live data streams into predictive insight. An inbound-call agent handles routine calls end to end and escalates the rest.
The engagement opened with a full operational audit across building sensor feeds, payroll and timekeeping, supply and inventory, the proposal archive and reporting, mapped into a single roadmap. From that, four priority systems were scoped and delivered as independent sprints so each could go live and prove value before the next started. Proposal automation came first: the system finds the closest prior answers in the archive, adapts them to the new building and scope, and produces a draft for the team to finalize. Unified operational analytics followed, turning live data streams into predictive insight. Payroll and timekeeping were then normalized into one processing layer across every building, and the inbound-call agent came last, resolving routine calls end to end and escalating the rest. The hardest part was getting clean data out of legacy sensor feeds and older payroll records; sprinting one system at a time let the team validate data quality before it became a dependency.

best fit for

Multi-site service businesses (janitorial, facilities, security, landscaping, HVAC service) that respond to formal RFPs, run payroll across many locations, and field a steady volume of routine inbound calls. The audit-first, sprint-by-sprint structure suits operators who want to fund each system on its own results.
Ai ROLE
Three distinct roles. Retrieval and drafting for proposals (find the right prior answers, rewrite for the new scope). Normalization and prediction over operational data (payroll, timekeeping, sensor feeds). A conversational agent for inbound calls that resolves routine requests and hands off the rest.

impact

50-70%

Faster proposal and RFP turnaround, auto-assembled from the firm's own proposal archive.

40-60%

Of inbound calls resolved without a human.

4 systems deployed as independent sprints from one audit

implementation complexity

Each of the four systems is a contained build, but they touch different data (documents, payroll, sensors, telephony).
Kevin Malaekeh, ModernEdge AI

Kevin Malaekeh

Co-Founder at ModernEdge AI
ModernEdge AI
Co-founder of ModernEdge AI, a Claude partner that builds custom AI agents, voice systems and document workflows for mid-market and PE-backed companies.
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industry
Construction & Engineering
business organization
HR & People
AI TYpe
AI-Accelerated Custom Software
value type
Time Savings

frequently asked questions

How did a facilities services firm cut RFP turnaround 50-70% with AI?

It turned its archive of past proposals into a drafting source. AI finds the closest prior answers, adapts them to the new building and scope, and hands the team a draft to finalize, which cut RFP turnaround by 50-70%.

What AI tools were used for the facilities operations system?

Claude via the Anthropic API, used for retrieval over the proposal archive, a payroll and timekeeping normalization layer, operational analytics and an inbound-call agent.

What results did the facilities firm achieve?

RFP and proposal turnaround 50-70% faster, 40-60% of inbound calls resolved without a human, and four systems deployed as independent sprints from one operational audit.

How long did the facilities AI systems take to deploy?

First meaningful results came in four to six months, with each of the four systems shipped as its own sprint.

Who is this AI operations approach best for?

Multi-site service businesses such as janitorial, facilities, security, landscaping and HVAC service that answer formal RFPs, run payroll across many locations and field steady routine call volume.

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