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83% of RFPs Processed via Agentic AI Without Manual Review 

A global enterprise needed to cut time and effort spent on RFP intake and quote creation. CES built an agent‑based workflow that extracts inputs, reuses prior content, and accelerates compliance.

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The Challenge

High manual effort to parse RFPs and assemble responses

High manual effort to parse RFPs and assemble responses

Quote-generation TAT slowed by manual extraction and review

Quote-generation TAT slowed by manual extraction and review

Compliance checks for building-code and government rules

Compliance checks for building-code and government rules

the client

Elevator & Escalator

Global

Technology Stack

  • LLMs
  • Machine Learning Algorithms
  • Intelligent Document Understanding
  • Smart Label Generation
  • Classification Workflow

Solution Area

  • RFP Processing Automation
  • Quote Generation Workflow
  • Compliance Validation

the impact

Only 17% of RFPs required manual intervention

Immediate reduction in quote-generation TAT

Fewer hours spent on proposal responses

Faster compliance checks for regulated contracts

how we did it

Workflow-led RFP intake.

Faster quote cycles.

The Need & The Challenges
The CES Solution
Results & Business Impact

The Need

Reduce hours spent on RFP intake and response preparation, shorten quote-generation TAT, and introduce repeatable compliance checks—without losing control over review and approvals.

Challenges

  • RFP parsing and classification at scale: Incoming RFPs required consistent parsing and structured classification to route work through the right proposal steps.
  • Quote data extraction with controlled review: Quote inputs had to be extracted reliably from documents, with a review safety net to prevent rework.
  • Compliance checks for regulated contracts: Generated proposals needed to be checked against building-code guidelines and other requirements tied to government-specific contracts and state regulations.
  • RFP Parser (first-level agent): Implemented smart label generation using LLMs and machine-learning algorithms to parse and classify incoming RFPs for subsequent workflow steps.
  • Quote Generator with document understanding + safety-net review: Automated extraction of quote inputs via intelligent document understanding and surfaced extracted data for manual review where needed, reducing time spent on manual checks.
  • Proposal + engineering diagram reuse workflow: Enabled an analysis flow that compares current requirements with past proposals and engineering diagrams to support faster proposal building.
  • Compliance Checker for government-specific contracts: Added compliance checks aligned to building-code guidelines and other rules tailored to state regulations for regulated contracting scenarios.
  • Only 17% of RFPs required manual intervention
  • Reduced hours spent responding to proposal requests
  • Immediate reduction in quote-generation TAT
  • Faster compliance validation for regulated proposal submissions
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RFP effort reduced. Quote prep accelerated. A structured workflow moved RFP parsing, proposal building, and compliance checks into a repeatable path—so teams could respond faster with tighter control.