Media case study | CloudFiles for Salesforce

Overview

A leading global media and entertainment company partnered with multiple advertising agencies for campaign collaborations. To support their complex RFP workflows, the sales team needed to extract key campaign details – such as agency names, advertiser info, and campaign dates – from unstructured and highly varied documents. CloudFiles' Document AI automated this process with intelligent extraction, contextual understanding, and fuzzy matching against Salesforce data.

Business challenges

Varied document types – teams received a mix of RFPs, agency proposals, budget plans, and even email threads, each with different structures and purposes.
Inconsistent formatting – even within the same document type, formats varied widely based on the advertiser or agency involved.
Incomplete or hidden information – not all documents included every data point, requiring close reading and inference to locate relevant fields.
CRM mismatches – names found in documents didn't always match Salesforce entries, requiring human judgment to identify and associate the right records.

Solution

AI-powered multi-format extraction – captured key fields like campaign dates, advertiser names, and agencies across varied document types.
Chain-of-thought prompting – used layered logic and prompt refinement to interpret structure, resolve ambiguity, and extract contextually accurate data.
Fuzzy CRM matching – matched extracted data to Salesforce records with high accuracy, even when names were abbreviated, partial, or formatted differently.

Business challenges, in depth

With a steady influx of sales documents from agencies and advertisers, extracting campaign details consistently became a challenge, making manual review time-consuming, error-prone, and difficult to scale.

Unstructured, multi-type document inputs

"We'd receive RFPs, proposals, spreadsheets – even email chains. Each had different layouts, making it hard to process consistently." No standardized document structure existed across advertisers or agencies.

Inconsistent formatting within document types

"Even RFPs looked completely different depending on who created them – every agency and advertiser had their own way of doing things."

Incomplete or implicit data

"Sometimes, the info wasn't even labeled – it had to be inferred from context or cross-referenced with other sections."

Fuzzy matches against CRM records

"Even after extracting data, advertiser names rarely matched CRM exactly. We had to rely on experience to link them correctly." AI needed to interpret partial, abbreviated, or alternate spellings and parent-child record relationships.

How CloudFiles solved it

CloudFiles deployed a multi-layered AI solution, combining intelligent field extraction, refined prompting techniques, and Salesforce-integrated fuzzy matching for faster, more accurate data capture regardless of document type or structure.

AI-powered basic field extraction

CloudFiles used intelligent prompts to identify and extract foundational fields like start dates, end dates, campaign names, and agency info, even from scattered or unstructured formats, adapting to different document types automatically.

Prompt refinement with logical reasoning

CloudFiles leveraged chain-of-thought reasoning and prompt chaining to navigate complex, unstructured documents, identifying the correct advertiser or agency name even when multiple company names were present.

Fuzzy matching against CRM data

CloudFiles matched extracted fields to existing Salesforce records with high confidence, using AI-driven fuzzy logic to compare multiple data points and applying parent-child record resolution to return the correct top-level CRM entry.

Rollout & secondary impact

CloudFiles Document AI was implemented in a phased, non-disruptive rollout – from idea to full integration in just 10 weeks.

Phase 1 (2 weeks)

Deployed AI for basic field extraction and Salesforce mapping, running alongside the current workflow so users could compare AI outputs with manual inputs without interrupting operations.

Phase 2 (4 weeks)

Introduced advanced prompt refinement to handle varied document formats, using real document samples to fine-tune accuracy and contextual understanding.

Phase 3 (4 weeks)

Rolled out fuzzy matching with CRM data, training AI models to recognize naming variations and return the most relevant parent records.

Dedicated support throughout

CloudFiles delivered dedicated onboarding, contextual training, and responsive support, ensuring high adoption and user confidence with no disruption to operations.

Impact & results

CloudFiles significantly improved both the speed and quality of sales data processing, freeing up teams to focus on higher-value tasks while ensuring cleaner, more reliable Salesforce records.

25% more records extracted

CloudFiles surfaced significantly more usable data per document by going beyond surface-level fields and interpreting context.

50% faster data extraction

Manual extraction previously took 30-60 seconds per document; after CloudFiles, AI reduced the time to 10-15 seconds per document. At 600 monthly documents, that's roughly 5 hours saved per month, a 50% faster cycle time on campaign data ingestion.

Better data quality and CRM accuracy

Improved match rates across advertisers and agencies, reduced manual verification, and strengthened sales analytics with structured, matched data entering the CRM.

How a media & entertainment giant improved sales data accuracy by 25% using CloudFiles' AI-powered document extraction