Use Case Intelligence RSS Search use cases Search
MongoDB Software / SaaS

Scalestack

Sales Automation

Scalestack needed to unify first-party, third-party, and zero-party go-to-market data so sales teams could stop manually researching prospects and act on AI-generated recommendations faster.

Read the original story Explore all use cases
Customers saw a 40% increase in rep productivity.
GTM teams saw a 53X ROI on average on the Scalestack platform.
Salesforce research cited in the story found reps spend just 28% of their week actually selling.

Reported impact

Business outcome

Scalestack streamlined data management, improved query performance, and helped customers increase rep productivity by 40% while achieving an average 53x ROI on the platform.

  • Productivity Improvement
  • Revenue Growth

Solution

Scalestack built its AI-powered go-to-market data orchestration platform on MongoDB Atlas running on Amazon Web Services so sales teams could work from one live operational data layer instead of juggling disconnected inputs. The platform ingests first-party CRM records, third-party sources such as LinkedIn, Crunchbase, and ZoomInfo, and zero-party lead information from marketing forms. MongoDB Atlas stores those varied datasets in a document model that can absorb new formats quickly, which lets Scalestack automate workflow assembly and prioritize the actions sellers should take next. Atlas also provides the real-time processing and query performance the company needs to keep customer data current as GTM plays evolve. By consolidating structured and unstructured GTM data on one developer-friendly platform, Scalestack can keep its lean team focused on product innovation while its customers get AI assistance that turns fragmented sales intelligence into execution-ready plays.

Data flow

CRM records, external GTM datasets, and lead-form submissions are ingested into MongoDB Atlas, organized into automated workflows, and surfaced back through Scalestack's AI platform so sellers can identify and act on the most relevant opportunities quickly.

Solution architecture

1 components · 1 layer
  1. Storage
    • MongoDB Atlas Stores and unifies GTM data from multiple sources so Scalestack can automate sales workflows and surface prioritized actions.

Architecture clues

  • MongoDB Atlas runs on AWS for this workload.
  • Scalestack combines first-party, third-party, and zero-party GTM data on one platform.
  • The company uses an automated workflow model to prioritize sales plays.
  • The story notes planned work around MongoDB Vector Search and Amazon Bedrock integration.

Evidence from the source

Customers saw a 40% increase in rep productivity, and GTM teams saw a 53X ROI on average on the Scalestack platform.
“Leveraging AI, Scalestack has been key to helping us to easily aggregate, manage, and automate disparate GTM data sets.”
“MongoDB's real-time processing capabilities have also been crucial in providing up-to-date information across our services.”
“Scalestack helps customers manage both structured and unstructured data, from first-party CRM data to third-party data from sources like LinkedIn.”
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Scalestack customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/scalestack
    Source type
    Vendor-published customer story
    Published
    Aug 11, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Aug 11, 2026 · MongoDB · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
MongoDB Sep 30, 2026 MongoDB Software / SaaS global MongoDB needed to stop sales reps from spending 4 to 6 hours per account or deal cycle manually stitching together customer context from Salesforce, email, meeting transcripts, and documents before they could plan… Holly currently runs on 30 agents. Sales Automation Atlas Agent Engine MongoDB May 18, 2026 LekoTech Software / SaaS Europe LekoTech needed to digitize millions of unique car parts to promote reuse but faced latency of up to 35 seconds with its existing search system. LekoTech needed to digitize millions of unique car parts to promote reuse but faced latency of up to 35 seconds… Enterprise Knowledge SearchSales Automation MongoDB Atlas · MongoDB Atlas Search · MongoDB Atlas Vector Search MongoDB Apr 29, 2026 Tenali AI Software / SaaS United States Tenali AI needed to help enterprise sellers answer deeply technical questions during live calls in under one second by searching Slack, PDFs, CRM data, and prior meeting content without the latency and synchronization… After switching embedding models, sales reps actively using Tenali's answers on live calls increased by over 40%. Enterprise Knowledge SearchSales Automation MongoDB Atlas · MongoDB Vector Search · Voyage AI