ServiceNow Commercial and Professional Services Tokyo, Japan

Syslabo

Application Refactoring · Sales Automation

Syslabo needed to replace a 30-year-old legacy sales support stack, remove silos between separate sales tools, and give business users a practical way to build and improve internal applications without long specialist development cycles.

2x Faster onboarding for App Engine with AI Agents
50% Quicker app prototype creation with GenAI
~60% Of employees are citizen developers with AI Agents

Reported impact

Business outcome

Syslabo cut application prototyping time in half, doubled App Engine onboarding speed with AI assistance, and expanded citizen-development participation while creating a repeatable blueprint for both internal and customer-oriented application delivery.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Syslabo standardized internal application delivery on ServiceNow App Engine and used Now Assist for Creator with AI Agents to modernize both development and workflow design. The company rebuilt sales support and sales management processes on App Engine after testing multiple approaches, then used workshops to let sales specialists and certified engineers prototype applications together. Now Assist for Creator generates code, flows, playbooks, and app prototypes so citizen developers can move from ideas to working designs more quickly, while App Engine provides the shared workflow and data foundation for requests such as business cards, device loans, and paid leave. The target architecture consolidates sales, HR, and admin information into one database so teams can reuse data, reduce duplicate entry, and extend successful patterns into future customer-facing solutions.

Data flow

Sales and internal operations requirements are modeled in App Engine, where Now Assist for Creator and AI Agents generate prototypes, code, and workflow logic. The resulting applications write shared records into a single database spanning sales, HR, and admin processes, and employees use those workflows for requests such as business cards, device loans, and paid leave.

Solution architecture

3 components · 2 layers
  1. Compute
    • Now Assist for Creator Generates code, flows, playbooks, and application prototypes to speed development for both expert and citizen developers.
    • AI Agents Give users AI-assisted starting points for workflow design and accelerate learning on the platform.
  2. Orchestration
    • App Engine Provides the application platform used to rebuild sales support systems and internal workflows.

Architecture clues

  • App Engine replaced a 30-year-old legacy system used for sales support and sales management.
  • Syslabo uses training and production workshops so employees can prototype and validate workflows in real settings.
  • The target state is a single database shared across sales, HR, and admin teams.

Evidence from the source

For a citizen developer like me, App Engine will cut development times by half.
Shinya also estimates that using AI Agents doubles the pace of learning for both App Engine and integrating application functions.
The company expects more than half of its employees will be able to develop their own business applications using App Engine in the future, and around 60% of 100 employees have licenses.
We needed to simplify internal workflows, connect data, and manage that information from a single database to avoid duplicating effort between sales, HR, and admin teams.
When I used the App Generation function I could mock up an application really easily.
Transparency Evidence & methodology 96/100 reference value · 79% 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 value96/100
  • 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 specificity16/20
    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 confidence79%
  • 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
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. ServiceNow

    Syslabo customer story

    First-party
    https://www.servicenow.com/customers/syslabo.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 30, 2026 · ServiceNow · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.