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IBM Public Sector Finland

City of Helsinki

Customer Service Automation

The City of Helsinki needed to improve multilingual customer-care chatbots so foreign-language questions could be understood more accurately without lowering the quality or safety of responses provided to residents.

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38,000 employees
650,000 citizens
For English to Finnish, watsonx.ai scored 0.90 and current solution scored 0.91 (out of 1.0)
For Swedish to Finnish, watsonx.ai scored 0.87 and current solution scored 0.86 (out of 1.0)

Reported impact

Business outcome

Helsinki validated a safer path to better multilingual support, improving the prospect of faster and more accurate service for residents without sacrificing control over public-sector answers.

  • Customer Experience
  • Productivity Improvement

Solution

The City of Helsinki worked with IBM Client Engineering and IBM Consulting on a proof of value that used IBM watsonx.ai to strengthen translation inside the city's customer-care chatbot stack. Instead of replacing the chatbot outright, the team focused on a narrow but high-impact language step: translating incoming foreign-language questions into Finnish, the chatbot's base language, so intent recognition could improve while answers remained pre-written and reviewed. IBM ran a series of workshops with the city to design the approach, evaluate large language models and assess how the translation service could fit Helsinki's existing infrastructure. The result showed watsonx.ai could match the current translation solution on quality while opening a path to broader AI-assisted customer care. This makes watsonx.ai the compute layer for safer multilingual understanding, rather than the final answer generator, which is important in a public-service context.

Data flow

Questions submitted in foreign languages are routed through watsonx.ai for translation into Finnish, then the city's existing chatbot maps the translated question to intents and returns pre-written responses in a controlled customer-care workflow.

Solution architecture

1 components · 1 layer
  1. Compute
    • IBM watsonx.ai Translates foreign-language resident questions into Finnish to improve chatbot intent recognition.

Architecture clues

  • Responses remain pre-written and checked to avoid mistakes.
  • The deployment scope focused first on translation rather than answer generation.
  • The solution is designed to integrate into the city's current chatbot infrastructure.

Evidence from the source

"For English to Finnish, watsonx.ai scored 0.90 and current solution scored 0.91"
"For Swedish to Finnish, watsonx.ai scored 0.87 and current solution scored 0.86"
"The answers given are pre-written and checked to avoid mistakes."
"could be the new translation solution for City of Helsinki"
"translating questions in foreign language into Finnish, the chatbot's base language to increase intent recognition"
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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 specificity8/10
    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. IBM

    City of Helsinki customer story

    First-party
    https://www.ibm.com/case-studies/city-of-helsinki-watsonx
    Source type
    Vendor-published customer story
    Published
    Apr 26, 2024
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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