Case Study: Scaling Multilingual Customer Support with a Nearshore + AI Model

Scaling multilingual customer support without exploding costs or losing CSAT: a 2026 playbook

Hook: If your mid-market SaaS is facing rising support volumes, inconsistent multilingual responses, and ballooning headcount costs, this is the playbook you need. In 2026, the most effective path to scale is not just hiring more people — it's combining nearshore support with intelligent AI augmentation. This case-study template, inspired by MySavant.ai’s nearshore + AI approach, shows how a mid-market SaaS can cut cost per ticket while improving CSAT and accelerating multilingual support.

Executive summary — what this case study template delivers

This narrative template walks through a repeatable program used by a fictional mid-market SaaS (we’ll call them Brightly). Brightly combined a bilingual nearshore team with AI copilots to:

• Reduce cost per ticket by an illustrative 35–50% vs. onshore-only staffing
• Improve CSAT from the high 70s to the high 80s/low 90s in 12 months
• Increase handled ticket volume 2.5x without proportionate headcount growth
• Maintain brand tone and legal compliance across markets

Why nearshore + AI matters in 2026

Traditional nearshoring optimized for labor arbitrage. By late 2025 and into 2026, leaders learned that labor alone won’t scale quality or visibility. AI augmentation — enterprise LLMs, retrieval-augmented generation (RAG), and agent-assist copilots — multiply agent productivity and standardize responses. The result: intelligence, not just labor, becomes the scaling lever.

“We’ve seen nearshoring work — and we’ve seen where it breaks.” — Hunter Bell, MySavant.ai

That observation underpins this template: pair bilingual human judgment with AI that enforces policy, surfaces knowledge, and drafts responses — then let humans edit and own final quality.

Background: Brightly’s problems and goals

Context

Brightly is a B2B SaaS with customers in North America, Latin America, and Western Europe. Its product is complex (integrations + API), and customers submit support requests in English, Spanish, and Portuguese. The company had three pain points:

1. Rising cost per ticket. Onshore agents were expensive; growth forced headcount increases that outpaced revenue per customer.
2. Inconsistent multilingual quality. Generic machine translations and ad-hoc bilingual hires led to tone and accuracy issues.
3. Slow scaling and poor observability. Managers lacked metrics to understand where work concentrated, causing hidden costs and rollback-prone processes.

Goals

• Cut average cost per ticket by at least 30% within 12 months
• Increase multilingual CSAT by 8–12 points
• Support 2x–3x ticket volume without linear headcount growth
• Preserve data privacy and meet SOC2/GDPR requirements

Solution architecture: nearshore bilingual teams + AI copilots

Brightly implemented a layered model with three components:

1. Nearshore bilingual agents. Hired agents in Latin America fluent in English and Spanish (and Portuguese for Brazil). These agents handled initial triage, troubleshooting, and relationship-sensitive tasks.
2. AI agent assist platform. An enterprise LLM with RAG connected to the knowledge base and CRM produced suggested replies, diagnostic steps, and policy flags. Agents reviewed and edited AI drafts before sending.
3. Orchestration and observability. A supervisor dashboard tracked suggested vs. sent responses, AI confidence, containment rates, and per-ticket time breakdowns.

Why this mix?

Nearshore agents provide cultural alignment, faster ramp time, and bilingual fluency. AI increases throughput and consistency, handling repetitive phrasing, translating technical content reliably when combined with a verified terminology dictionary, and suggesting resolutions based on historical tickets. Crucially, humans validate AI outputs — avoiding the “set-and-forget” automation that triggers escalations and brand risk.

Implementation timeline (12 months)

We recommend a phased rollout with clear milestones.

Phase 0 — preparation (1 month)

• Baseline metrics: cost per ticket, CSAT, FRT (first response time), TTR (time to resolution), escalations
• Audit KB: canonicalize articles, extract templates, create terminology list
• Compliance check: data residency, GDPR, and SOC2 requirements

Phase 1 — pilot (months 2–3)

• Onboard 8–12 nearshore bilingual agents as a separate queue
• Deploy AI-assist with RAG limited to KB and internal playbooks
• Measure: AI suggestion acceptance rate, edit distances, and customer sentiment

Phase 2 — scale (months 4–9)

• Expand agent pool, integrate AI into primary support platform (e.g., Zendesk, Freshdesk)
• Introduce dynamic routing: simple tickets routed to AI-assisted nearshore agents, complex tickets to product SMEs
• Launch continuous training loop: flagged edits update KB and fine-tune prompts

Phase 3 — optimize (months 10–12)

• Measure net promoter signals and friction points — iterate on playbooks
• Run A/B tests: AI-drafted vs. human-drafted responses on CSAT
• Establish quarterly governance and compliance reviews

Illustrative outcomes (what success looks like)

Every organization’s results will vary. Below are representative, conservative outcomes Brightly achieved when following this template.

• Cost per ticket: reduced 40% in year one (from $9.50 to ~$5.70 per ticket)
• CSAT: increased from 78% to 88% within 9 months, with multilingual parity across English, Spanish, and Portuguese
• Throughput: capacity to handle 2.8x ticket volume without a linear increase in headcount
• FRT/TTR: median first reply time cut by 35%, median time to resolution improved 30%
• Quality: AI suggestion acceptance rate stabilized at ~65% after three months of fine-tuning; edit distance metrics used to flag problem areas

Note: These figures are illustrative results from the template application and should be validated against your own baseline.

Playbook: step-by-step actions you can run this quarter

1. Establish baseline KPIs. Capture cost per ticket, CSAT by language, FRT, TTR, and escalations for the prior six months.
2. Clean and structure knowledge. Convert KB articles into canonical Q&A pairs, add tags for intent, issue type, and resolution steps.
3. Create a terminology and localization guide. Include product terms, brand voice, and country-specific phrasing to pass to both AI and agents.
4. Choose a nearshore partner or set up your office. Prioritize bilingual recruiting, agent quality over pure cost, and onshore overlap hours for coaching.
5. Deploy AI agent assist with RAG. Connect to KB, CRM, and ticketing history. Start in “suggest-only” mode.
6. Design guardrails and approval workflows. Automatic escalation triggers: CSAT risk language, regulatory mentions, PII exposure, refunds/issues requiring manager approval.
7. Set up continuous monitoring. Track AI confidence, suggestion acceptance, edit distance, CSAT per response, and churn signals among agents.
8. Iterate weekly. Feed back corrected responses into KB and adjust prompts. Run monthly A/B tests.

Tech stack checklist (recommended)

• Ticketing: Zendesk, Freshdesk, or similar with API access
• AI layer: enterprise LLM with RAG and vector search, with fine-tuning and logging
• Knowledge base: headless KB or structured CMS (semantic tagging essential)
• Observability: dashboards for per-ticket cost analysis, AI metrics, and CSAT by language
• Security/compliance: SOC2-ready cloud infra, encryption at rest and in transit, PII redaction
• Workforce tools: scheduling, eLearning, and QA review platforms supporting bilingual content

Governance, privacy, and trust (non-negotiables in 2026)

In 2026, regulatory scrutiny and enterprise risk demands stronger controls than ever. A few must-dos:

• Data residency & EU AI Act compliance. If you serve EU customers, ensure any LLM processing meets EU AI Act high-risk controls and local data rules — see EU data residency rules.
• PII handling pipeline. Use automatic redaction and human review gates for sensitive attributes; privacy teams should align with deliverability and privacy guidance.
• Audit logs and explainability. Maintain logs of AI suggestions, agent edits, and final message so you can trace decisions — invest in auditability and decision-plane tooling.
• Vendor contracts. Include security attestations (SOC2 Type II), access controls, and IP clauses for training data usage.

Common pitfalls and how to avoid them

• Pitfall: Treating AI as a replacement for agents. Fix: Use AI to augment, not replace; keep humans in the loop for empathy and escalation.
• Pitfall: Deploying AI without a clean KB. Fix: Canonicalize knowledge first — “garbage in, garbage out” applies to enterprise LLMs. Consider a tool-sprawl audit to consolidate KB tooling.
• Pitfall: Hiring purely on cost. Fix: Measure language skills, customer empathy, and troubleshooting ability during hiring.
• Pitfall: Ignoring observability. Fix: Track AI suggestion leverage, edit rates, and CSAT by language to detect regressions fast.

Advanced strategies and 2026 trends to watch

Apply these once you’ve stabilized operations:

• Adaptive routing with intent classifiers. Route tickets to AI-first or human-first queues based on intent and customer value — pair this with predictive tooling that narrows the response gap.
• Fine-tune models on your conversational data. Deliver higher-quality suggestions and reduce hallucinations by using labeled support conversations (with consent) — see internal assistant frameworks.
• Localized SEO support. Integrate support KB improvements into SEO strategy — translated KB pages can drive organic traffic when localized properly; review microlisting strategies for details.
• Real-time translation fallback. For rare-language queries, combine on-the-fly translation with bilingual review to expand coverage without immediate hires.
• AI observability and safety tooling. Adopt model-monitoring tools to detect drift, response toxicity, and privacy anomalies — a 2026 standard for regulated industries; see resources on edge auditability.

Measurement template: calculate cost per ticket and ROI

Track direct and indirect costs to compute accurate cost per ticket:

1. Agent salaries + benefits (pro-rated) + nearshore vendor fees
2. AI platform costs (tokens, indexing, fine-tuning amortized)
3. Tooling and overhead (ticketing, training, compliance)

Cost per ticket = (Total monthly support cost) / (Tickets handled monthly). To estimate ROI, compare projected cost per ticket pre- and post-implementation and include soft benefits (CSAT lift, churn reduction, faster onboarding).

Sample governance checklist (quarterly)

• Review AI suggestion acceptance & edit trends
• Audit 50 random multilingual tickets for tone and accuracy
• Validate compliance controls and vendor attestations
• Update KB with new product changes and local variations

Real-world example: narrative summary

Brightly’s support team started with 12 nearshore bilingual agents and a ‘suggest-only’ AI assist. Within 90 days, agents increased throughput and their acceptance rate of AI drafts rose as the knowledge base and prompts were tuned. Monthly QA revealed fewer localization errors and higher resolution accuracy. Over the next six months, Brightly scaled the nearshore pool and expanded AI responsibilities to propose full resolution steps. By month 12, cost per ticket fell ~40% and CSAT rose by 10 points — enabling Brightly to invest savings into product improvements and localized onboarding content.

Actionable takeaways — run this checklist in 30 days

• Measure baseline KPIs (cost per ticket, CSAT by language)
• Canonicalize your top 200 KB articles and extract a terminology list
• Run a 6-week AI-assisted pilot with 8–12 nearshore bilingual agents
• Enable edit-tracking and monitor AI suggestion acceptance rate weekly
• Implement PII redaction and data residency controls before wide rollout

Final thoughts — why this approach wins in 2026

Nearshore support gives you bilingual, culturally-aligned human judgment at scale; AI provides consistency, speed, and the ability to scale knowledge. When combined with rigorous governance and observability, the nearshore + AI model reduces cost per ticket while improving CSAT — a rare win for both finance and customer experience teams. This hybrid model is the logical evolution of the nearshore playbook described by MySavant.ai: intelligence first, labor second.

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Call to action

Ready to pilot a nearshore + AI support model tailored to your product and markets? Contact our team for a free 30-minute assessment: we’ll map your current costs, identify the top 50 KB articles to optimize, and build a 90-day pilot plan that targets measurable reductions in cost per ticket and improvements in CSAT.