Gistly
Subscribe to newsletter
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Code-switching is the practice of alternating between two or more languages within a single conversation or sentence. In Indian contact centers, it is not an exception; it is the norm. When an agent says "Sir, aapka account verify ho gaya hai, main aapko next steps explain karta hoon," they have switched between Hindi and English four times in one sentence. Traditional QA systems cannot parse this. And if your QA tool cannot understand what the agent actually said, it cannot evaluate whether they said the right thing.
Code-switching occurs when a speaker blends two or more languages within a single interaction, sometimes within a single sentence. Linguists distinguish between inter-sentential switching (alternating languages between sentences) and intra-sentential switching (mixing languages within a sentence). Indian contact centers see both constantly.
This behavior is natural and functional. India recognizes 22 scheduled languages, and according to the 2011 Census, 26% of Indians are bilingual while 7% are trilingual. Hindi alone is spoken by over 43% of the population as a mother tongue, while English is spoken by roughly 10.6% (approximately 129 million people). In practice, millions of Indians operate in a fluid blend of Hindi and English that linguists call "Hinglish."
For contact center agents handling domestic calls, this means conversations rarely stay in a single language. An agent might greet a customer in Hindi, explain a technical process in English, and close with reassurance in Hindi. The customer might respond in pure Hindi, or in their own Hindi-English blend. In southern and eastern India, agents may layer in Tamil, Telugu, Bengali, or Kannada alongside English.
This is not sloppy communication. It is efficient, culturally natural, and often preferred by both agents and customers. The problem is not the code-switching itself. The problem is that most QA tools were never built to handle it.
Traditional quality assurance in contact centers relies on three approaches, and all three break down when conversations include code-switching.
Keyword spotting fails. Most automated QA tools use keyword detection to flag compliance phrases, required disclosures, or prohibited language. These systems are typically trained on a single language. When an agent delivers a mandatory disclosure partly in Hindi and partly in English, keyword spotting catches fragments at best. It misses the context entirely.
Manual review does not scale. Human QA analysts can understand code-switched conversations, but only if they are bilingual in the right language pair. Finding QA reviewers fluent in Hindi, English, Tamil, and Telugu is difficult and expensive. Even when teams have bilingual reviewers, the math is punishing. Industry benchmarks show that manual QA teams review roughly 2 to 5% of total calls. For a BPO with 300 agents handling 3,000 calls per day, that means 2,850 to 2,940 calls go unreviewed every single day.
Sampling misses language-specific patterns. Random sampling assumes that a small percentage of calls is representative of the whole. But language-related compliance failures tend to cluster. If agents in one region consistently skip a Hindi disclosure, or if code-switching causes them to miss a required English compliance phrase, a 3% sample is unlikely to surface the pattern. The QA team sees a clean sample while systemic issues persist undetected.
The result is a QA program that is functionally blind to the majority of conversations happening in your contact center.
Modern AI transcription and analysis systems approach code-switching through several technical capabilities working in sequence.
Language detection at the segment level. Rather than classifying an entire call as "Hindi" or "English," advanced speech recognition models detect language shifts at the phrase or even word level. When an agent says "Please hold karein, main aapka issue check karta hoon," the system identifies "Please hold" and "issue check" as English segments and the connecting phrases as Hindi. This granular detection is critical because code-switching often happens mid-sentence.
Contextual understanding across language boundaries. Once the system identifies which words belong to which language, it applies contextual understanding that spans the language boundary. The Hindi word "karein" after "please hold" is understood as a polite imperative form, not an isolated Hindi token. This contextual bridging allows the AI to extract meaning, sentiment, and intent from the conversation as a unified whole rather than two disconnected language streams.
Sentiment analysis that crosses languages. Customer frustration does not stay in one language. A customer might start calmly in English, switch to Hindi when they become upset, and return to English when the agent resolves the issue. AI systems trained on multilingual data can track sentiment shifts across these transitions, providing accurate emotional mapping regardless of which language the speaker is using at any moment.
Compliance monitoring in blended language. This is where multilingual AI becomes essential for regulated industries. If an agent is required to state a disclosure in English but wraps it in Hindi context ("Sir, main aapko inform karna chahta hoon ki this call is being recorded for quality purposes, toh please continue karein"), the AI can verify that the English disclosure was delivered completely even though it was embedded in a Hindi sentence.
Current industry benchmarks indicate that English transcription typically achieves word error rates (WER) below 8%, while Hindi transcription lands in the 15 to 20% range. For code-switched content, accuracy depends heavily on the training data. Systems trained specifically on Hinglish conversational data close this gap significantly compared to systems that treat Hindi and English as entirely separate languages.
India's BPO industry employs over four million people and is projected to reach a market value of USD 139.35 billion by 2033. Domestic BPO operations, which serve Indian customers, require language support that matches India's linguistic reality.
The essential language coverage for Indian domestic BPOs includes:
Beyond these, Gujarati, Odia, Malayalam, and Punjabi round out the top tier. The reality is that Indian BPOs serving domestic customers need at minimum 7 to 10 language capabilities, and the conversations in each language will frequently include English code-switching.
Tier-2 city expansion is intensifying this requirement. Cities like Indore, Jaipur, Coimbatore, and Visakhapatnam are growing as BPO hubs, and their workforces naturally operate in regional languages blended with English and Hindi.
Gistly was built with Indian multilingual reality as a core design consideration, not an afterthought. The platform supports 10+ languages, including Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, and English, with specific support for Indic language code-switching.
Full transcription of code-switched calls. When an agent moves fluidly between Hindi and English, Gistly's transcription engine captures both languages accurately within the same transcript. There is no need to classify a call as "Hindi" or "English" before processing. The system handles the blend natively.
QA scorecards that work across languages. Gistly's custom QA scorecards evaluate agent performance regardless of which language the agent is using. If your scorecard requires a proper greeting, needs verification, and mandates a closing disclosure, Gistly checks for these elements whether they were delivered in Hindi, English, Tamil, or a blend.
Compliance monitoring in any supported language. For BPOs operating under India's Digital Personal Data Protection (DPDP) Act, compliance language may need to be delivered in the customer's preferred language. Gistly monitors for required disclosures and consent statements across all supported languages, flagging gaps even when the conversation switches languages mid-disclosure.
100% call coverage, not sampling. Because Gistly audits every call automatically, language-specific compliance patterns that would hide in a 3% manual sample become visible. If agents in your Hyderabad center consistently miss a Telugu disclosure, or if Hindi-speaking agents in your Delhi center skip an English compliance phrase, the data surfaces immediately.
48-hour deployment. Gistly's speed to value means teams can start auditing multilingual calls within two days, not weeks or months of configuration and language model training.
Implementing effective QA for multilingual operations requires a structured approach. Here is a practical framework for BPO leaders.
Step 1: Audit your language distribution. Before selecting tools or building scorecards, understand the actual language mix in your contact center. Pull a representative sample of 500 to 1,000 calls and categorize them: percentage in English only, percentage in Hindi only, percentage code-switched, and percentage in regional languages. Most Indian domestic BPOs find that 40 to 60% of their calls involve some degree of code-switching.
Step 2: Design language-aware QA scorecards. Your scorecards should evaluate outcomes, not language choice. If the agent delivered the required disclosure accurately and the customer understood it, the language used should not affect the score. Build scorecards that assess compliance completeness, customer understanding, empathy, and resolution regardless of which language was used.
Step 3: Set transcription accuracy baselines. Establish word error rate thresholds for each language your center handles. Target under 10% WER for English, under 15% for Hindi, and adjust expectations for other languages based on available benchmarks. Track these metrics monthly and flag degradation early.
Step 4: Train QA teams on multilingual evaluation. Even with AI handling transcription and scoring, human QA reviewers need to understand the nuances of code-switching. Train them to evaluate whether language blending aided or hindered customer understanding, rather than penalizing agents for not staying in a single language.
Step 5: Monitor language-specific trends. Use your QA platform's analytics to track performance metrics by language. Look for patterns: Do compliance scores differ between Hindi-only and code-switched calls? Are customer satisfaction scores higher when agents mirror the customer's language choices? These insights drive targeted coaching.
Step 6: Review and iterate quarterly. Language patterns shift as your agent base changes, as you expand to new geographies, or as customer demographics evolve. Revisit your language distribution audit every quarter and adjust your QA program accordingly.
Which AI QA tool supports Hindi-English code-switching?
Gistly supports Hindi-English code-switching natively as part of its 10+ language capability set. The platform transcribes and analyzes code-switched conversations without requiring calls to be pre-classified by language. Other platforms like Mihup and Gnani offer speech analytics for Indian languages, but Gistly combines multilingual transcription with full QA automation, compliance monitoring, and 100% call coverage in a single platform.
How accurate is AI transcription for Indian languages?
Accuracy varies by language and the specific AI model used. Current industry benchmarks show English transcription achieving word error rates (WER) below 8%, while Hindi typically falls in the 15 to 20% range. Code-switched Hinglish accuracy depends on training data quality. Systems specifically trained on Indian conversational data, including code-switching patterns, achieve significantly better results than general-purpose models.
Can AI detect compliance violations in Hindi calls?
Yes. AI systems trained on Hindi and Hinglish content can monitor for required disclosures, prohibited language, and consent verification regardless of which language they are delivered in. This includes detecting when a required English compliance phrase is embedded within a Hindi conversation, or when an agent delivers a disclosure in Hindi that should have been in English per regulatory requirements.
What is the best AI QA tool for Indian BPOs?
The best tool depends on your specific requirements. For BPOs needing multilingual QA with code-switching support, compliance monitoring under the DPDP Act, and 100% call auditing, Gistly is purpose-built for this use case. Key evaluation criteria should include the number of Indian languages supported, code-switching handling capability, compliance monitoring features, and deployment speed. Gistly offers 48-hour deployment with support for 10+ languages including major Indic languages.
How do I maintain QA consistency across multilingual teams?
Consistency starts with language-neutral scorecards that evaluate outcomes rather than language choice. Use AI-powered QA to apply the same evaluation criteria across all calls regardless of language. Supplement with language-specific coaching based on trend data. The goal is ensuring that a call handled in Tamil receives the same quality standard as one handled in English.
Does multilingual QA cost more than English-only QA?
With manual QA, yes. Hiring bilingual or trilingual reviewers commands a premium, and you need enough reviewers to cover each language your center handles. With AI-powered QA, the cost difference is minimal because the same platform processes all languages. Gistly's multilingual capability is included in the standard platform, not an add-on.
---
---
CTA
Your agents already speak multiple languages. Your QA tool should too. See how Gistly audits 100% of calls across 10+ languages with native code-switching support. Request a free demo →
Gistly audits every conversation automatically — compliance flags, QA scores, and coaching insights in 48 hours.