Cartesia brings real-time voice AI to Bengaluru: what operations leaders need to know
Cartesia, a San Francisco-based startup building real-time voice AI agents, is launching operations in Bengaluru with a $2.5 million investment over the next 12-24 months. India became its second-largest market after it launched support for nine Indian languages in November 2025.
The company sits at the foundation layer: it trains voice models from scratch and provides the low-latency infrastructure to run them in real time. Think of it like OpenAI for interactive voice-enterprises and startups build their applications on top of Cartesia's core stack. OpenAI is a useful reference point for platform thinking, not a direct competitor.
India operations: scope and hiring
The Bengaluru office will start lean with a ~10-person team focused on onboarding and managing clients, plus research hires to extend the core models. The company is prioritizing India-specific business needs and scaling support.
Founded in September 2023 by Stanford AI Lab researchers Karan Goel, Albert Gu, Brandon Yang, and Arjun Desai, Cartesia has raised $100 million from Kleiner Perkins, Index Ventures, Lightspeed, and NVIDIA. Headcount has grown from 20 to 100 in two years, and global hiring continues.
Who's already using it
Enterprise adoption includes Magicbricks, Gupshup, and One Point One Solutions. Startup users include Grey Labs, SpeakX, and Supernova for voice tutoring and conversational AI.
These teams rely on Cartesia's real-time voice infrastructure to scale customer interactions, automate routine queries, and deliver more natural experiences without noticeable lag.
Why this matters for operations
- Latency and SLAs: Real-time agents reduce dead air and transfers. Lower latency helps hit stricter SLA targets during peak hours.
- Scale without headcount spikes: Spin up concurrent sessions for seasonal surges or marketing campaigns without scrambling for staffing.
- Language coverage: Support across nine Indian languages makes nationwide rollouts practical and consistent.
- Foundation layer control: Owning the base models and infra can reduce brittle integrations and improve reliability.
- Cost profile: Automation shifts cost from variable labor to predictable infrastructure-useful for budgeting but demands close KPI tracking.
Implementation checklist for ops leaders
- Use-case fit: Start with high-volume, bounded workflows (billing, order status, appointment booking, KYC steps). Avoid complex edge cases in the first phase.
- Latency targets: Set p50/p95 round-trip goals (e.g., p95 < 300-500 ms) and test during peak traffic windows.
- Data and privacy: Map PII flows, transcription storage, consent prompts, retention policies, and data residency requirements.
- Integration plan: Connect to CRMs, ticketing, IVR, and knowledge bases. Define fallbacks to human agents with clean transfer context.
- Workforce routing: Update WFM rules for hybrid queues (AI-first, human-assist). Train agents for mid-call takeovers.
- Quality management: Add call recording, redaction, and QA scoring for AI-led interactions.
- Pilot and iterate: Run an A/B pilot on one queue or region. Expand only after hitting KPI thresholds.
KPIs to track from day one
- Containment rate: Percent of calls closed by AI without human involvement.
- AHT and handle variance: Watch both median and outliers.
- First-contact resolution (FCR) and escalation rate.
- Latency p95 across peak periods, ASR accuracy/WER by language.
- CSAT/NPS for AI-handled interactions.
- Cost per interaction vs. human baseline, and SLA adherence.
India vs. US buyer mindset: practical implications
Karan Goel notes a gap: Indian firms often lead with cost; US firms emphasize co-investment for growth. For ops, the takeaway is simple-negotiate for unit costs, but don't starve the pilot.
Budget for experimentation so the model can learn your domain. The fastest ROI comes from a tight loop: small pilot, aggressive tuning, then scale.
Competitive landscape and procurement tips
In the US, Cartesia's peers include Deepgram, PlayHT, and Hume AI. When evaluating vendors, request latency and accuracy benchmarks on your real call traffic, not demo data. Check concurrency limits, failover behavior, and incident history.
- Portability: Ask about model export options, transcript formats, and how easy it is to switch ASR/TTS components later.
- Compliance: Verify SOC 2, ISO 27001, call recording laws, and language-specific accuracy commitments.
- Support model: Ensure you have named technical contacts during rollout and peak seasons.
Risk areas and how to mitigate
- Hallucinations or policy misses: Lock prompts, add guardrails, and restrict action scopes. Use supervised fine-tuning on your transcripts.
- Outage impact: Define IVR fallback rules and stand up a warm backup path. Monitor health endpoints in your NOC.
- Language drift: Re-benchmark accuracy quarterly across all supported languages and accents.
- Hidden costs: Model updates, redaction, and long-term storage add up. Model the full cost per resolved interaction.
30-60-90 day action plan
- Days 1-30: Identify 1-2 use cases, map data flows, set KPIs, provision sandboxes, and run latency tests with real traffic samples.
- Days 31-60: Launch a controlled pilot (one queue/region), enable QA scoring, and tune prompts and call flows weekly.
- Days 61-90: Expand coverage, integrate analytics into the ops dashboard, and renegotiate pricing based on observed volume and containment.
Bottom line: If your operation handles high call volumes in multiple Indian languages, Cartesia's India presence and foundation-layer control make it worth a pilot. Keep guardrails tight, measure everything, and scale only after you see consistent containment and SLA stability.
If you're upskilling your ops team for voice AI rollouts, explore role-based training here: Complete AI Training - Courses by Job.
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