Kratya hires the agents, deploys the AI that scores and augments them, and stays accountable to the metrics. Not three vendors and a coordination problem. One operating partner.
Most AI CX companies sell software to your ops team. We are the ops team. Kratya hires, trains, and manages the agents — so when AI scoring finds a gap, we close it the same week. No ticket to a BPO. No dashboard nobody checks.
Our founders ran 100+ seat CX operations at UrbanCompany, Urbanic, and TravelPlus. Kratya exists because they couldn't buy what they needed. The product is shaped by 15 years of knowing what breaks at scale — not by guessing from the outside.
We don't start with automation and hope. We start human-led, build a scoring baseline, then let AI handle only what confidence and quality data say it should. Every AI failure becomes a training example. Automation grows because the numbers prove it should.
Scored in real time, filed instantly, available for coaching.
Most CX operations fail not because the technology is bad — they fail because no one company owns the full chain. SaaS measures. BPOs operate. AI startups automate. The customer experiences the gaps between them.
Your QA team scores 2% of conversations. The other 98% is invisible until it shows up in churn.
Agents get a one-time onboarding. Six months in, they've drifted from the playbook — and no feedback loop catches it.
AI bots resolve 70% of cases. The other 30% — edge cases, disputes, upset customers — fall through with no handoff and no context.
Kratya isn't a tool you add to your stack. It's the company that runs the stack.
We source, screen, and onboard agents matched to your customer profile — then keep developing them through a coaching loop powered by live QA data.
Auto-QA scores 100% of voice, chat, and email against your SOPs. No sampling. Evidence card filed for every interaction.
Voice and chat agents handle what they can resolve confidently — and route to humans the instant confidence drops. Automation grows as quality data proves it should.
One company on the SOW. When scoring finds a gap, the same team that found it closes it. Measured on metrics that move the business, not seat count.
Each step feeds the next. Nothing sits in a dashboard waiting to be acted on.
AI scores 100% of conversations across every channel and language.
Every response reconciled against your CRM and policies before it's filed.
Scores become prioritised coaching for agents and live prompts for AI.
AI handles cases where confidence and quality data say it should.
QA, CSAT, FCR, cost-to-serve — tracked weekly, reviewed monthly.
↻ The loop runs continuously. Every conversation makes the next one better.
Deployment targets baselined in month one, measured monthly, reviewed openly with every client.
Every conversation scored against your SOPs — vs the 2–5% industry standard.
Multiple of conversations evaluated relative to a manual QA team of the same headcount.
Score lift over a baselined month-one benchmark — the unlock condition for outcome-tied terms.
Time from AI confidence drop to a human agent receiving the call with full context.
From 10% AI handling at month one to 90% by month eighteen, in earned phases.
One company on the SOW, one team in the room, one set of numbers reviewed monthly.
We'd rather show you exactly where we are than let you discover it in due diligence.
Wherever you serve customers at scale and the cost of a missed interaction is real, Kratya fits.
Kratya is being built with traceability, interaction evidence, and governance in mind from day one — not retrofitted later. Our architecture supports the auditability, consent, and data handling requirements that regulated industries increasingly demand. TRAI's service call controls, RBI's responsible AI expectations, and DPDP's data fiduciary obligations all inform how we store, score, and surface conversation data.
In Sanskrit, Kratya means the action that ought to be done.
Not the observation that was filed. Not the score on the dashboard. The thing that should follow — the coaching, the fix, the kept promise — because someone finally knows the truth and has the authority to act on it.
In 2024, the technology to score every conversation — every call, every chat, in real time — finally existed. The models were ready.
The problem was never the technology. It was that no single company was willing to own what happened after the score. The coaching. The improvement. The automation that earns its way in through data. The outcome.
So we built it.
Between us, we've run CX operations at scale across three consumer companies. We knew the gap wasn't a technology problem. It was an accountability problem.
Kratya hires and develops the agents. We score every conversation with AI, close the coaching loop in the same platform, and automate only what the quality data says is ready. One company. One loop. One accountable outcome — not a SLA report, not a licence fee.
Every customer conversation carries a signal. A coaching moment. A broken promise. A handoff that didn't happen.
Kratya is the commitment to close that gap. Every conversation. Every agent. Every customer.
Observation without action is just watching.
Kratya is the doing.
We didn't build Kratya to enter a market. We built it because we ran CX at scale and couldn't buy what we needed.
15 years building CX at scale. At UrbanCompany, reduced CX cost from 30% to 8% of revenue and lifted NPS from negative to +35. At TravelPlus, cut fulfilment failure rate from 2.2% to 0.6% across 129 people. Built Kratya because the system he kept needing didn't exist.
Named to India's Top 100 CX Leaders. Spent years watching quality data arrive after the damage was done. Brings the auditor's precision and the operator's practicality to every Kratya engagement. Architect of our coaching loop.
Built and scaled AI decision systems at ShopDeck, Fairmatic, and UrbanCompany. Scaled engineering teams to 35+. At Kratya, he's building the scoring and automation infrastructure for production — not for demos. The same way he builds everything.
Tap any question. If we missed yours, write directly — these answers should hold up.
Convin and Salesken surface problems. Kratya is built to fix them. The difference is structural — we own the team and the AI together, so when scoring finds a gap, there's a direct path to the coaching action and the verified outcome. A dashboard that flags 20 issues doesn't help if no one closes the loop.
Your QA team gets more focused, not replaced. Kratya covers 100% of calls and surfaces the cases that actually need human judgment. Most internal teams find their work becomes higher-leverage once sampling noise disappears.
It breaks when automation is pushed before the data earns it. Kratya starts human-led, builds the scoring baseline, then automates only what confidence and quality data support. If AI drops below threshold mid-conversation, it routes to a human in under 2 seconds. Customers never hit a dead end.
Everything is baselined before anything scaled is measured against it. QA scores, CSAT, FCR — locked in month one and signed off by you. Monthly reviews include raw scoring data. You can pull the evidence card for any call, any time.
We're an AI-native CX operations company — we can run the team, run the AI, and own the quality loop end-to-end. Most clients start with scoring and coaching, then expand.
Pricing is discussed in the first conversation. Engagements vary by team size, current QA maturity, and how much of the chain we own. The model has a floor covering operating cost and a layer tied to measurable improvement — designed so our incentives match yours.
No SDR. No automated sequences. Sai responds personally within 24 hours.
Or write directly — cvsai@kratya.ai