01
Customer support teams drowning in tier-1 queries that are 80% the same 12 questions.
Fits you
Multilingual bots shipping
80%
Tier-1 automation
4.6/5
Customer CSAT
1.4s
Median response
AI Chatbots
Your customers speak Tamil. Does your chatbot?
Generic GPT-wrapped chatbots fumble Indian languages, Indian names, and Indian intent. We build bots that handle all three — and know when to hand off to a human.
எனக்கு refund வேண்டும், order #AB1234
கண்டிப்பாக. Order AB1234 — ₹1,499-க்கு refund initiate பண்ணிட்டேன். 3-5 days-ல bank-க்கு வந்துடும் ✅
Thanks! Update WhatsApp-la varuma?
Yes, credit notification-a WhatsApp message anuppura. 👍
FintechHealthcareE-commerceEdtechReal EstateInsuranceFintechHealthcareE-commerceEdtechReal EstateInsuranceFintechHealthcareE-commerceEdtechReal EstateInsurance
Who this is for
Who needs more than a rule-based bot.
If any of these sound like you, we should talk.
02
E-commerce with returns, refunds, tracking, and reordering — where a bot can deflect 70% safely.
Fits you
03
Multilingual customer bases where call-centre agents can't handle every language shift in one conversation.
Fits you
04
Sales / lead-qualification bots for real estate, insurance, education — before a human calls back.
Fits you
The proof
The four numbers that separate a good bot from a gimmick.
78%
Automation rate
queries resolved without human
12
Languages + code-mixed
we deploy in production
< 2s
Response latency
or users drop the chat
94%
Intent accuracy
on first-turn classification
What we build
Where our bots run.
01
Official Cloud API, templates, flows, interactive buttons.
02
Website widget
Embedded chat, context-aware, live-agent handoff.
03
Voice IVR
AI-driven phone support in regional languages.
04
Internal agents
Knowledge bots for your team, on your data.
Our approach
Not a template. Your answer, engineered.
Four disciplines we apply to every engagement — tuned to your context, your team, your constraint.
- 01DiscoverStep 01 of 03
LLM + rules + human. All three.
Pure LLM bots hallucinate. Pure rule-based bots are stupid. We design three-layer bots: rules for deterministic flows (refund, track), LLM for conversational ones, human for edge cases.
- 02DesignStep 02 of 03
Knowledge, not just prompts.
RAG over your actual policy docs, product catalogue, past tickets. Answers grounded in your data, not whatever the LLM imagines.
- 03DeliverStep 03 of 03
Escalation is a feature, not a failure.
A bot that knows when to escalate is more valuable than one that tries to answer everything. We train the handoff like a first-class path.
ClientA financial services providerFintech
80% of tier-1 queries <em>handled by the bot</em>. In Tamil, Hindi, and code-mixed English.
A Chennai-based NBFC supporting loan customers. Replaced a 40-agent call centre tier with a bot that handles application status, EMI info, payment, documents. CSAT went up.
01
80%
Tier-1 automation
02
4.6/5
Customer CSAT
03
34%
Cost reduction
04
1.4s
Median response
What it's like working with us
Our Tamil-speaking customers stopped hanging up. That was the success metric.
LN
Lakshmi N.
Head of Support · A financial services provider
Fintech · Chennai
Tech stack
The tools. Chosen for your reasons.
10technologies in rotation
01OpenAI GPT-4o
02Anthropic Claude
03Gemini 2.0
04Rasa
05LangChain
06Pinecone
07Weaviate
08WhatsApp Cloud API
09Twilio
10PostgreSQL
Process
How we actually work.
06 stages
01
Ticket audit
Top 20 intents, their volumes and outcomes.
Week 1
02
Flow design
Rules vs LLM vs human per intent.
Weeks 2-3
03
Knowledge base
RAG setup over your docs and data.
Weeks 3-4
04
Build + train
Intent classifier, dialog, integrations.
Weeks 4-8
05
Pilot
10% traffic, supervised, feedback loop.
Weeks 8-10
06
Scale
Full traffic, continuous improvement.
Week 10+
Questions
Answers, without the fluff.
Still have questions? Talk to us — we answer within a business day.
08common questions
01GPT-4, Claude, or Gemini?
Depends. GPT-4o for conversational breadth. Claude for nuanced multi-turn. Gemini for cost at scale. We benchmark all three on your actual data before picking.
02How do you prevent hallucinations?
RAG grounded in your documents, strict output constraints, confidence thresholds, and human-in-the-loop for low-confidence answers.
03What about data privacy?
Sensitive data stays on our infrastructure. We use LLM APIs with no-training clauses and on-prem options (Llama 3, Mistral) for highly regulated use cases.
04Can the bot speak Tamil / Hindi / Telugu properly?
Yes. We train on Indian-language data and post-process for natural expression. Not word-by-word Google Translate output — actual fluent conversation.
05How long does a typical engagement take?
Most projects run 10-18 weeks from kickoff to production launch. We share a milestone plan in week one and update weekly.
06Do you sign an NDA?
Yes. Standard mutual NDA on request, before the first technical conversation.
07Who owns the code and IP?
You do. Code is in your GitHub org from day one. All IP transfers unambiguously on delivery.
08What does your pricing model look like?
For v1 builds: fixed scope, fixed milestones. For ongoing work: monthly retainer with a defined team. We don't do time-and-material surprise billing.
Related
Explore more of what we do.
A bot that your customers actually enjoy.
Share your top-10 ticket types. We'll tell you the automation percentage we can credibly hit.
Free 45-min automation audit.