AI Solutions
Chatbots that answer correctly,or hand over.
Grounded in your documentation, with citations, confidence thresholds and a clean escalation to a person.
Sound familiar?
- “Your support team answers the same twenty questions every day.”
- “The chatbot you tried made things up, so you turned it off.”
- “Customers message on WhatsApp at 11pm and get a reply the next afternoon.”
Capabilities
What it does.
Grounded answers
Responses drawn from your documentation and records, with the source cited.
Knows when to stop
Confidence thresholds route uncertain questions to a human instead of guessing.
Multilingual
Handles Hindi, English and regional languages, including code-mixed messages.
On your channels
Website, WhatsApp Business, app and email from one knowledge base.
Clean handover
Escalation carries the full conversation so the agent isn't starting cold.
Measured
Deflection rate, resolution rate and escalation reasons reported weekly.
How we deploy it
The path from pilot to production.
Most AI projects die between a good demo and a working deployment. This is the sequence that avoids that.
Content audit
We look at what your support team actually answers, not what your FAQ says.
Grounding
Your documents indexed with retrieval, so answers come from your material rather than the model's memory.
Evaluation set
Real historical questions scored before launch, so quality is a number.
Pilot
One channel, monitored, with every escalation reviewed for a fortnight.
Expand
Additional channels and languages once deflection is proven.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Enterprise API tiers contractually exclude your data from model training.
- Conversation logs stored in Indian regions and retained only as long as you specify.
- Personally identifiable information can be redacted before it reaches the model.
- Open-weight models can run in your own VPC where policy forbids external processing.
What it costs to run
A typical support conversation costs between ₹0.50 and ₹3 in model usage depending on length and model choice. We instrument spend per conversation from day one and set hard daily caps.
Technology
What we build it with.
- Anthropic Claude
- OpenAI
- LangChain
- pgvector
- Python
- Next.js
- WhatsApp Business API
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
By grounding it in your content and requiring citations, then setting a confidence threshold below which it escalates rather than answers. A model asked to answer freely will confidently fill gaps — the fix is architectural, not a better prompt.
Yes. Code-mixed Hindi-English is extremely common in Indian support conversations and modern models handle it well. We test against real message samples from your channels.
For a well-scoped support bot with good documentation, 40–65% of conversations resolved without a human is realistic. Anyone promising 90% either has very simple queries or is counting deflection generously.
That's an AI agent rather than a chatbot — see our AI Agents page. The distinction matters: a chatbot informs, an agent acts in your systems.
Start here
Tell us the process, not the technology.
The best AI projects start from an expensive manual task, not from a decision to use AI. Describe the task and we'll tell you honestly whether this is the right tool.