AI chatbots and assistants
Website, WhatsApp and in-app chatbots for support, sales and onboarding that answer from your documentation using retrieval-augmented generation (RAG), cite sources and hand off to your team.
Flagship · AI
AI chatbot, agent and automation development that reaches production
Arokha is an AI chatbot and agent development company based in Hyderabad, India. We build support and sales chatbots, AI agents that act through your tools, voice agents and workflow automation that run inside the systems your team already uses. Every build comes with evaluations, guardrails, audit logs and a clear route to a person when the AI should not answer.
Who it's for
Built for teams like yours
- Support and customer-experience leads handling a high volume of repetitive chats, tickets or calls
- Operations teams moving data between CRMs, inboxes, spreadsheets and internal tools by hand
- Product teams adding an AI assistant, search or agent feature to an existing app
- Business heads who have run AI pilots and now need them reliable enough for real customers
Problems we solve
What usually goes wrong
Pilots that never ship
The prototype impressed in a demo, but it has no evaluations, monitoring or owner, so nobody will put it in front of customers.
Answers you cannot control
Generic chatbots make things up, ignore policy or share what they should not. We ground answers in approved sources and add guardrails and escalation.
Hours lost to repetitive work
Staff copy data, triage requests and chase approvals by hand. A well-designed automation handles the routine steps and keeps a person on the judgement calls.
AI that sits in another silo
An assistant that cannot read from or write to your CRM, helpdesk or database adds a tool instead of removing work. We integrate it with the systems you run.
What we build
Services within AI Agents, Chatbots & Automation
Each one can be scoped on its own or combined into a single engagement.
AI agents
LLM agents that take actions through your tools and APIs, such as looking up orders, updating CRM records, drafting replies or raising tickets, with the permissions and approval steps you define.
AI voice agents
Inbound and outbound call flows for booking, lead qualification, reminders and first-line support, with transfer to a live agent and full call transcripts.
Workflow and process automation
Multi-step automations across CRMs, helpdesks, email, spreadsheets and internal tools, built with n8n, Make or custom code, with human checkpoints where judgement matters.
LLM features inside your product
Summarisation, semantic search, classification, document extraction and content generation added to your web or mobile app, with cost and latency controls.
Deliverables
What you receive
The exact list is agreed in your scope document. These are the deliverables most engagements include.
- Use-case shortlist with expected impact, risks and success measures agreed before build
- Production chatbot, agent, voice flow or automation deployed to your channels
- Knowledge-base ingestion and retrieval pipeline, with a process for keeping content current
- Evaluation set and test report covering accuracy, refusals and edge cases
- Guardrails, escalation rules, audit logs and an admin view your team controls
- Monitoring for answer quality, usage and model cost, plus runbooks and prompt documentation
How an engagement runs
4 phases, each with a clear output
You review and sign off at the end of every phase before the next one starts.
Discover
We review the workflow, sample conversations or tickets, data sources and compliance limits, then agree a shortlist of use cases and how each will be measured.
Output Use-case brief and success measures
Pilot
We build a working version on your real data in a staging environment and test it against an evaluation set your team helps write.
Output Working pilot and evaluation report
Go to production
We connect your channels and systems, add guardrails, logging, monitoring and escalation, and roll out in stages so issues surface early.
Output Live system with monitoring
Hand over and improve
We hand over documentation, prompts and runbooks, train your admins, and review real usage to improve answers and extend automation coverage.
Output Runbooks, admin training and an improvement plan
Technology
Typical models and tools
We choose tools to suit your requirements, existing systems and team, and explain the reasoning in writing.
- Models
- Orchestration and retrieval
- Channels
- Voice
- Automation
Engagement models
Ways to work with us
Every engagement has a written scope, milestones and a named point of contact.
Fixed-scope pilot
One use case taken from discovery to a working pilot on your data, judged against success measures agreed up front.
Best for Teams proving AI on a single workflow
Fixed-scope production build
A defined rollout to your channels and systems, delivered in milestones you review and accept.
Best for Use cases already approved for production
Improvement retainer
Monthly monitoring, content updates, evaluation reviews and new intents or automations as usage grows.
Best for Live assistants and automations
FAQs
Common questions about AI Agents, Chatbots & Automation
How much does AI chatbot development cost in India?
Cost depends on the number of channels, integrations and languages, and on how much your content needs preparing. A chatbot that answers from your website and documents is a much smaller build than an agent that updates your CRM or handles bookings. We give a fixed quote after a short scoping call and usually suggest starting with a pilot on one use case.
Can the chatbot work on WhatsApp?
Yes. We build assistants for WhatsApp through the WhatsApp Business Platform, as well as for website chat, Slack, Microsoft Teams and in-app chat. One knowledge base and one set of rules can serve several channels.
Which LLM should we use?
It depends on the accuracy you need, the languages involved, latency, cost and where your data may be processed. We test candidate models against an evaluation set built from your own questions, recommend one, and design the system so the model can be changed later without a rebuild.
How do you stop the AI giving wrong answers?
We ground answers in approved sources, restrict what the assistant is allowed to do, add refusal and escalation rules, and test against real questions before launch. After launch, logs and user feedback show where answers need improving, and we update content and prompts accordingly.
What is the difference between an AI chatbot and an AI agent?
A chatbot answers questions. An agent can also take actions, such as looking up an order, updating a record or booking an appointment, through tools and APIs you approve. Many projects start as a chatbot and add agent actions once the answers are reliable.
Is our data used to train AI models?
We set up model providers so that your data is not used for training wherever the provider offers that option, keep access to the minimum needed, and can redact personal data before it reaches a model. We document where your data is stored and processed so your security team can review it.
Related services
Often combined with this service
Talk to us about AI Agents, Chatbots & Automation
Tell us what you want to achieve. We'll come back with questions, a suggested approach and the next step, whether that's a scoping call or a written proposal.