AI engineering
Chatbots that answer from your actual policies, and know when to stop.
An AI chatbot answers customer questions in text on your website, WhatsApp or in-app, using your real product information rather than a generic model's guess. AISphereX builds custom AI chatbots grounded in your documentation, pricing and policies, with escalation rules that hand the conversation to a person at the right moment, and reporting that shows you what customers actually asked. We build it, connect it to your helpdesk and CRM, and maintain it as your content changes.
A chatbot is easy to launch and easy to regret. The ones that work are grounded in real content, honest when they do not know, and quick to hand over. The ones that damage you are confidently wrong about your refund policy at two in the morning.
What you get
The scope, in plain terms.
- Grounded answers
- The bot answers from your documentation, pricing, policies and past tickets. When the answer is not in there, it says so and offers a human instead of inventing one.
- Website, WhatsApp and in-app
- One brain, the channels you need. WhatsApp matters for a lot of markets and is treated as a first-class channel, not an afterthought.
- Escalation that works
- Rules for when to hand over, routing to the right queue, and the full conversation carried across so the customer does not start again.
- Helpdesk and CRM integration
- Conversations land as tickets or activities on the right record, so your team keeps one inbox rather than two.
- A content loop
- Reporting on what was asked, what it could not answer and where it was corrected. That list is the most useful thing the bot produces in month one, because it tells you what your documentation is missing.
- Tone and boundaries
- It sounds like your company, and there are subjects it declines to discuss. Both are configured, reviewed and testable.
How it runs
From first call to something live.
- 01
Audit what you already answer
Existing tickets, FAQs, docs and the questions your team is tired of. This decides whether a chatbot is even the right tool.
- 02
Ground it
We build the retrieval layer over your real content and test that answers are traceable back to a source.
- 03
Set the edges
What it must not answer, when it escalates, and what it says when it does not know.
- 04
Pilot behind a door
Live for a subset of traffic or internal users first, with every conversation reviewed.
- 05
Open up and keep tuning
Widen the traffic, watch the gaps report, and keep the content current as your product changes.
What it is built on
Chosen per project, not per fashion.
- Retrieval
- Your documents, indexed and searched so the model answers from sources rather than memory, with the source available for checking.
- Reasoning
- A current frontier model, constrained by instructions and by what retrieval returns.
- Channels
- A website widget, the WhatsApp Business platform, and in-app where it applies.
- Downstream systems
- Helpdesk, CRM and order or billing systems, so the bot can look things up rather than guess.
Is this you
We would rather disqualify early than waste your quarter.
This is a good fit when
- You answer the same fifty questions every week.
- Support volume is highest outside the hours you staff.
- Your documentation is decent, or you are willing to fix it as part of this.
- You sell in a market where WhatsApp is where customers actually message you.
It is the wrong call when
- Your product information is scattered, outdated and contradictory. Grounding a bot on that publishes the mess at speed.
- Every enquiry is bespoke and high value. Route those to a person.
- You want to remove human support entirely. That is not what this is, and the escalation path is not optional.
Proof
We did not learn this on client projects.
ViveLead is our own CRM and HRMS platform. We designed it, built it, shipped it on web, Android and iOS, and we run it in production for paying customers. Everything we do for clients is done by the team that has to answer for that.
See the case study →Questions we get asked before signing.
For simple FAQ deflection, an off-the-shelf tool is often the right answer and we will say so. Custom work earns its cost when the bot has to look things up in your systems, follow policy logic, work across channels with one brain, or meet requirements a SaaS tool will not bend for. If your case is the simple one, we would rather tell you than sell you a build.
By making it answer from retrieved sources rather than from the model's own memory, testing that answers trace back to a document, and giving it an explicit way to say it does not know. We also test it adversarially before launch, including the questions a frustrated customer asks.
Yes, through the WhatsApp Business platform, and we treat it as a primary channel rather than a bolt-on. There is a business verification and template approval process on Meta's side that we will walk you through, because it is usually the slowest part of the timeline.
The major languages are well covered by current models, including Hindi and English mixed together, which matters more than people expect. Quality varies by language, so we test in the languages you actually sell in before committing to them.
A grounded bot on one channel with decent existing documentation is typically a few weeks. The variable is almost never the AI. It is how good your content is and how long WhatsApp verification takes.
We keep it current. Your pricing and policies change, customers ask things nobody anticipated, and models get updated. We review the gaps report with you and keep the content and behaviour in sync with the business.
Related
Most clients end up combining these.
Next step
Thinking about ai chatbots?
You get a scoping conversation with the people who would do the work, not a sales call. If we are not the right fit we will say so on that call.