Chatbots
A chatbot takes the routine questions off your team and answers them in seconds, around the clock, without anyone waiting for office hours.
What a chatbot actually changes
Most customer service teams spend a large share of their day on the same handful of questions. Where is my order. Do you ship to my country. How do I change my plan. None of it is difficult, all of it is interrupting, and every one of those messages sits in a queue while someone gets to it.
A chatbot answers that class of question immediately. Not because it is clever, but because the answer already exists somewhere in your documentation and the bot can find it faster than a person can. The measurable effect is a shorter first-response time and a support queue that contains fewer trivial tickets.
Where chatbots earn their keep
The value is not in replacing your team. It is in changing what reaches them.
Out-of-hours coverage. Questions asked at 11pm get answered at 11pm. For an e-commerce business this directly affects whether a hesitant buyer completes a purchase.
Peak absorption. Launches, sales and incidents produce spikes that a fixed-size team cannot absorb. A chatbot flattens the routine part of that spike.
Consistency. The same question gets the same answer regardless of who is on shift, which matters a lot when the answer involves a policy.
A record of what people ask. Conversation logs are an unusually honest source of product feedback. Recurring confusion in the chat is usually a symptom of something unclear in the product or the copy.
Grounded, not generic
The difference between a chatbot that helps and one that embarrasses you is where the answers come from.
A generic model answers from what it absorbed during training, which means it will confidently describe a returns policy you have never had. We build retrieval-grounded bots: your documentation, product data and policies are indexed, and the model answers from retrieved passages rather than memory. When nothing relevant is retrieved, the bot says so and hands over rather than improvising.
That constraint is also what makes the bot maintainable. Updating an answer means updating the source document, not retraining anything.
Integration is what makes it useful
A chatbot that can only quote the FAQ is a search box with better manners. The step that changes its value is connecting it to the systems where the answers actually live – order management, booking, CRM, subscription state.
Once that connection exists, "where is my order" stops being a question the bot deflects and becomes one it resolves. That is usually the point at which deflection rates move from marginal to worth the investment.
Starting small is the right move
You do not need to automate everything. The sensible first release covers the ten or fifteen questions that make up the bulk of your inbox, does them well, and hands over cleanly on everything else.
That version is quick to build, easy to evaluate against real numbers, and gives you a grounded basis for deciding how much further to go.
What you get
Answering questions
AI handles the most common customer questions automatically, cutting first-response time and freeing your team for the cases that actually need a person.
Product and service search
The chatbot helps customers find products or information in natural language, so they stop bouncing off a search box that only matches exact phrases.
Order and booking support
The bot walks users through the whole process step by step, which reduces input errors and abandoned carts.
CRM integration
Every conversation is written back to your CRM, so you can analyse what customers actually ask and match offers to it.
Escalation to a human
Clear handover rules for anything the bot shouldn't answer alone. The full conversation history goes with it, so the customer never repeats themselves.
Grounded answers
Responses are drawn from your documentation, product data and policies rather than the model's general knowledge, which is what keeps a bot from inventing things.
How we work
- 01
Scope the questions worth automating
We start from your real support inbox and chat logs. The top repeated questions become the first release; the long tail waits until the basics are solid.
- 02
Connect the knowledge sources
Docs, product catalogue, FAQs and policies are indexed so the bot answers from your material, with citations back to the source where that helps trust.
- 03
Wire up the systems
Order status, bookings, CRM records. This is the step that turns a FAQ widget into something that can actually resolve a request.
- 04
Test against real conversations
We replay historical tickets and adversarial phrasings, and set the guardrails for what the bot must refuse or hand off.
- 05
Launch and tune
After go-live we watch deflection rate, handover rate and the questions it got wrong, and feed those back into the knowledge base.
Tools and technology
Where a solid open-source tool exists, we choose it over a closed one. No lock-in to a single vendor, and costs you can actually predict.
- Vercel AI SDK
- Chat SDK
- LangChain
- Next.js
- PostgreSQL
- pgvector
- Supabase
- OpenAI
- Anthropic Claude
- HubSpot
- Zendesk
- Intercom
Frequently asked questions
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Ready to take routine questions off your team?
Message usTell us what your customers ask most often and we will suggest a scope for the first release and the knowledge sources the bot should draw on.
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