Question 1

How much does an AI chatbot or integration cost?

Accepted Answer

A focused integration — a support chatbot, document Q&A or a recommendation feature — typically takes 4–8 weeks to build. Ongoing cost is mostly model usage, which we optimise deliberately: caching, smaller models for simple steps and prompt efficiency often cut running costs by an order of magnitude.

Question 2

Which AI models do you work with?

Accepted Answer

We work with the current frontier models from Anthropic, OpenAI and Google, plus open-source options like Llama and Mistral where they fit. We're model-agnostic by design and build so that swapping providers later is straightforward rather than a rewrite.

Question 3

Do we need our own data to build an AI feature?

Accepted Answer

Not always. For LLM-based features like chatbots or document Q&A, you can use pre-trained models with your own documents — no training required. Custom prediction models (churn, demand forecasting) do need historical data, typically at least six months of it.

Question 4

What's the difference between fine-tuning and RAG?

Accepted Answer

RAG (retrieval-augmented generation) gives an existing model access to your knowledge base at question time — no training, and easy to update whenever your content changes. Fine-tuning adjusts the model itself for a specific task or style. We use RAG for most knowledge use cases and fine-tune when accuracy or latency demands it — see our RAG chatbot for AVYANTA for a live example.

Question 5

How do you handle AI hallucinations and safety?

Accepted Answer

Every AI feature we build includes evaluation suites, output validation, guardrails and fallback logic for when the model isn't confident. We test adversarially before launch and monitor outputs in production, so regressions get caught before your users find them.

Question 6

Can AI be added to our existing website or app?

Accepted Answer

Yes — that's the most common request. We integrate into existing websites and mobile apps through your current stack, so you don't need to rebuild anything to add an AI feature.

Question 7

Is our data safe when using AI models?

Accepted Answer

It depends on the setup, and we're specific about it: enterprise API tiers generally don't train on your data, and for sensitive workloads we can run open-source models in your own infrastructure so nothing leaves your environment. We document exactly where your data goes before anything ships.

Question 8

Can you use open-source models instead of paid APIs?

Accepted Answer

Yes — and we often do. Open-source models can run dramatically cheaper at scale and keep data in-house. We evaluate both against your accuracy, latency and cost requirements rather than defaulting to the most expensive option.

Question 9

How much does building in-house cost compared to hiring you?

Accepted Answer

A typical integration takes 4–8 weeks with us. Building the same capability in-house usually means hiring ML engineers and takes 3–6 months before anything ships. For most companies, bringing us in first and transferring knowledge afterwards is the cheaper path.

Question 10

What happens after the AI feature is live?

Accepted Answer

We monitor model performance, cost per request and accuracy drift, since model behaviour changes as providers update them. You get documentation and training so your team can maintain it — or we handle ongoing improvement on a retainer.

Chatbots that actually answer the question.

Why AI Chatbot?

AI Chatbot solutions we deliver.

Support Assistants

Product & Docs Q&A

Lead Qualification Bots

Internal Knowledge Assistants

WhatsApp & Multi-channel

Human Handoff & Inbox

Order Status & Tracking

Product Discovery & Recommendations

Why teams choose AI Chatbot.

Grounded, not guessing

It knows when to stop

Protected against manipulation

Every conversation is a lead

Cheap to run when built well

Improves with use

The tools we pair
with AI Chatbot.

What an AI chatbot actually costs.

Model usage

Vector database

Hosting

WhatsApp Business API

Maintenance retainer

Common questions,
honest answers.

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