Custom AI Chatbots — Trained on Your Data, Deployed on Your Stack
Turn your documentation, support tickets, and internal knowledge into a conversational AI that answers customers accurately, around the clock.
What Is a Custom AI Chatbot?
And why off-the-shelf solutions fall short for most businesses
Off-the-shelf chatbots rely on generic language models that know nothing about your product, your pricing, or your policies. They hallucinate answers and frustrate customers.
A custom AI chatbot is trained exclusively on your data — product docs, FAQs, support transcripts, internal wikis — then deployed inside your existing infrastructure. It speaks your brand voice, respects your business rules, and cites sources so users can verify every answer.
Off-the-Shelf Bots
- Generic responses with no product knowledge
- No control over data or model behaviour
- Vendor lock-in with monthly subscription
- Limited integration options
Custom-Built Chatbot
- Answers grounded in your actual content
- Full ownership of data pipeline and model
- One-time build cost, predictable API spend
- Deploys wherever you need it
Data Sources We Can Ingest
The chatbot is only as good as the knowledge behind it
- Documentation and knowledge bases — Confluence, Notion, GitBook, Markdown repos
- PDFs and Office documents — product manuals, contracts, onboarding guides
- Website content — crawled and chunked automatically with sitemap support
- Databases — SQL Server, PostgreSQL, or any ODBC-compatible source via read-only queries
- Support tickets and chat logs — Zendesk, Freshdesk, Intercom exports
Data is chunked, embedded, and stored in a vector database. A scheduled pipeline keeps the index in sync so the chatbot always reflects your latest content.
Privacy and Hosting Options
Choose the deployment model that fits your compliance requirements
Cloud (Managed)
Hosted on managed infrastructure with OpenAI or Anthropic APIs. Fastest to deploy, lowest upfront cost. Data encrypted at rest and in transit.
Private Cloud (VPC)
Deployed inside your own AWS, Azure, or GCP account. Your data never leaves your cloud tenant. Ideal for regulated industries.
On-Premises
Fully air-gapped deployment with open-source models (Llama, Mistral). Zero external API calls. Maximum data sovereignty.
Pricing
Transparent, fixed-price packages so you know exactly what you are paying for
- Single deployment channel (web widget)
- Up to 50 pages of training data
- OpenAI GPT-4 integration
- Basic analytics (queries, satisfaction)
- 2 rounds of prompt tuning
- 30-day post-launch support
- Up to 3 channels (web, WhatsApp, Slack)
- Up to 500 pages of training data
- Choice of GPT-4 or Claude
- RAG pipeline with scheduled re-indexing
- Analytics dashboard with conversation logs
- Human handoff escalation flow
- 5 rounds of prompt tuning
- 60-day post-launch support
- All channels including custom integrations
- Unlimited training data sources
- Private cloud or on-prem deployment
- Open-source LLM option (Llama, Mistral)
- Custom fine-tuning and evaluation suite
- Role-based access and audit logging
- SLA with guaranteed response times
- 90-day post-launch support + maintenance plan
Common Questions
Honest answers to the concerns we hear most often
Will the chatbot hallucinate wrong answers?
Every response is generated using Retrieval-Augmented Generation (RAG), which means the model only answers from retrieved chunks of your actual content. When no relevant source is found, the bot says so instead of guessing. We also add source citations so users can verify answers themselves.
Is my data safe? Who can see it?
On the managed cloud tier, data is encrypted at rest and in transit and is never used to train third-party models (both OpenAI and Anthropic offer zero-retention API plans). For stricter requirements, the private cloud and on-prem tiers ensure your data never leaves your infrastructure.
How much will API costs run after launch?
For most businesses handling under 10,000 conversations per month, API costs typically range from $50 to $200/month with GPT-4. We optimize token usage through smart chunking, caching, and model routing (sending simple queries to cheaper models). The on-prem tier eliminates API costs entirely.
Am I locked into a specific AI provider?
No. The architecture uses a model-agnostic abstraction layer, so you can switch between OpenAI, Anthropic, or open-source models without rebuilding the pipeline. Your embeddings and vector store are yours to keep regardless of which LLM provider you use.
Ready to Scope Your Chatbot?
Book a 30-minute call and walk away with a clear plan — data sources, channels, hosting model, and a fixed-price quote.