82%
of customers would rather chat with an AI bot than wait for a live agent (Tidio, 2026 Chatbot Statistics)
$80B
in contact center labor costs Gartner predicts conversational AI will cut by 2026
23%
a year is the projected growth rate of the conversational AI market through 2028
What We Build
Every Chatbot Type, Grounded in Your Data
From a simple FAQ widget to a transactional assistant that takes real actions, every chatbot we ship is tested for accuracy before launch.
FAQ & Support Chatbots
Answer common questions instantly, escalate the rest to a human, and cut first-response time to seconds.
RAG Knowledge Chatbots
Chat over your documents, wikis, and product data with citations, so every answer traces back to a real source.
Transactional Assistants
Book appointments, process orders, and update accounts, not just talk, actually complete the task end to end.
Conversational AI for Customer Service
Around the clock coverage for repeat questions, with seamless handoff to your support team for anything complex.
Multi-Channel Deployment
One chatbot brain, deployed across your website widget, WhatsApp, Slack, and mobile apps.
Guardrails & Evaluation
Topic boundaries, hallucination checks, and an evaluation suite tested before launch and monitored after.
Our Process
How We Build Your Chatbot
Five steps from first call to a chatbot in production, each one signed off before we move to the next.
1. Discovery & Data Audit
We map your support volume, top question types, and every system or document the bot will need to read from before writing a line of code.
2. Architecture & Model Selection
We decide API based versus RAG, then pick the right model for the job, weighing GPT-5.5, Claude Opus 4.7, and Gemini 3.1 Pro on cost and accuracy.
3. Build & Integration
Conversation design, the RAG retrieval pipeline, and connections into your website widget, WhatsApp, Slack, or CRM all come together in this stage.
4. Testing & Guardrails
We run the bot against real customer questions, check for hallucinations, and set topic boundaries so it declines gracefully outside its scope.
5. Launch & Monitoring
Once live, we track accuracy, escalation rate, and usage, then tune prompts and retrieval as real conversations reveal edge cases.
How Pricing Works
Every Chatbot Is Quoted After Scoping
Cost depends on whether the bot just chats or answers from your own data with RAG, plus how many channels and integrations it needs. Tell us what you need built, and a senior engineer will scope it and give you a fixed quote.
Get a Quote| Chatbot type | Complexity | Best for |
|---|---|---|
| FAQ / support bot (API based) | Lowest | Answering common questions |
| RAG bot (answers from your data) | Moderate | Knowledge bases, product help |
| Transactional bot | Higher | Booking, orders, account tasks |
| Multi-channel assistant | Highest | Web, WhatsApp, and app in one |
Build vs Buy
Custom Chatbot vs Off-the-Shelf Tools
Tools like Intercom Fin or Zendesk AI work for a generic help widget. A custom build makes sense once your bot needs to reason over your own data or take real actions.
| Factor | Off-the-shelf tool | Custom build (Codioo) |
|---|---|---|
| Setup cost | Low, but capped by the vendor's plan | Higher upfront, no plan ceiling |
| Grounded in your own documents | Limited to what the vendor's connectors support | Full RAG pipeline over any data source |
| Taking real actions (bookings, orders) | Only through pre-built integrations | Any workflow your systems support |
| Data ownership | Lives on the vendor's platform | Stays inside your own infrastructure |
| Ongoing cost as usage grows | Per-seat or per-conversation fees scale up | Model and hosting cost only |
Start Your Chatbot Project
Book a Free Scoping Call
Tell us what your chatbot needs to do. A senior AI engineer will recommend the right architecture and give you a fixed quote within 24 hours.
Architecture Recommendation
RAG or API based, model choice, and integrations
Fixed Quote in 24 Hours
No open ended hourly billing
Related Services
Talk to an AI Engineer
// free scoping call · no commitment
Model selection, connecting your data with RAG, conversation design, integration with your website or apps, accuracy testing, and deployment with monitoring.
Cost depends on whether the bot just answers from an API model or is trained on your own documents with RAG, plus how many channels it needs to support. Share your requirements and we will scope it and quote a fixed price. See our full chatbot guide.
Yes, for any business fielding repeat questions. Conversational AI for customer service answers common questions instantly at any hour and frees staff for harder cases that need a human.
A chatbot that retrieves the right passages from your own documents before answering, so responses are grounded in your real content instead of the model's general training.
2 to 4 weeks for a simple API based bot, and 6 to 12 weeks for a RAG bot trained on your knowledge base.
Yes. We integrate chatbots with your website widget, WhatsApp, Slack, and CRM or help desk systems as part of a standard build.
We pick the model that fits the job rather than defaulting to one, usually GPT-5.5, Claude Opus 4.7, or Gemini 3.1 Pro, weighing accuracy, latency, and cost per conversation.
Whatever your bot should answer from: help center articles, product docs, PDFs, a knowledge base, or database records. We index it into the RAG pipeline during the build stage.
Yes. We monitor accuracy and escalation rates after launch and offer ongoing retainers to retrain, add new data sources, and tune prompts as your product changes.
Ready to Ship a Chatbot That Actually Knows Your Business?
Book a free scoping call with a senior AI engineer. We will recommend the right architecture and give you a fixed quote within 24 hours.