Chatbot platforms like Tidio, Intercom Fin, and Ada give you generic AI on a template. Codeora Vision does custom AI chatbot development, training each bot on your catalog, your docs, and your brand voice. Every build is RAG-powered, so it cites sources instead of hallucinating, integrates with Gorgias and Zendesk, and targets 80%+ autonomous resolution, agent-native, not retrofitted.
Trusted by a DTC subscription brand, a multi-location healthcare group, and a SaaS support team.
Updated June 7, 2026
YOU
What's your return policy on subscription orders?
AI · CITED
Subscription orders can be returned within 30 days of delivery. Cancellations skip the next renewal, not past charges.
TEST IT YOURSELF
This is a real chatbot, not a video. Ask it something. Watch it answer from a knowledge base and cite where the answer came from.
Hi — I'm a demo chatbot trained on a sample of Codeora Vision's documentation. Ask me anything, or tap a suggested prompt below.
This demo is trained on a sample of our own documentation, with source citations on. Your chatbot is trained on your catalog, docs, and tickets, with the same anti-hallucination guardrails.
WHAT IT IS
Custom AI chatbot development is building a conversational system trained on your own content, not configuring a generic SaaS widget. It pairs an LLM with retrieval-augmented generation, so the bot answers from your catalog and documentation and cites the source. Codeora Vision builds these on Claude and GPT-4o, integrated with your helpdesk and CRM.
LLM (the large language model that understands and writes the reply) · retrieval-augmented generation (RAG, where the bot looks up your documents before answering) · hallucination (an AI confidently making up an answer it cannot back up).
Per Klarna's published 2024 AI benchmark, its assistant handled two-thirds of service chats and resolved 80%+ autonomously.
Buyer translation — That is the bar a custom build aims at. A bot trained on your real content, citing sources, resolves most tickets without a human and escalates the rest cleanly.
BUILD VS BUY
SaaS chatbots are fast to switch on and fine for simple FAQs. The ceiling is accuracy and fit. They answer from a generic model and shallow connectors, so they hedge, miss your edge cases, and cannot reach your real systems. A custom build trades setup time for accuracy you can trust.
We do not build flow-only bots on a drag-and-drop builder, and a custom GPT is not a production system. If a no-code chatbot builder solves your problem, use one. When accuracy, integration, and ownership matter, that is when custom AI chatbot development earns its cost.
WHAT WE BUILD
Four shapes cover most of what businesses ask for. Each one is the same RAG core, trained on different content and wired to different tools.
Resolves tickets, answers FAQs, checks order status, and triages the rest to a human with context.
Gorgias · Zendesk
Qualifies website visitors, recommends products, and books the meeting before they leave.
HubSpot · Salesforce
Answers staff questions from policy and product docs, and runs onboarding flows.
Notion · Slack
Answers technical and product questions from your docs, citing the exact page.
Semantic search
The credible bar is Klarna's published 2024 AI benchmark at 80%+ autonomous resolution. Platform vendors report their own rates, self-reported (Intercom Fin around 70%, Ada around 60%). We measure resolution on your real conversations, not a vendor's slide.
START WITH ONE
Support tickets, lost sales chats, or repeated internal questions. Tell us where the volume is, and in a 30-minute review we will map a chatbot that resolves it, what it trains on, and what it integrates with. NDA from the first minute.
HOW IT WORKS
A RAG chatbot answers in four moves: ingest, retrieve, reason, and improve. Codeora Vision indexes your content in a vector database, then at question time retrieves the right passages and reasons over them with Claude or GPT-4o, orchestrated on LangGraph and MCP. Because it is agent-native, it cites sources and calls tools.
We chunk your catalog, docs, and tickets, generate embeddings with OpenAI or Voyage AI, and index them in Pinecone, Weaviate, Qdrant, or Supabase pgvector.
On each question, the bot runs semantic and hybrid search, re-ranks the matches, and pulls the most relevant passages, with the source attached.
LangGraph, MCP, and Claude (or GPT-4o) reason over the retrieved context, use function calling to check an order or book a slot, and reply with the citation.
We score answers with LangSmith and Langfuse, catch failures, and retrain on real conversations, so resolution climbs over time instead of drifting.
The retrieval layer is a discipline of its own. For a large or sensitive knowledge base, see our private RAG for chatbot knowledge base work.
INTEGRATIONS
A chatbot that cannot reach your systems just deflects. We connect it to the helpdesk where your tickets live and the CRM where your customers live, then write back to both.
HELPDESK
CRM
KNOWLEDGE & DOCS
AUTOMATION & SYNC
Need the chatbot to trigger real back-office workflows, not just reply? That is our chatbot to helpdesk integration work.
WHERE IT LIVES
We build the brain once, then deploy it where your audience already is. The same trained chatbot runs across web and messaging.
COMPLIANCE
A chatbot that talks to customers is an attack surface and a privacy obligation. We design for both before launch, not after the first incident.
GDPR · CCPA
Lawful data handling, retention limits, and deletion on request for EU, UK, and California users.
HIPAA · SOC 2
PHI-safe builds with BAAs for healthcare, designed for SOC 2 access and audit controls.
PII redaction
Sensitive data masked in logs and before it reaches the model.
Prompt injection prevention
Input and output filtering to stop jailbreaks and data exfiltration attempts.
Data residency
Hosting and storage scoped to the region your policy requires.
WHERE IT WORKS
A chatbot is a horizontal capability. The wedge is the same, the content and rules change per industry. These two verticals run it hardest, and the page for each goes deeper.
A subscription brand cut repeat "where is my order" tickets and resolved most support chats without a human.
A brokerage answers listing inquiries instantly and qualifies website visitors before a rep ever replies.
We also build patient-triage chatbots for healthcare, intake chatbots for law firms, and support copilots for SaaS teams. Same engine, different knowledge base and compliance scope.
TRANSPARENT PRICING
Most agencies hide chatbot pricing behind a call. We publish the range. Build cost scales with how many flows, knowledge sources, and channels you need. The monthly retainer is optional, for monitoring and retraining.
One RAG-powered support or sales chatbot, one knowledge base, one channel, helpdesk integration
OPTIONAL $1,500/MO
Scope this buildSupport plus sales and internal flows, helpdesk and CRM integration, evaluation dashboard
OPTIONAL $2,500/MO
Scope this buildWebsite, app, and messaging channels, multi-source RAG, continuous training and tuning
OPTIONAL $3,000/MO
Talk to an architectThe retainer is optional, not a lock-in. Some clients keep us on to monitor and retrain. Others take the chatbot in-house after launch. The build price is published here either way.
SEE IT ON YOUR DOCS
Send us a sample of your docs or help center. In a few days we will train a demo chatbot on it and show you, live, how it answers your real questions and cites your real pages. Then you decide.
PROVEN
Tickets resolved without a human
BEFORE
0
AFTER
82%
"Where is my order" volume
BEFORE
baseline
AFTER
down 73%
First-response time
BEFORE
minutes-hours
AFTER
instant
A subscription brand in the $15M to $25M ARR range was burning a BPO team on repetitive support. We built a custom RAG chatbot on Claude and LangGraph, indexed in Pinecone, integrated to Gorgias and Shopify, with source citations on. Within 90 days it resolved 82% of chats autonomously and cut repeat order-status tickets by 73%. Edge cases route to a human with full context. Single anonymized deployment, not a guarantee.
FAQ
AI chatbot development is building a custom conversational system that understands natural language and answers from your own knowledge, not configuring a generic SaaS widget. A modern build pairs an LLM like Claude or GPT-4o with RAG, so the bot looks up your catalog and docs before answering and cites the source. Codeora Vision trains each one on your data and integrates it with helpdesks like Gorgias and Zendesk.
Builds run from $15,000 to $50,000, plus an optional $1,500 to $3,000 per month retainer for monitoring and retraining. A single RAG support chatbot on one channel sits at the low end. A multi-channel program drawing on several knowledge sources sits at the top. The retainer is optional, because some teams run the chatbot themselves after launch.
A rule-based chatbot follows decision trees and keyword triggers. Tools like ManyChat, Chatfuel, and Tidio rule-trees work this way, and they break the moment a question goes off-script. An AI chatbot uses an LLM to understand intent in plain language and, with RAG, answers from your real documentation and cites it. Codeora Vision builds the AI kind on Claude and GPT-4o.
A chatbot converses: it answers, qualifies, and books. An AI agent acts: it runs multi-step tasks, uses tools through function calling, and completes workflows with little supervision. The line blurs, since a good chatbot already calls tools to check an order. The difference is autonomy and scope. We build chatbots on LangChain and LangGraph, and the same patterns extend into deeper agent work.
We deliver a working demo, trained on a sample of your documents, in a few days, so you can test it first. A full build, with the knowledge base ingested, helpdesk and CRM integrated, channels deployed, and evaluation in place, typically goes live in three to six weeks. Multi-channel programs drawing on several sources run a little longer.
Yes, that is core to the build. We integrate with CRMs like HubSpot and Salesforce and helpdesks like Zendesk and Intercom through their REST API, GraphQL, webhooks, and OAuth. For multi-step syncs we use n8n, Make, or Zapier. The chatbot can read a record, log a conversation, create a ticket, or update a deal, then hand off to a human with context.
The strongest builds run on Claude Sonnet or Claude Opus and GPT-4o, with Gemini, Llama, Mistral, and DeepSeek where cost, speed, or self-hosting matter. Codeora Vision picks the model per use case instead of defaulting to one, and uses function calling and structured outputs for clean results. The model is half the system. Retrieval and integrations decide whether answers are accurate.
If the chatbot must answer from your specific content, yes. RAG means the bot searches your documents in a vector database, retrieves the right passages, and answers from them with a citation, instead of guessing. It is the difference between a bot that hallucinates and one you trust on a ticket. We build retrieval on Pinecone, Weaviate, or Supabase pgvector with re-ranking.
DALLAS, TEXAS — RESPONDING IN < 4 HOURS
Send us a sample of your docs and help center. We will train a custom chatbot on it, on RAG with Claude, show you the live answers and the source citations, and scope the full build. You see it work before you commit.