AI Chatbot Development
AI chatbots that resolve tickets, not redirect them.
LLM-powered chatbots with RAG from your knowledge base — live in 2–3 weeks, connected to your CRM, deployed across every channel your customers use.
GPT-4o & Claude 3.5
RAG from your docs
Live in 2–3 weeks
GDPR compliant
Support Bot
Online
Hi! I'm here to help. What can I assist you with today?
How do I downgrade my subscription?
Sure! Go to Settings → Billing → Change Plan, then select your new tier. Changes apply at the next billing cycle — no charges until then. Anything else?
Perfect, found it. Thanks!
Resolved without agent · 12 seconds↑ 73% deflection
200+
Chatbots deployed
89%
Avg. resolution rate
73%
Ticket deflection
40+
Channel & CRM integrations
The Case for AIYour support team handles complexity.
Your support team handles complexity.
Your chatbot handles volume.
73%
of support tickets never need a human
Password resets, order status, billing questions, how-to guides — the same 20 intents make up 80% of your volume. An AI chatbot trained on your docs resolves them instantly, consistently, every time.
Order statusPassword resetBilling FAQHow-to guidesAccount changesPolicy lookups
24 / 7 / 365
Always on — no shifts, no holidays, no queue spikes at 2 AM
< 1 second
vs 2–8 hours average wait with traditional support queues
Capabilities
Everything that makes a chatbot actually work
Answers from your actual docs — not hallucinations
Connect PDFs, Notion, Confluence, Zendesk, or any URL. The bot embeds your content into a vector database and retrieves exact passages before answering. Every response is grounded in your documentation.
PDF · Notion · Confluence · ZendeskAuto re-indexes on updateCites source in response
// User query → vector search → grounded answer
query: "How do I cancel my subscription?"
// Top retrieved chunks
→ help-center.pdf §4.2 [score: 0.94]
→ faq-billing.md §7 [score: 0.87]
// Response grounded in your docs ✓
How We Build
From kickoff to live bot — in weeks, not months
01
Discovery & Knowledge Audit
We map your top support intents — usually 20 topics = 80% of volume — audit your knowledge base quality, and identify integration points with your CRM and channels.
02
Architecture & Flow Design
Conversation flows, escalation rules, persona tone, and RAG retrieval architecture are designed before any code is written. You approve the blueprint before we build.
03
Build, Test & Integrate
Bot trained on your knowledge base, connected to your channels and CRM. Load-tested and validated against real historic tickets before any live user sees it.
04
Launch & Optimise
Production deployment with monitoring dashboards. Weekly performance reviews for the first 30 days — then we hand over a bot that continuously improves from user feedback.
The Numbers
AI chatbot vs traditional support
| Metric | AI Chatbot | Traditional Support |
|---|---|---|
| Response time | < 1 second | 2–8 hours |
| Availability | 24 / 7 / 365 | Business hours only |
| Simultaneous chats | Unlimited | 3–5 per agent |
| Cost per interaction | $0.02–0.05 | $8–15 |
| Answer consistency | ✓ Always identical | ✗ Agent-dependent |
| Scales with volume | ✓ Instant | ✗ Hire more agents |
| Learns from feedback | ✓ Continuous | ✗ Manual retraining |
Client Outcome
The Challenge
B2B SaaS: 1,200+ tickets/day, 2 agents, 6-hour average response time. Team burning out fast.
What We Built
RAG chatbot on GPT-4o connected to Zendesk KB. Graceful escalation with full transcript handoff to human agents.
The Result
- 73% ticket deflection
- 6 hours → 30 seconds response
- CSAT held at 4.6 / 5
6-month ROI
4.1×
return on implementation cost
2 agents now handle 5× the volume
Saved 1,400+ agent-hours per month
Ready to automate your support?
Talk to an engineer this week — not a sales rep.
“Our support volume was crushing the team. The chatbot now handles 73% of tickets autonomously — and users actually prefer it for quick queries. We went from 6-hour response times to instant answers.”
Chatbot DevelopmentBuild a chatbot that
Build a chatbot that
actually resolves
Tell us where you need automation. We will scope an LLM assistant with RAG that understands context and escalates when needed.
Free consultation24-hour responseNDA on request
FAQ
Questions we get all the time
If yours is not here, reach out. We respond within 24 hours with a real answer from an engineer — not a sales pitch.
We're model-agnostic. Most chatbots use GPT-4o or Claude 3.5 Sonnet, chosen based on your latency, cost, and capability needs. For privacy-sensitive deployments we run Llama 3 or Mistral on your own infrastructure — no data leaves your environment.
A focused single-channel chatbot (e.g. web widget with RAG on your docs) goes live in 2–3 weeks. Multi-channel deployments with CRM integration typically take 4–6 weeks. We always ship a working prototype in week one so you can see real progress immediately.
Yes. LLMs natively support 50+ languages. We configure the bot to detect the user's language and respond in kind. If your knowledge base is English-only, we add a translation layer before retrieval — answers still come from your docs, just delivered in the user's language.
We have pre-built integrations for Zendesk, Intercom, Freshdesk, HubSpot, and Salesforce. The bot can read ticket history, look up customer data, and create escalation records directly. Custom integrations via REST API or webhook are built within the same project timeline.
The bot uses a confidence threshold. Below it, the bot acknowledges the limitation clearly and triggers the escalation flow — routing to a human agent with the full conversation context. It never makes up an answer. Fallback rates typically drop from 20%+ at launch to under 5% after the first month of optimisation.
You don't need labelled training data — modern LLM chatbots use RAG, so we point the bot at your existing knowledge base (help-center articles, PDFs, Notion, Zendesk) and it retrieves answers from those documents at query time. Even a few dozen support articles is enough to launch, and the bot improves as you add content and we tune it against your real ticket history.
Build cost is typically $6k–$25k depending on channels, CRM integrations, and knowledge-base complexity. Running costs are low — most LLM-powered chats cost $0.02–$0.05 per conversation versus $8–$15 for a human-handled ticket, so the bot usually pays for itself within the first few months at moderate volume.
Yes. We write the conversation logic once and deploy it across your web widget, WhatsApp, Slack, Teams, Telegram, and email, with channel-aware formatting applied automatically. The bot keeps a consistent persona everywhere and shares the same knowledge base, so customers get the same answer on every channel.