Our Services
AI Chatbots
& Assistants
Custom conversational AI powered by the latest language models. From customer support bots to internal knowledge assistants, we build chatbots that actually understand your business.
Why AI Chatbots
Transform customer interactions
24/7
Always available
24/7 Customer Support
Never miss a customer inquiry. AI chatbots handle conversations around the clock, delivering instant responses in any timezone.
<2s
Average response time
Instant Response Times
Eliminate wait times. Customers get answers in seconds, not minutes or hours, dramatically improving satisfaction scores.
10K+
Concurrent chats
Handle Volume at Scale
One chatbot can manage thousands of simultaneous conversations. Scale your support without scaling your team.
95%
Resolution accuracy
Continuous Learning
Our chatbots learn from every interaction, getting smarter and more accurate over time with built-in feedback loops.
What We Build
Chatbot capabilities
Customer Support Bots
Handle FAQs, troubleshoot issues, process returns, and escalate complex cases to human agents seamlessly.
Sales & Lead Qualification
Engage website visitors, qualify leads with intelligent questions, and book meetings directly on your calendar.
Internal Knowledge Assistants
Give your team instant access to company knowledge bases, SOPs, and documentation through conversational AI.
Voice-Enabled Agents
Deploy AI assistants that understand and respond with natural speech for phone support and voice interfaces.
Multi-Channel Deployment
One bot, everywhere. Deploy across your website, Slack, WhatsApp, SMS, and social media from a single codebase.
Custom LLM Integration
We integrate the best model for your use case — GPT-4, Claude, Llama, or fine-tuned models trained on your data.
Under the Hood
How we build AI chatbots
Every chatbot we build follows a 6-layer architecture designed for accuracy, reliability, and seamless integration with your existing systems.
Conversation Layer
The user-facing interface
This is where your customers interact with the chatbot. We build custom chat widgets, voice interfaces, and messaging integrations that feel native to each platform. The conversation layer manages the UI, typing indicators, message history, rich media (images, cards, carousels), and accessibility.
Understanding Layer (NLU)
Making sense of what users say
The brain of the chatbot. We use a combination of large language models and custom classifiers to understand user intent, extract entities (names, dates, product IDs), detect sentiment, and determine the best course of action — even when users phrase things in unexpected ways.
Knowledge Layer (RAG)
Grounding responses in your data
Retrieval-Augmented Generation (RAG) is how we make chatbots accurate and trustworthy. We ingest your knowledge base, FAQs, documentation, and product data into a vector database. When a user asks a question, the system retrieves the most relevant information and uses it to generate a grounded, accurate response — not a hallucinated guess.
Conversation Manager
Orchestrating multi-turn dialogues
Real conversations aren't single-turn Q&A. The conversation manager maintains state across messages, handles follow-up questions, manages context windows, and orchestrates complex multi-step workflows like booking appointments, processing returns, or collecting information across multiple exchanges.
Action Layer
Connecting to your systems
Chatbots that can only answer questions are limited. Our action layer lets the bot actually do things — look up orders, reset passwords, create tickets, schedule meetings, process payments, and trigger workflows in your existing tools via API integrations.
Safety & Escalation Layer
Keeping conversations on track
We build guardrails that prevent your chatbot from going off-script, sharing inaccurate information, or handling situations it shouldn't. When the bot reaches its limits, it escalates smoothly to a human agent with full conversation context — no cold transfers, no lost information.
Core Technology
RAG: The secret to accurate AI
Why RAG matters
Large language models are powerful, but they hallucinate — they make things up when they don't know the answer. RAG solves this by grounding every response in your actual data. The chatbot only answers from verified sources, and when it can't find relevant information, it says so honestly instead of guessing.
How our RAG pipeline works
1) Your documents are split into semantic chunks. 2) Each chunk is converted into a vector embedding that captures its meaning. 3) Embeddings are stored in a vector database for lightning-fast similarity search. 4) When a user asks a question, we find the most relevant chunks. 5) Those chunks are passed to the LLM as context to generate an accurate, grounded response with source citations.
What makes our RAG different
We don't just use off-the-shelf RAG. We optimize every step: custom chunking strategies per content type, hybrid search combining semantic and keyword matching, re-ranking models for relevance, and automatic freshness detection so the chatbot always uses the latest version of your docs.
RAG Pipeline Flow
Our Process
From concept to production
A typical chatbot project takes 8-12 weeks from kickoff to launch. Here's exactly what happens at each stage.
Week 1-2
Discovery & Strategy
We start by understanding your business, your customers, and your goals. We audit your existing support data, map conversation flows, and identify the highest-impact use cases for AI.
Week 3-4
Conversation Design
We design the chatbot's personality, tone of voice, and conversation flows. This isn't just UX — it's crafting how your brand sounds in every interaction, including edge cases and error states.
Week 4-6
Knowledge Base Engineering
We build the RAG pipeline that gives your chatbot accurate, up-to-date answers. This involves ingesting, cleaning, chunking, and embedding your documentation — then fine-tuning retrieval for maximum accuracy.
Week 5-8
Core Development
We build the chatbot engine — NLU pipeline, conversation manager, action integrations, and safety guardrails. Development happens in agile sprints with weekly demos so you see progress in real time.
Week 8-10
Training & Testing
We train the chatbot on real conversation data, run it through hundreds of test scenarios, and stress-test edge cases. We use a combination of automated testing and human evaluation to ensure quality.
Week 10-12+
Launch & Continuous Improvement
We deploy with monitoring, analytics, and feedback loops built in. Post-launch, we analyze real conversations to identify improvement areas and push updates — your chatbot gets smarter every week.
Packages
Choose your chatbot tier
Starter Bot
Perfect for small businesses that need a smart FAQ bot to handle common customer questions.
What's included
- Up to 500 knowledge base articles
- Website chat widget
- Basic conversation analytics
- Email escalation to human agents
- 5 custom conversation flows
- Monthly performance report
Business Bot
For growing companies that need multi-channel support with deeper integrations and customization.
What's included
- Unlimited knowledge base articles
- Website, Slack, and WhatsApp channels
- CRM and ticketing integration
- Advanced analytics dashboard
- Custom conversation flows (unlimited)
- Weekly optimization updates
- Voice-enabled option
- Multi-language support
Enterprise Bot
Full-scale AI assistant platform with custom models, advanced security, and dedicated support.
What's included
- Everything in Business Bot
- Custom fine-tuned LLM
- On-premise or private cloud deployment
- SSO and enterprise security
- Custom API and webhook integrations
- Dedicated success manager
- SLA-backed uptime guarantee
- Quarterly strategy reviews
FAQ
Common questions
How long does it take to build a chatbot?
A basic FAQ chatbot can be live in 4-6 weeks. A full-featured multi-channel assistant with CRM integrations and custom conversation flows typically takes 8-12 weeks. Enterprise deployments with custom models may take 12-16 weeks.
Will the chatbot understand my industry-specific terminology?
Yes. We train the chatbot on your specific documentation, support tickets, and industry terminology. For highly specialized domains, we can fine-tune the underlying LLM on your data for even better understanding.
What happens when the chatbot can't answer a question?
We build confidence scoring into every response. When the bot isn't sure, it either asks a clarifying question or smoothly escalates to a human agent with full conversation context. Your agents never get a cold transfer — they see everything the customer discussed.
Can the chatbot take actions, or just answer questions?
Both. Our chatbots integrate with your existing tools to perform real actions — look up orders, reset passwords, book appointments, create support tickets, process returns, and trigger workflows in your CRM or backend systems.
How do you prevent the chatbot from giving wrong answers?
Three layers of protection: 1) RAG grounding ensures responses come from verified sources. 2) Confidence thresholds trigger escalation when the bot is uncertain. 3) Topic boundaries prevent the bot from discussing subjects outside its scope. We also run ongoing monitoring to catch and correct any issues.
Can we update the knowledge base ourselves?
Absolutely. We set up automated ingestion pipelines that sync with your documentation sources (Notion, Confluence, Google Docs, your help center). When you update a doc, the chatbot's knowledge updates automatically — usually within minutes.
What about data privacy and security?
We take security seriously. All data is encrypted in transit and at rest. We support on-premise and private cloud deployment for enterprise clients. PII detection automatically redacts sensitive information. We're happy to work within your compliance framework (SOC 2, HIPAA, GDPR).
Technology