AI service · AI Chatbots
AI Chatbot Development
We build AI chatbots that customers thank instead of rage-clicking past — assistants grounded in your documentation and live data that answer accurately, check order status, process routine requests, and escalate to your team with full context when it matters. Deployed on web, in-app, SMS, and WhatsApp.
Free scoping call · Clear ROI plan before any commitment
The challenge
Everyone has met the bad chatbot: a keyword-matching decision tree that loops, misunderstands, and buries 'talk to a human.' It deflects tickets by exhausting customers, and it costs goodwill with every interaction. Meanwhile support queues grow, first-response times stretch, and your team answers the same twenty questions on repeat.
How we solve it
We build LLM-native chatbots grounded in your help center, product data, and policies — so they understand messy, misspelled, multi-part questions and answer specifically, not generically. Beyond answering, they act: order lookups, returns, plan changes, appointment booking, all via governed API integrations. We measure what support leaders care about — resolution rate, CSAT, escalation quality — and tune against real conversation logs after launch.
Capabilities
What we deliver
The building blocks of a production-grade ai chatbots engagement.
Customer support chatbots
Grounded assistants that resolve the repetitive majority of tickets accurately, with instant, context-rich handoff into Zendesk, Intercom, or your helpdesk for the rest.
Sales & lead-gen chat
Conversational assistants that answer product questions, recommend options, capture qualified leads, and book demos — turning site traffic into pipeline while you sleep.
Action-taking integrations
Chatbots that do things: check order status, initiate returns, update subscriptions, and book appointments through secure, permissioned API calls.
Multi-channel deployment
One assistant, consistent everywhere — website widget, in-app, SMS, WhatsApp, and Messenger — with conversation history that follows the customer across channels.
Conversation analytics & tuning
Dashboards for resolution rate, CSAT, escalations, and unanswered-question clusters — the feedback loop that makes the bot measurably better every month.
How we work
A clear path to production
Five stages, each with visible output — you're never waiting on a black box.
Discovery & scoping
We map the problem, success metrics, constraints, and existing systems before writing code. You leave with a clear scope, timeline, and a fixed view of what 'done' means.
Architecture & design
We design the system end to end — data model, integrations, security, and a path to scale — and validate it against your real workloads, not a demo.
Iterative delivery
We ship in short, reviewable increments. You see working software every sprint, give feedback early, and never wait months to find out it missed the mark.
Hardening & launch
Testing, observability, performance, and security are built in, not bolted on. We launch with monitoring in place and a rollback plan ready.
Support & iteration
After launch we stay on — measuring outcomes, fixing fast, and iterating on what the data tells us actually moves the metric.
Representative stack
- Claude (Anthropic) / OpenAI
- RAG over your help content
- Zendesk / Intercom / HubSpot
- WhatsApp / Twilio / web SDK
- Vercel AI SDK
- TypeScript
Where it applies
AI Chatbots in the industries we serve
Vertical context changes what good looks like — see how this capability lands in your space.
E-commerce
High-performance commerce, headless storefronts, and AI personalization that lift conversion.
Explore E-commerceEducation
LMS, course platforms, and AI tutoring built for engagement, accessibility, and scale.
Explore EducationFintech
Payments, lending, and financial platforms built for security, reliability, and compliance.
Explore FintechAnswers
Frequently asked questions
How is a modern AI chatbot different from the chatbots customers hate?
Legacy bots match keywords against scripted flows, so anything off-script fails. LLM-native bots understand intent in natural language, ground answers in your actual content, handle multi-part questions, and know when to hand off. The practical difference shows up in resolution rate and CSAT — which we measure from day one.
What share of support tickets can a chatbot realistically resolve?
It depends on your ticket mix, which is why we start by analyzing your historical conversations rather than promising a number. Repetitive, information-seeking, and status-check tickets — often the majority — are strong automation candidates; complex judgment calls stay with your team, reached faster because the queue is shorter.
Can the chatbot access live customer and order data?
Yes, through authenticated, scoped API integrations — the bot can look up a verified customer's orders or subscription and act within limits you define. Sensitive actions can require step-up verification or route to staff.
How quickly can we launch, and what does maintenance look like?
A grounded support bot on one channel typically launches in 4–8 weeks including integration and testing. After launch, monthly tuning reviews the conversations the bot handled poorly, expands coverage, and keeps content synced — that loop is where good bots become great ones.
Ready to build with AI Chatbots?
Book a free consultation and we'll map the fastest path to a working system — with the metric that proves it.