AI chatbots connected to
the systems your team already uses.
Customer support, helpdesk, product, and sales chatbots — grounded in your real documents, integrated into your CRM and helpdesk, and designed to escalate cleanly when a human is the right answer.
One chatbot,
several jobs.
Customer support, employee helpdesk, product assistant, sales qualification, WhatsApp, website widget, and embedded SaaS assistant — each is the same underlying architecture pointed at a different knowledge base and channel.
This page covers conversational, retrieval, and support use cases specifically. If the job is to take an action — issue a refund, update a CRM record, escalate a case — that's AI agent development, and the two are often combined once the conversational layer is proven.
Two categories of buyer show up most: SaaS teams embedding a support or onboarding assistant directly in-product, and telecom and call-center operators replacing a chat-based IVR tier with something that actually resolves the question instead of routing it.
What makes it
reliable, not just fluent.
Routes a question to the right knowledge source or escalation path before generating an answer.
Answers are grounded in your actual documents, with a visible source behind every claim.
Reads order status, account state, or ticket history where relevant — not answering blind.
Tested in the actual conversational patterns of each language, not translated test questions.
Hands off to a human cleanly, with full context, when the question is outside its scope.
Never surfaces information the asking user isn't entitled to see.
From channel
to human handoff.
The same pipeline underlies every channel — what differs is the entry point and the systems it's allowed to read.
Tested against
what it will actually see.
- Retrieval evaluated against real customer questions, not a curated demo set
- Out-of-scope detection, so it recognizes what it shouldn't answer
- Safe refusal instead of a confident guess when it's unsure
- PII handling and redaction before anything is logged or stored
- Transcript controls — retention period and access scoped deliberately
What we need
from your docs.
Less than most teams expect, but the prep step still determines retrieval quality more than any model choice does.
| Source | Prep needed | Typical turnaround |
|---|---|---|
| Existing help center / docs site | Export or crawl; dedupe outdated articles before indexing | 1–3 days |
| PDFs & internal wikis | Parsing (via LlamaParse for complex layouts) and chunking tuned to structure | 3–5 days |
| Scattered / no written docs | A content gap-fill pass with your SMEs before the bot can answer reliably | 1–2 weeks |
| Live systems (order status, account state) | API or database read access — see AI integration for the connection pattern | Depends on API readiness |
Integration examples we build regularly: Zendesk or Intercom for the handoff queue, Shopify or a custom order system for order-status lookups, Salesforce for account and case history, and Slack or Microsoft Teams for internal helpdesk deployments. Each follows the same architecture — the chatbot reads through the existing platform's API rather than requiring a parallel data store.
What you can see
after launch.
Each supported language gets its own retrieval and response eval set — a bot that's accurate in English isn't automatically accurate in Hindi or Spanish idiom.
A dashboard showing containment rate, top unanswered topics, and escalation volume by category — so you can see what to fix next, not just that it's "working."
When a new question pattern starts appearing that the knowledge base doesn't cover yet, it surfaces as a gap to fill — not a silent failure.
What it did
for one retailer.
reduction in support tickets needing a human, after the WhatsApp AI agent took over routine order-status, product, and sales questions.
coverage on messages that previously queued for hours outside business hours, now answered immediately, any time of day.
Picking the right
category.
| Need | Right fit |
|---|---|
| Answer questions from documents or a knowledge base | AI chatbot (this page) |
| Take an action — refund, record update, booking | AI agent |
| Run a fixed, rule-based sequence with no judgment calls | Rule-based automation (often not AI at all) |
| Retrieve and cite from a large internal knowledge base | RAG development |
Timeline,
inputs, and deliverables.
A single-channel support chatbot over existing documentation typically ships in 4–6 weeks. We need your current help content, access to the CRM or helpdesk it should read from, and a point of contact to approve tone and escalation rules. You get the working chatbot, its evaluation results, and full source code and prompts at handoff.
| Stage | Duration | Milestone |
|---|---|---|
| Knowledge-base ingestion & eval-set build | Week 1–2 | Retrieval eval scored against real questions, not yet live |
| Integration & escalation wiring | Week 2–4 | Connected to CRM/helpdesk, handoff path tested |
| Staged rollout | Week 4–6 | Live to a subset of traffic before full rollout |
No sales pitch,
just how we work.
We measure accuracy against real questions, not a cherry-picked demo.
The handoff to a human is part of the architecture from day one.
WhatsApp, your website, your existing CRM — no new platform to adopt.
You own the code and configuration at completion.
Tools we've shipped
in real projects.
Before you
book a call.
How is a chatbot different from an AI agent?
A chatbot answers and retrieves information. An agent takes actions in other systems — issuing a refund, updating a record, booking something. Most projects start as a chatbot and add agent actions once the conversational layer is proven.
How much does chatbot development cost?
Priced by milestone based on channel count, integration complexity, and knowledge-base size. A single-channel support chatbot is a different scope from a multilingual, multi-channel assistant wired into a CRM. Book a call for a specific number.
What knowledge base preparation do we need to do?
Less than most teams expect. We can work from existing help docs, PDFs, and internal wikis — the main prerequisite is that the content is current.
Can it handle multiple languages?
Yes — multilingual support is a configuration choice, not a separate build. We test it against real conversational patterns in each language.
What does it integrate with?
CRM and helpdesk platforms, internal knowledge bases, and messaging channels like WhatsApp and your website widget. See AI integration for the general pattern.
Do we own the chatbot after it's built?
Yes. Source code, prompts, and configuration transfer to you at project completion.
How do we know the chatbot is actually resolving questions, not just responding to them?
Containment rate — the share of conversations resolved without escalation — is the primary metric, reported alongside top unanswered topics so you can see exactly where the knowledge base has a gap, not just that a number went up or down.
Will it work the same in a second language, or do we need a separate build?
Same build, but not automatically the same accuracy. Each language gets its own evaluation set against real conversational phrasing — a bot that's accurate in English isn't guaranteed to be accurate once idiom and phrasing shift in another language, so we test rather than assume.
Tell us what
questions it should answer.
Book a 30-minute call — bring your current help docs and the channel you want to cover first.