AI Chatbot Building and Integration
Engineer bespoke conversational interfaces powered by Retrieval-Augmented Generation (RAG), then connect them natively into the channels and systems you already run on (Salesforce, Slack, WhatsApp Business, and your internal data stores) without fragmenting security or context.
What we focus on
From strategy to launch, we help you build AI & Agents solutions that are reliable, scalable, and ready for the future.
- Customer support
- Internal knowledge base
- Sales qualification
- WhatsApp sales automation
GPT-4o
Claude 3.5
Google Gemini
Pinecone
LangSmith
Vercel AI SDK
Twilio
WhatsApp Business API
Slack Bolt
HubSpot API
Make.com
MongoDB
Azure OpenAI ServiceWe build systems that scale.
AI Chatbot Building and Integration covers both halves of a working conversational assistant: designing a Retrieval-Augmented Generation (RAG) pipeline grounded in your own documents and policies, then wiring that assistant into the channels and systems your business runs on. Each engagement ships as ten concrete deliverables, from the RAG pipeline and a deployed chat interface through WhatsApp Business API setup, bidirectional CRM sync, and a Slack Bolt app for handoff. Content is chunked, embedded, and indexed in a vector store so the bot answers from your own knowledge instead of a general-purpose model's defaults, and every response can surface the source passage it was grounded in. One shared session and identity layer keeps a customer recognized whether they start on the web widget, move to WhatsApp, or land in Slack, so context survives the switch. A LangSmith evaluation suite tracks retrieval quality and hallucination rate against a curated question set, so quality is measured before and after every prompt or content change.
Everything in one place
- A working RAG pipeline (ingestion, chunking, embedding, and retrieval) wired to a vector index in Pinecone or Weaviate, with the model layer pointed at GPT-4o or Claude 3.5 per your accuracy and cost preference
- A deployed chat interface: an embeddable web widget built on the Vercel AI SDK for production, plus a Streamlit or Chainlit app for internal review and stakeholder demos
- Source-citation behavior so each answer can surface the documents or passages it was grounded in, with a configured fallback for out-of-scope questions
- A content ingestion process (manual upload, scheduled sync, or API hook) so your knowledge base can be refreshed without rebuilding the bot
- A LangSmith evaluation suite: a curated question set with traces, retrieval-quality scoring, and hallucination checks you can re-run after any content or prompt change
- A unified conversation router that keeps one session and one user identity across web, WhatsApp, Slack, and any other connected channel, so context survives channel switches and human handoffs
- WhatsApp Business API setup via Twilio: number provisioning, approved message templates for outbound/session messaging, and webhook handling for the 24-hour customer-care window
- Bidirectional CRM sync (HubSpot API or Salesforce) that resolves the contact, logs the transcript, updates deal/ticket fields, and triggers lead-routing rules from conversation outcomes
- A Slack Bolt app for internal use and live-agent handoff, plus webhook and middleware code (or documented Make.com/Zapier/Voiceflow flows) with secret management, signature verification, and retry/dead-letter handling
- Documentation covering prompt design, retrieval configuration, channel runbooks, API-scope inventory, and the fallback/escalation map for every connected system
Ready to get started?
Book a free scoping call and we'll map AI Chatbot Building and Integration to your workflow.
- A 30-minute call with a senior engineer, not a salesperson
- A tailored rollout plan scoped to your existing stack
- A straight answer on timeline and cost, no pressure to commit
A clear process. Predictable results.
Corpus, channel, and CRM scoping
We inventory the content the bot will draw on (help center, PDFs, wikis, product data) and map every channel you want it deployed to (web, WhatsApp, Slack, others), auditing your CRM schema and API scopes in HubSpot or Salesforce. This sets the boundaries of what the bot should answer and where it needs to read and write.
Ingestion and indexing
We build the ingestion pipeline: parsing sources, chunking them sensibly, generating embeddings, and loading them into a Pinecone or Weaviate index. Chunking strategy and metadata are tuned here because they largely determine retrieval quality downstream.
Retrieval and prompt engineering
We assemble the RAG loop (retrieval, re-ranking where needed, and a system prompt that instructs the model how to use retrieved context, cite sources, and handle uncertainty), comparing GPT-4o and Claude 3.5 on your content to pick the model that fits your accuracy, tone, and cost constraints.
Identity, session, and channel wiring
We design how a user is resolved across channels (phone number on WhatsApp, Slack user ID, web cookie/session) into one canonical contact, then connect each transport (Twilio plus the WhatsApp Business API, Slack Bolt, the web widget, and bidirectional HubSpot/Salesforce sync) so context and conversation state persist across channel switches and human handoffs.
Evaluation and sandbox testing
Using LangSmith, we run the bot against a curated set of representative questions, inspecting traces for retrieval misses and hallucinations, while testing every channel in sandbox (WhatsApp template rejection, the 24-hour session window, webhook retries, and CRM field-mapping edge cases) before any production traffic.
Deployment, rollout, and monitoring
We ship the production interface via the Vercel AI SDK, launch channels one at a time, and watch delivery success rates, retrieval quality, webhook failures, and CRM write errors. You receive the runbook, evaluation suite, and dashboards so your team can monitor and extend the system.
Corpus, channel, and CRM scoping
We inventory the content the bot will draw on (help center, PDFs, wikis, product data) and map every channel you want it deployed to (web, WhatsApp, Slack, others), auditing your CRM schema and API scopes in HubSpot or Salesforce. This sets the boundaries of what the bot should answer and where it needs to read and write.
Ingestion and indexing
We build the ingestion pipeline: parsing sources, chunking them sensibly, generating embeddings, and loading them into a Pinecone or Weaviate index. Chunking strategy and metadata are tuned here because they largely determine retrieval quality downstream.
Retrieval and prompt engineering
We assemble the RAG loop (retrieval, re-ranking where needed, and a system prompt that instructs the model how to use retrieved context, cite sources, and handle uncertainty), comparing GPT-4o and Claude 3.5 on your content to pick the model that fits your accuracy, tone, and cost constraints.
Identity, session, and channel wiring
We design how a user is resolved across channels (phone number on WhatsApp, Slack user ID, web cookie/session) into one canonical contact, then connect each transport (Twilio plus the WhatsApp Business API, Slack Bolt, the web widget, and bidirectional HubSpot/Salesforce sync) so context and conversation state persist across channel switches and human handoffs.
Evaluation and sandbox testing
Using LangSmith, we run the bot against a curated set of representative questions, inspecting traces for retrieval misses and hallucinations, while testing every channel in sandbox (WhatsApp template rejection, the 24-hour session window, webhook retries, and CRM field-mapping edge cases) before any production traffic.
Deployment, rollout, and monitoring
We ship the production interface via the Vercel AI SDK, launch channels one at a time, and watch delivery success rates, retrieval quality, webhook failures, and CRM write errors. You receive the runbook, evaluation suite, and dashboards so your team can monitor and extend the system.
Outcomes you can count on.
Concrete gains AI Chatbot Building and Integration delivers for your operations, measured in hours saved, errors removed, and cost avoided, not vague promises. Each outcome below maps to a specific capability you can trace end-to-end, so the impact is visible from day one.
Answers grounded in your content, not the model’s guesses
Because responses are retrieved from your own documents before generation, the bot answers from your policies, pricing, and product details rather than improvising. Citations let users and your team verify where each answer came from.
One assistant, every channel
The same agent answers on your website, WhatsApp, and Slack while sharing a single session. A customer who opens a WhatsApp thread, then returns via the web widget, is recognised and does not repeat themselves.
Stays current without retraining
Updating the bot’s knowledge means re-indexing content, not retraining a model. New help articles, updated policies, or fresh catalog entries flow into the vector store and are usable on the next query, so the bot does not go stale between releases.
CRM stays the source of truth
Conversations resolve against live HubSpot or Salesforce records and write outcomes back automatically, so sales sees synced leads and support sees updated tickets without manual copy-paste between the bot and your systems.
Measured quality instead of vibes
Every build ships with a LangSmith evaluation set, so retrieval relevance, grounding, and latency are tracked against real questions. When you change a prompt or swap GPT-4o for Claude 3.5, you can see the effect on answer quality before it reaches users.
Clean handoff to humans
When the bot hits its boundary or detects a high-value or frustrated user, it escalates into Slack or your helpdesk (Intercom) with the full transcript attached, so your team resumes mid-conversation instead of starting over.
