Chat with us!We developed a generative AI chatbot that holds natural, context-aware conversations across a SaaS product — answering product questions, guiding onboardin...
Our users finally get instant, accurate answers — and our docs are doing the work they were always meant to.
Head of Product
We developed a generative AI chatbot that holds natural, context-aware conversations across a SaaS product — answering product questions, guiding onboarding, and surfacing the right docs at the right moment, all grounded in the client's own content.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | SaaS |
| Technologies | OpenAI / Anthropic LLM, LangChain, pgvector, React, Node.js, Redis |
| Delivery Partner | mTouch Labs |
| Primary Outcome | The chatbot became the primary self-serve channel, deflecting routine questions and measurably smoothing onboarding. |
The product had rich documentation but users could not find answers, leading to abandoned trials and a flood of "how do I" tickets. A scripted FAQ widget felt robotic and frequently sent users in circles.
We combined a large language model with retrieval-augmented generation so the chatbot answers from the client's live docs and changelog. Conversation memory and user metadata let it tailor responses to each account's context.
The chatbot ships as an embeddable widget with streaming responses, source citations, and a feedback loop that continuously improves retrieval quality.
Natural-language understanding and generation
Retrieval pipelines and prompt orchestration
Embedding storage and similarity search
Embeddable streaming chat widget
Streaming API and retrieval service
Conversation memory and caching
Mapped and cleaned all docs, changelog, and macros for ingestion.
Built an automated re-indexing pipeline triggered on content changes.
Crafted a helpful brand voice with strict grounding rules.
Built the streaming, themeable, embeddable front end.
Ran answer-quality evals and tuned retrieval thresholds.
The chatbot became the primary self-serve channel, deflecting routine questions and measurably smoothing onboarding.
A grounded generative AI chatbot turned static documentation into an interactive guide, lifting conversion while cutting support load.
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View case studyWhat businesses ask us most often about this project and how we built it.
A generative chatbot understands natural language and composes original, context-aware answers grounded in your content, whereas a rule-based bot can only follow pre-scripted flows.
No. Retrieval-augmented generation forces the bot to answer from your real documentation and cite sources, and it declines gracefully when it lacks grounding.
Yes. It ships as a lightweight, themeable widget that drops into any web app or marketing site.
An automated re-indexing pipeline updates the knowledge base whenever your docs or changelog change.
We are model-agnostic and select the best fit (OpenAI, Anthropic, or open models) based on quality, latency, and cost for your use case.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
