Multimodal WhatsApp Assistant, Text, Voice, Image & PDF
A WhatsApp assistant that accepts text, voice notes, images and PDF documents in one thread, with conversation memory that survives between sessions.
Assistants grounded in real content and wired into real systems — each one measured on what it resolved, not on how many conversations it started.
Selected work
Assistants that answer on the channel the customer already uses — grounded in real content, and handing over cleanly when they should.
A WhatsApp assistant that accepts text, voice notes, images and PDF documents in one thread, with conversation memory that survives between sessions.
A support bot answering strictly from a Google Docs knowledge base, so the team updates a document rather than filing a ticket with engineering.
A retrieval-backed WhatsApp assistant using a Pinecone vector store, so answers cite real documentation instead of improvising around it.
A restaurant assistant handling menu questions, table enquiries and order taking on WhatsApp, escalating to staff when a human is genuinely needed.
A dental practice assistant that reads real calendar availability, books and reschedules appointments, and confirms without a receptionist in the loop.
A conversational capture flow that qualifies visitors through natural dialogue and writes structured, sales-ready rows straight into Google Sheets.
A community bot answering member questions from server knowledge, surfacing the threads that need a moderator and keeping channels on topic.
A selection of chatbots work. Detailed case studies, metrics and references are shared on request.
Methodology
The methodology behind every engagement, from first workshop to the retainer that follows launch.
We read your real tickets, chats and calls, cluster them by intent and volume, and agree which clusters the assistant should own on day one.
Content is gathered, cleaned, chunked and indexed — with a gap list where a question has no approved answer anywhere yet.
Topic boundaries, refusal behaviour, escalation triggers and voice agreed and written into both the prompt and the evaluation set.
The assistant is connected to the systems it needs to read — orders, accounts, CRM — with every retrieval and tool call logged.
Scored against a held-out set of real questions with known correct answers, before a single customer sees it.
Staged rollout behind a percentage of traffic, then weekly review of unanswered questions and escalation reasons.
Common challenges
The problems clients usually arrive with — and how each one gets handled.
Answers are retrieved from your approved content before anything is written, each one carries its source, and when retrieval finds nothing the assistant says so and offers a person.
The question audit tells you that early. The gap list — the questions customers ask that nobody has written an answer to — is usually worth having on its own.
They hate bots that trap them. Handover is one step, triggers on frustration and repeated failure, and carries the transcript so nobody repeats themselves.
Detection on the way in, translated retrieval, and a deliberate restriction to the languages your team can actually back up when a conversation escalates.
Deflection, containment, satisfaction and unanswered questions are reported weekly — with the transcripts behind each number available to read.
Send us a month of transcripts. We will tell you what proportion an assistant should handle, what it must never touch, and what it costs to build.