I design self-hosted, multi-agent AI ecosystems that automate customer communication, lead capture, booking, voice interaction, moderation and reporting — across Messenger, WhatsApp, Telegram, Email, Voice and e-commerce.
Not limited to one platform, one channel or one tool. I design, build, deploy and maintain custom AI agents for any workflow — from a single chatbot to a coordinated team of agents. n8n is one of the tools I use; the model, framework and channels are chosen for your job.
Answer questions, qualify leads, book calls and hand over to a human — on WhatsApp, Messenger, Telegram, web chat and email, in Bangla or English.
Agents that listen and speak: voice notes, phone-style booking calls, speech-to-text summaries and spoken replies.
Assistants grounded in your own documents, products, SOPs and databases — with sources, and a safe “I don’t know” fallback.
Scheduled agents that browse, compare, extract and report: market, competitor, price and lead research delivered as a daily brief.
Read invoices, forms, IDs, PDFs, spreadsheets and emails; extract, validate and push clean data into Sheets, CRM or accounting.
Code review, bug triage, test generation, uptime and log monitoring — agents that diagnose problems and open the ticket.
Content pipelines, social posting, comment moderation, ad-account audits and SEO reports that run on their own every day.
Orders, invoices, inventory, HR screening, approvals and reminders — the repetitive work your team does by hand today.
Agents that call APIs, read and write inside your apps, fill forms and trigger other systems (REST, webhooks, MCP-style tools).
An orchestrator with specialist agents — researcher, writer, checker, publisher — that plan, debate and merge their results.
Multilingual information, appointment and follow-up agents with strict data control and a human always in the loop.
Describe the job in plain words. If it follows a process, I’ll scope the agent, the stack and the cost with you.
Pick a system to see how it works, step by step.
An end-to-end Messenger AI agent that replies in fluent Bangla, captures leads and hands over to a human when needed.
Three specialised agents with distinct personas — working alone, or together in Council Mode where outputs are merged into one structured answer.
A conversational booking and inquiry agent that checks availability, answers pricing and confirms bookings.
Page automation for customer messages and booking requests with multi-modal understanding.
An AI sales assistant that answers product questions, captures orders and follows up with customers.
A branded Messenger agent for a commercial shoe brand, with Meta Ads campaign structure managed via API / MCP.
An email-ops assistant — a scheduled inbox digest every morning plus on-demand email queries.
I design, build, deploy and maintain every category of AI agent and automation system.
24/7 support, sales and inquiry agents for Messenger, WhatsApp, Telegram, web chat and email — multilingual (incl. Bangla), conversation memory, human-handoff logic and unified-inbox routing.
Orchestrator-led agent teams: specialist sub-agents (email, tasks, calendar, research) coordinated by a master agent; parallel “council” architectures where multiple LLMs deliberate and merge outputs.
Voice-first automation: speech understanding of customer voice messages (Gemini Audio), voice-note-driven workflows and voice-controlled agent interfaces.
Lead capture → intent classification → personalised reply → CRM entry → follow-up sequences; order processing, booking management, invoice and report generation, approval chains.
Company-specific assistants grounded in your own data — Pinecone vector databases, embeddings, retrieval pipelines and context-aware answers for support teams, internal docs and product catalogues.
AI content pipelines, automated posting and comment moderation, ad-campaign structures via Meta Graph API, daily AI analytics reports and email digest automation.
Practical adoption roadmaps for SMEs and agencies: the right LLM stack, self-hosted vs API trade-offs, cost engineering, data privacy and measurable-ROI rollout plans.
Google Drive / Docs automation, PDF pipelines, advanced voice-agent interfaces and deeper multi-agent orchestration.
In Council Mode a single message fans out to specialist agents in parallel — their answers are merged into one structured reply.
Three more production workflows — one that audits ad accounts, one that turns lead forms into qualified conversations, and a WhatsApp agent that understands voice, images and video.
Eight Meta Graph API pulls are merged, analysed by Gemini and delivered as a clear daily & weekly report in your inbox — no dashboards to check.
A new Facebook lead lands in the CRM and gets an instant WhatsApp welcome; the AI agent then chats (text, voice, images, video), qualifies the lead and moves it through the pipeline.
Every message type is converted into text by Gemini, then a business-trained agent answers using a Google Docs knowledge base, remembers each customer’s conversation and logs every chat.
The systems above are live in production. Below are seven more advanced agent architectures — multi-step, multi-agent and human-in-the-loop — that I can design, build and run on the same self-hosted stack.
A team of agents that researches a topic, writes it, fact-checks, designs the visuals and publishes across your channels — with you approving before anything goes live.
Every new lead is enriched, scored, answered within seconds and followed up until it books a call or says no.
One agent for every channel that answers only from your approved knowledge — and hands over to a human, with a full summary, when it isn’t sure.
An agent that takes the call, understands what the caller needs, books the appointment and confirms it by message — day or night.
Documents arrive by email or chat; clean, validated data leaves — and only the real exceptions reach a person.
Scout agents gather the data every morning, an analyst agent compares it, and you get a one-page decision brief instead of 40 open tabs.
Watches your servers and sites, diagnoses what broke, proposes the fix and — after your approval — runs the safe action and writes the incident report.
…and many more, for example
Cut response time, reduce headcount cost and scale without paying per seat or per message.
Answer course and visa questions, qualify students, book counselling calls and capture leads around the clock.
Handle product inquiries, capture orders in chat and follow up — straight into your CRM.
Check availability, quote prices and confirm bookings on WhatsApp, Messenger or your website.
Qualify enquiries, share listings, schedule viewings and route hot leads to agents.
Multilingual information assistants, appointment requests and internal knowledge bots with strict data control.
Automate reporting, comment moderation, content pipelines and ad-campaign setup.
Map your channels, questions, leads and bottlenecks.
Conversation flows, personas, LLM stack and data privacy plan.
n8n workflows, API and CRM integrations, memory and handoff.
Real conversations, edge cases, languages and escalation paths.
Self-hosted launch, monitoring and continuous optimisation.
Yes. I build custom AI agents for any process that can be described — customer support, sales, voice, research, document processing, back-office, marketing, coding and DevOps, and multi-agent teams. n8n is one of the tools I use, not a limit: I choose the model, framework and channels that fit the job.
Yes. Tell me the task your team repeats most. In the free discovery call we turn it into an agent design, the right stack, a timeline and a clear price.
No. n8n is my main tool for fast, self-hosted automation, but I also build custom agents in code (Python / FastAPI), connect any API or webhook, and combine several LLMs when the job needs it.
Your agents run on your own server (a VPS with Docker + n8n) instead of a monthly SaaS platform. You keep control of your data, pay only for hosting and API usage, and avoid per-seat or per-message fees.
Facebook Messenger, WhatsApp, Telegram, web chat, email and voice-note workflows — with a unified inbox and human handoff when a person needs to step in.
Yes. My Messenger agent replies in fluent Bangla, and the agents can work in English and other languages, depending on the LLM stack chosen.
Google Gemini, GPT-4 / 4o, Claude, Groq and OpenRouter — chosen per use case for quality, speed and cost. Company knowledge can be added through Pinecone-based RAG.
A focused single-channel agent can take about one to three weeks. Multi-channel or multi-agent systems take longer — you get a clear timeline after the discovery call.
Yes — monitoring, optimisation and new workflows as an ongoing retainer, plus hosting and server management if you need it.
Let’s build your automated future — starting with a free discovery call about the work you want your AI team to take over.
No commitment · Free discovery call · WorldwideDescribe the channel, the questions and the process — I’ll come back with a plan and an estimate.
Thanks for reaching out — I’ll reply within a few hours.