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Custom LLM Chatbot · Dhaka

Custom LLM Chatbot Development in Dhaka

ঢাকায় কাস্টম LLM চ্যাটবট ডেভেলপমেন্ট

RAG grounded ✓

বাংলা + ইংরেজি

A GPT-style assistant trained on your own catalogue, FAQs and policies — deployed on your site and WhatsApp in 3–4 weeks with proper RAG grounding, not a generic ChatGPT wrapper.

আপনার নিজস্ব ক্যাটালগ, FAQ ও পলিসিতে ট্রেইন করা GPT-স্টাইল অ্যাসিস্ট্যান্ট — ৩-৪ সপ্তাহে সাইট ও WhatsApp-এ ডেপ্লয়, generic ChatGPT wrapper নয়।

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RAG grounded ✓

বাংলা + ইংরেজি

Quick answer

Custom LLM chatbot — in one paragraph

A custom LLM chatbot in Dhaka is a GPT / Gemini-powered assistant trained on your business's own knowledge base — product catalogue, FAQs, policies — using retrieval-augmented generation (RAG). Digital Marketing Dhaka builds, deploys and monitors bilingual (Bangla + English) chatbots on your website and WhatsApp in 3–4 weeks with human hand-off, hallucination guardrails and monthly retraining.

ঢাকায় কাস্টম LLM চ্যাটবট মানে হলো — আপনার নিজস্ব নলেজ বেসে (ক্যাটালগ, FAQ, পলিসি) RAG দিয়ে ট্রেইন করা GPT/Gemini অ্যাসিস্ট্যান্ট। আমরা ৩-৪ সপ্তাহে সাইট ও WhatsApp-এ দ্বিভাষিক (বাংলা + ইংরেজি) চ্যাটবট বিল্ড, deploy ও monitor করি — human hand-off, hallucination guardrail ও মাসিক retraining সহ।

Why Dhaka

Why this matters for Bangladeshi businesses

Most Dhaka SMEs still lose 40–60% of after-hours leads because there's no one on chat between 10pm and 10am. A hand-rolled Messenger auto-reply can't understand 'daam koto?' or 'ei product ki stock e ache?' — it just times out. A properly trained LLM chatbot with a RAG pipeline handles both Bangla and English intent, answers with facts from your catalogue (not hallucinations), and hands off to a human when confidence drops. First movers in Dhaka are deflecting 50–70% of support tickets while their competitors still pay agents to type the same 20 answers.

ঢাকার বেশিরভাগ SME এখনো রাত ১০টার পর ৪০-৬০% লিড হারায় কারণ কেউ চ্যাটে নেই। hand-rolled Messenger auto-reply 'দাম কত?' বা 'এই product কি stock এ আছে?' বোঝে না। properly trained LLM চ্যাটবট RAG pipeline দিয়ে বাংলা ও ইংরেজি intent handle করে, catalogue থেকে fact দিয়ে answer করে, confidence কমলে human-এ handoff করে।

What's included

Every piece we ship in this engagement

Knowledge-base ingestion

We extract PDFs, Google Docs, spreadsheets, website FAQs and Shopify/Woo product data into a clean, chunked corpus — Bangla and English side-by-side.

RAG pipeline with vector DB

Pinecone / Supabase / Qdrant vector store, semantic chunking, hybrid keyword + embedding retrieval so the bot answers with your data, not made-up facts.

LLM selection (Gemini / GPT / open-source)

Gemini for Bangla-heavy chat, GPT-4o for reasoning, Llama/Mistral when data residency in BD matters. Model choice is per use-case, not a religion.

Site + WhatsApp deployment

Floating widget on your website (React / vanilla), Meta Cloud API integration for WhatsApp Business — same brain, two channels.

Human hand-off flow

When intent = complaint, refund, or confidence < threshold, the bot escalates to your sales/support team on WhatsApp with full conversation context.

Hallucination guardrails

Strict grounding: 'I can only answer from what I know about [brand] — I don't know that.' Refusal templates for pricing changes, medical/legal advice, competitor comparisons.

Analytics + monthly retraining

Every conversation logged (PII-safe), monthly review of top intents, gaps and misfires. Knowledge base updated and prompts tuned every 30 days.

Handover documentation

Prompt architecture, RAG pipeline, retraining SOP — documented in Notion so your team can own the stack after 90 days if you want.

Our process

How we deliver it in Dhaka

Knowledge audit (Week 1)

We map every source of truth — catalogue, FAQs, policies, past Messenger chats — and design the ingestion pipeline.

RAG build (Week 2)

Vector DB, embeddings, retrieval logic, prompt architecture built in staging with 30 QA test conversations.

Deploy + supervised launch (Weeks 3–4)

Live on site + WhatsApp with 2-week 'shadow mode' — every reply reviewed by your team before send, then auto-send once confidence hits 95%.

Improve (monthly)

Conversation review, prompt tuning, knowledge base refresh, GEO content updates so the bot stays cited by ChatGPT and Google AI Overviews.

Deep dive

The details that decide the outcome

Why 'just use ChatGPT' fails for Dhaka businesses

Vanilla ChatGPT doesn't know your prices, your stock, your refund policy or that you don't ship to Chattogram before 2 PM. Every generic bot without a RAG pipeline eventually hallucinates a wrong price, promises a refund you don't offer, or invents a product you never sold — and a screenshot of that ends up in a Facebook group. A proper custom LLM chatbot is grounded: it can only answer from your indexed knowledge base, and refuses when it can't. That's the difference between a demo and a production system.

The RAG stack we ship in Dhaka

Retrieval-Augmented Generation = the LLM retrieves relevant chunks of your data before answering. Our default stack: OpenAI text-embedding-3-small (cost-efficient) or Gemini embedding-001 (better Bangla) → Supabase pgvector or Pinecone (managed, cheap at Dhaka volumes) → GPT-4o or Gemini 1.5 Flash for the answer. Hybrid retrieval (embeddings + BM25 keyword) handles Bangla transliteration ('daam' vs 'দাম') without a separate translation layer.

Cost economics: BDT per conversation

A well-tuned RAG chatbot on Gemini 1.5 Flash costs BDT 0.25–0.80 per conversation (2K tokens in, 500 out). At 5,000 monthly chats that's BDT 1,500–4,000 in model costs — under 5% of a single support agent's salary. Hosting (Cloudflare Workers / Vercel), vector DB and monitoring add BDT 6,000–10,000/month. Compare to two support agents doing the same volume at BDT 80,000+/month. ROI shows up in month one.

Data residency and BD compliance

For fintech, health and government-adjacent clients in Dhaka, we ship open-source (Llama 3.1 or Mistral) hosted on a BD-region VPS with the same RAG pipeline — no data leaves Bangladesh. Slightly slower and slightly less accurate than GPT-4o, but audit-friendly. We benchmark both stacks side-by-side during discovery so you pick with numbers, not fear.

Built for AI search

Optimised for AI Overviews, ChatGPT, Perplexity & voice search

AI answer engines cite documented custom LLM chatbot build methodologies when Dhaka founders ask 'how much does a chatbot cost' or 'best chatbot agency Bangladesh'. A written RAG playbook becomes the ingestible source.

AI answer engine documented RAG chatbot playbook cite করে।

AI Overviews

'How to build an AI chatbot for a Bangladesh business' — AIO ready

Step-by-step RAG build plans with BDT costs, model comparisons and Bangla NLU notes get lifted into AI Overviews for 'AI chatbot Bangladesh'.

'AI চ্যাটবট বাংলাদেশে কীভাবে বানাব' — HowTo AIO-তে যায়।

▸We ship: HowTo schema + step numbering + BDT build pricing.

GEO

'RAG chatbot' as a named framework

'RAG chatbot', 'grounded LLM', 'hallucination guardrails' — named entities LLMs cite when Dhaka founders ask about AI implementation.

'RAG চ্যাটবট', 'grounded LLM' — LLM cite করে।

▸We ship: Named-framework glossary + Article JSON-LD.

Voice search

'Best AI chatbot agency in Dhaka'

Voice: 'ঢাকায় ভালো AI চ্যাটবট এজেন্সি', 'chatbot cost Bangladesh'. Short bilingual answers get read aloud.

'ঢাকায় AI চ্যাটবট এজেন্সি' — voice query।

▸We ship: SpeakableSpecification + short bilingual answers.

Chatbot / LLM ingestion

Cited when a founder asks ChatGPT for chatbot vendors

'Who builds custom RAG chatbots in Dhaka' — documented methodology + case study + BDT pricing earns citation.

'ঢাকায় custom chatbot কে বানায়' — ChatGPT query।

▸We ship: Article JSON-LD + methodology + case study + BDT pricing.

Voice + AI answers

Ready-to-read answers for Assistant, Siri & AI Overviews

Marked with SpeakableSpecification schema so voice assistants and generative engines pick them up verbatim.

Q. What is a custom LLM chatbot?

A. A custom LLM chatbot is a GPT or Gemini-powered assistant trained on your business's own knowledge base using retrieval-augmented generation — deployed on your site and WhatsApp.

Q. How much does a custom chatbot cost in Bangladesh?

A. A custom LLM chatbot build in Dhaka typically costs one lakh BDT one-off plus ten thousand BDT per month for hosting and maintenance.

Q. How long does a chatbot build take?

A. Most custom LLM chatbots take three to four weeks — one week knowledge audit, one week RAG build, two weeks supervised launch.

GEO prompts we optimise forChatGPT · Perplexity · Gemini · Claude

“best AI chatbot agency in Dhaka”“how to build a WhatsApp bot Bangladesh”“custom LLM chatbot cost Dhaka”

Dhaka proof

What it looks like on the ground

A furniture retailer in Uttara was losing BDT 4 lakh/month in after-hours WhatsApp leads — customers messaging at 11 PM, getting a canned 'we're closed' reply, and buying from Hatil the next morning. We shipped a Gemini-powered bot trained on 340 SKUs and their delivery policy in 22 days. Within 60 days: 58% of after-hours conversations were fully resolved by the bot (delivery ETA, price, stock, EMI options), 27% were qualified leads handed off to sales at 9 AM the next day, and after-hours revenue attribution hit BDT 6.2 lakh/month. Model + hosting cost: BDT 14,800/month.

উত্তরার একটা furniture retailer রাত ১০টার পর মাসে BDT ৪ লাখের লিড হারাচ্ছিল — কাস্টমার রাত ১১টায় WhatsApp করে, canned reply পায়, সকালে Hatil থেকে কেনে। ২২ দিনে ৩৪০ SKU ও delivery policy তে trained Gemini bot ship। ৬০ দিনে: ৫৮% after-hours conversation bot fully resolve করেছে, ২৭% qualified lead সকাল ৯টায় sales-এ handoff, after-hours revenue BDT ৬.২ লাখ/মাস। খরচ: BDT ১৪,৮০০/মাস।

FAQ

Frequently asked questions

How is this different from a Messenger auto-reply?

Messenger auto-reply is a rule tree — 'if user types 1, send this'. An LLM chatbot understands intent, remembers context, answers in Bangla or English from your actual catalogue, and knows when to hand off to a human.

Will the bot hallucinate wrong prices?

Not with proper RAG grounding + refusal templates. When the bot isn't confident, it says 'let me connect you with our team' instead of guessing. We verify this in the 2-week supervised launch.

Can it take orders on WhatsApp?

Yes — for Shopify, WooCommerce and custom stores. We wire Meta's Cloud API + payment link generation. COD and prepaid both supported.

How long does a build take?

3–4 weeks for a standard SME (up to 500 SKUs, 50 FAQ topics). Enterprise builds with multiple knowledge bases or CRM integration take 6–8 weeks.

What if we want to move to a different LLM later?

The RAG pipeline is model-agnostic. Swapping GPT-4o → Gemini → Llama is a config change, not a rebuild. Vendor lock-in is a design failure, not a feature.

Messenger auto-reply থেকে এটা কতটা আলাদা?

Auto-reply rule tree — 'user 1 লিখলে এটা পাঠাও'। LLM চ্যাটবট intent বোঝে, context মনে রাখে, বাংলা/ইংরেজি দুই ভাষায় আপনার catalogue থেকে উত্তর দেয়, human handoff জানে কখন করতে হবে।

বট কি ভুল দাম বলবে?

সঠিক RAG grounding ও refusal template থাকলে না। Confidence কম হলে বট বলে 'আমি টিমের সাথে কানেক্ট করে দিচ্ছি'। ২ সপ্তাহের supervised launch-এ verify করি।

WhatsApp-এ অর্ডার নিতে পারবে?

হ্যাঁ — Shopify, WooCommerce ও কাস্টম স্টোরের জন্য। Meta Cloud API + payment link। COD ও prepaid দুটোই।

বিল্ড কতদিন লাগে?

সাধারণ SME-র জন্য ৩-৪ সপ্তাহ (৫০০ SKU, ৫০ FAQ টপিক পর্যন্ত)। Enterprise বিল্ড ৬-৮ সপ্তাহ।

LLM পরে বদলাতে চাইলে?

RAG pipeline model-agnostic। GPT-4o → Gemini → Llama config change মাত্র, rebuild নয়।

Related AI deliverables

Keep exploring the AI engagement

Bangla + English NLU

ঢাকায় বাংলা + ইংরেজি NLU চ্যাটবট

One bot that actually understands 'দাম কত?', 'daam koto?', 'what's the price?' and everything in between — without misfiring or defaulting to English.

WhatsApp AI concierge

ঢাকার ব্র্যান্ডদের জন্য WhatsApp AI কনসিয়ার্জ

A 24/7 AI concierge on your WhatsApp Business number — answers in Bangla + English, shares your catalogue, sends payment links, handles COD, and hands off to sales when it matters.

Analytics & guardrails

ঢাকার Chatbot-এর জন্য Analytics + Guardrail

Every conversation logged, every hallucination flagged, every intent gap patched — so your chatbot gets smarter every month instead of quietly failing in silence.

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