AI customer support agents that resolve tickets autonomously - not script-driven chatbots.

Remote AI Chatbot DevelopmentA Dedicated DeveloperHourly or Monthly

Q: What is the difference between an AI chatbot and an AI agent?

A chatbot (this page) answers questions, usually from a knowledge base, in a back-and-forth - it reads the question, retrieves the relevant context from your docs, and writes an answer with citations. An AI agent (see /services/ai-agent-development) takes multi-step actions - it plans, calls your tools, reads results, decides again, and writes back to your systems. A support chatbot can deflect 'what is your refund policy'; a support agent can also issue the refund. They overlap (a support agent is a chatbot that can also take actions), and one engagement can cover both - the scoping call sorts out which you actually need.

Q: How much does AI chatbot development cost - hourly or monthly?

Two ways. By the hour at $25 - pay as you go, time tracked to the minute, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 RAG pipeline, a KB ingest, an evals pass, or a model swap. Or monthly at the standard $2,000 per dedicated developer for 160-172 hours of full-time, exclusive work in your repo and your cloud, with a 7-day risk-free trial - the better value when the KB is a rolling thing with new docs every week, more channels, and patient eval-driven iteration. Either way: a senior lead reviews and tests every release. The monthly rate is the same flat rate as every other Empiric Infotech engagement; no premium for the chatbot framing. Model, embedding, and vector DB usage is billed to your own accounts at cost. Add a developer at the same flat rate when the surface grows.

Q: What is RAG and why do I need it?

RAG (retrieval-augmented generation) is the technique that lets a chatbot answer from your knowledge base instead of from the model's training data alone. The chatbot retrieves the most relevant chunks from your corpus (docs, help-centre, manuals, tickets, DB rows) and gives them to the LLM as context, so the answer is grounded in your content and the chatbot can cite its sources. Without RAG, a chatbot either makes up answers (hallucination) or only knows what was true the day the model was trained. For any chatbot that needs to answer factually from your data, RAG is the right pattern; we will tell you the few cases where a simpler approach (long-context with the whole doc, or a function call against a structured API) wins instead.

Q: Where can the chatbot be embedded - just on a website?

Wherever your users are. A site widget that loads fast and matches your brand; an in-product chat where the user is already authenticated and you can pass context (the page, the account, the entity); a Slack app for internal-knowledge use cases; WhatsApp Business for customer-facing markets where WhatsApp is the channel; Microsoft Teams for internal-tools cases; a mobile SDK for iOS or Android; a custom integration into your existing helpdesk or CRM. The same brain answers across every surface from one knowledge base.

Q: Does the chatbot really deflect tickets - or just deflect easy ones?

Honest answer: it depends on the KB and the eval discipline. A well-tuned RAG chatbot over a real help-centre and product-manual corpus typically deflects 30-60% of support volume on the questions it knows about, and routes the rest to a human with the conversation context intact. We measure deflection rate, CSAT, escalation rate by topic, the 'I did not know' rate, and citation accuracy - and use the unanswered-question report to grow the KB so the deflection rate climbs over time. We will not promise a number on the scoping call; we will commit to measuring it from week one and tuning against it.

Q: How do you stop the chatbot from hallucinating?

Several layers in production from day one. Strict RAG (the LLM answers only from retrieved chunks, with a clear 'I do not know' fallback when retrieval confidence is low). Citations the user can click. Off-topic handling so the chatbot does not answer questions outside the scope you set. Prompt-injection resistance on user input AND on retrieved chunks (a poisoned KB chunk is a real attack). Content and safety filters where they matter. Eval suites that run on every change and catch a regression before it ships. PII redaction in logs. An audit trail an oncall engineer or compliance reviewer can read.

Q: How does handoff to a human agent work?

Direct integration with your helpdesk - Zendesk, Intercom, Front, Freshdesk, HubSpot Service, or your custom system. The chatbot creates the ticket, attaches the conversation transcript, the retrieved sources, the user's account, and a brief summary of what was tried, so an agent picks up at the right place instead of asking the customer to start over. Routing rules on confidence (escalate when retrieval is weak), topic (escalate billing or legal questions), sentiment (escalate angry conversations), or business hours (escalate when nobody is around to follow up).

Q: Which models, embeddings, and vector DBs do you use?

Whichever wins on your evals at a cost that works. Chat models: Claude (Anthropic) is our default for long-context retrieval and citation discipline; GPT (OpenAI) for cases where the latest model lineup is a fit; Gemini (Google) for multimodal or long-context cases; open-source (Llama, Mistral, Qwen) for cost-sensitive or self-hosted cases. Embeddings: OpenAI text-embedding-3, Voyage, Cohere, or a local model. Reranking: Cohere rerank or Voyage rerank when retrieval quality is the bottleneck. Vector DBs: Pinecone, Weaviate, Qdrant, pgvector (cleanest when you already run Postgres), or Chroma. We support model and stack swaps as a first-class operation - the prompt and retrieval logic stay the same; only the model or the index changes.

Q: What about my existing chat tool (Intercom, Zendesk, HubSpot AI)?

Often the cleanest pattern is: keep your existing chat surface (the widget, the inbox, the user data) and put our RAG behind it as the answer engine, with handoff back to your agents through the same tool. We have integrated with Intercom, Zendesk, Front, HubSpot, and Freshdesk. If your existing tool's bundled AI is doing the job, we will tell you - we are not here to sell a rebuild for its own sake.

Q: Where does the chatbot run, and who owns it?

You own it - your repo, your cloud account, your knowledge base, your embeddings, your model keys, your data - from day one. It runs where you want it: AWS, Azure, GCP, Hetzner, DigitalOcean, Hostinger, or on-prem. We work inside your accounts, not ours, so there is nothing to claw back if the engagement ends. Model, embedding, and vector DB usage goes against your accounts at cost, not marked up.

AI chatbots that answer from your knowledge base with citations, deflect support, hand off to a human, and report what they could not answer - built by a remote dedicated developer for $25/hr or $2,000/mo, with a senior lead reviewing every release.

GDPR / SOC 2 Aware

Claude / GPT-Powered

Self-Hosted or Vendor-Hosted

AI Chatbots From Empiric Infotech LLP

Last updated: 2026-05-14

Empiric Infotech LLP builds custom AI chatbots that answer from your knowledge base - your docs, help-centre, product manuals, internal wiki, support history, and structured data - not a stock GPT bot that hallucinates and frustrates customers. Two ways to engage a remote dedicated AI chatbot developer: book hours at $25/hr for a defined scope (a v1 RAG pipeline, a knowledge-base ingest, an evals pass, a model swap), or lock a month at the standard $2,000 for 160-172 hours of full-time, exclusive work when the knowledge base is a rolling thing - new docs every week, more channels to embed in, evals to chase, model swaps to test. Either way the developer works in your GitHub or GitLab org, your cloud (AWS, Azure, GCP, Hetzner, DigitalOcean, or Hostinger - whichever your team is on), and your model and vector DB keys, with the retrieval stack that fits your case (OpenAI or Voyage embeddings, Cohere rerank, Pinecone / Weaviate / Qdrant / pgvector / Chroma, LangChain or LlamaIndex or a hand-rolled pipeline - chosen with you, not pushed). We design the chatbot's scope and tone, ingest and chunk your knowledge base, wire retrieval and reranking, build the prompt and citation logic, embed it where your users actually are (site widget, in-product, Slack, WhatsApp Business, Microsoft Teams, an iOS or Android SDK), set up handoff to your helpdesk (Zendesk, Intercom, Front), and add evals, guardrails, and a dashboard on deflection rate, citation accuracy, escalation rate, the questions it could not answer, and per-conversation cost. A senior team lead reviews and tests every release. Why the hourly premium? RAG and prompt work is high-iteration expert work - chunking, retrieval tuning, prompt and citation engineering, the long tail of edge cases; the monthly rate is the same flat $2,000 as any Empiric engagement once you commit. If your need is actions, not answers, see /services/ai-agent-development; if it is a phone line, see /services/voice-agent-development.

$25/hr

hourly, pay as you go

$2,000/mo

monthly, lock it in

Weekly

time report + demo

Senior-lead

review on every release

What an AI chatbot engagement delivers

Not a demo bot that handles the first three FAQs and hallucinates on the fourth. A production chatbot embedded where your users actually are, answering from your real knowledge base, with citations and the deflection metrics to prove it is working.

A chatbot that answers from your knowledge base, with citations

Not a generic GPT wrapper. A retrieval-augmented chatbot that reads from your real content - docs, help-centre articles, product manuals, internal wiki, past support tickets, structured DB rows - and quotes its sources back so users can verify and your team can trust it. A clear 'I do not know' path when the answer is not in the corpus, instead of a confident hallucination.

A RAG pipeline tuned for your corpus

Ingestion that handles HTML, PDFs, Markdown, Notion exports, Confluence dumps, Zendesk help-centre, your DB - with the chunking strategy your corpus calls for (semantic, structural, parent-child, recursive), embeddings from OpenAI / Voyage / Cohere / a local model, a vector DB you can move off (Pinecone, Weaviate, Qdrant, pgvector, Chroma), reranking where retrieval quality is the bottleneck, and an incremental re-index pipeline so new docs land in the chatbot the day they are written.

The right surfaces - your site, your product, Slack, WhatsApp, Teams

A site widget that loads fast and matches your brand; an in-product chat where the user is already authenticated and you can pass context (the page, the account, the entity ID); a Slack app for internal-knowledge use cases; WhatsApp Business for customer-facing markets where WhatsApp is the channel; Microsoft Teams for internal-tools cases; a mobile SDK for iOS or Android. The same brain answers across every surface from one knowledge base.

Handoff to a human that does not lose context

Direct integration with Zendesk, Intercom, Front, Freshdesk, HubSpot, or your custom helpdesk - the chatbot creates the ticket, attaches the conversation transcript, the retrieved sources, the user's account, and a brief summary of what was tried, so an agent picks up at the right place instead of asking the customer to start over. Routing rules on confidence, topic, sentiment, or business hours.

Deflection metrics that prove it is working

A dashboard on what matters - deflection rate, CSAT on chatbot conversations, escalation rate by topic, the 'I did not know' rate, citation accuracy on eval prompts, per-conversation cost, the questions your customers asked that the knowledge base did not cover - so your support manager can run the chatbot like a team member, and your team can grow the KB to plug the gaps the chatbot found.

Guardrails, evals, and a human escalation path

Eval-driven testing of the chatbot on real questions (regression on every prompt, chunking, model, or KB change), content and safety filters, PII redaction in logs and observability, off-topic handling, prompt-injection resistance on user input and on retrieved chunks, a clear escalate-to-human path, and an audit trail an oncall engineer or a compliance reviewer can read.

A developer who is still there next month

Your knowledge base changes, your product changes, models change, the cost-per-token line moves. A dedicated engagement means the same developer ingests the new docs, swaps the embedding or chat model, adds the next channel, keeps the evals green, and grows the chatbot from a v1 into a deflection lever, month after month - instead of handing you a v1 and a stale README.

How we scope an AI chatbot engagement

No multi-week sales cycle and no twenty-page statement of work. A call, a written scope, a trial, then hourly or monthly - your call.

A scoping call

Thirty to forty-five minutes. You tell us who the chatbot is for (customers, prospects, internal teams), what knowledge base it answers from, what your top-volume questions are today, where you want to embed it (site, product, Slack, WhatsApp, Teams), the helpdesk it should hand off to, and what would count as a real outcome - deflection rate, tickets avoided, first-response time, CSAT. No charge, no obligation.

A written scope and team proposal

We send back the chatbot scope (audience, channels, topics in and out of scope), the retrieval stack we would use (embeddings, vector DB, reranking), the knowledge-base ingest plan and the re-index cadence, the evals we would measure, the guardrails and escalation policy, the model and any rough cost-per-conversation estimate, who we would put on it - one developer to start, or a small dedicated team when there are multiple channels and a large corpus - and the price both ways.

A 7-day risk-free trial (on the monthly plan)

The developer gets into your repo, cloud, and a sample of your knowledge base, and ships the first slice - the chatbot answering real questions from a real subset of your KB with citations and an eval baseline, reviewed and tested by the senior lead - inside the first week. If it is not a fit by day 7, full refund on the monthly plan, no debate. On the hourly plan, you have already seen what an hour buys.

Hourly or monthly, your choice

Hourly: billed by the hour at $25, time tracked to the minute, a weekly time report and a demo, stop any time - best for a defined scope or a burst of work like a v1 RAG pipeline, an evals pass, or a model swap. Monthly: 160-172 hours at the standard $2,000, monthly billing, cancel with 7 days notice - the better value when the knowledge base is a rolling thing with new docs every week, more channels, patient eval-driven iteration. Switch between them month to month as the work grows or settles; add a developer at the same rate.

Two ways to engage an AI chatbot developer

Two ways to engage a remote AI chatbot developer. By the hour at $25 - pay as you go, time tracked to the minute, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 RAG pipeline, a knowledge-base ingest, an evals pass, or a model swap. Or monthly at the standard $2,000 for 160-172 hours of full-time, exclusive work - the better value when the knowledge base is a rolling thing with new docs every week, more channels, and patient eval-driven iteration, with a 7-day risk-free trial. Either way: your repo, your cloud, the right retrieval stack for the case, and a senior lead reviews and tests every release. Why the hourly premium? RAG and prompt work is high-iteration expert work - chunking, retrieval tuning, prompt and citation engineering, the long tail of edge cases; the monthly rate is the same flat rate as any Empiric engagement once you commit. Model and platform usage (LLM tokens, embeddings, vector DB seats) is billed to your own accounts at cost.

Pay as you go

Hourly plan

$25/hr

the premium short-burst rate - RAG work is high-iteration expert work

A dedicated AI chatbot developer, exclusive to you while you have hours booked
Pay as you go - billed by the hour, time tracked to the minute, a weekly report and demo
Best for a defined scope (v1 RAG pipeline, KB ingest, evals pass, model swap); no monthly commitment, stop any time
Your repo, cloud, vector DB, and model keys from day one
Every release reviewed and tested by a senior lead before it goes live

Book a scoping call

Best value

Monthly plan

$2,000/mo

the standard flat rate - much cheaper per hour when the KB is a rolling thing

A dedicated AI chatbot developer, full-time and exclusive - 160-172 hours a month
The best value when the KB is a rolling thing - new docs, more channels, evals, model swaps
Your repo and cloud from day one; the same flat rate as any Empiric engagement
7-day risk-free trial, monthly billing, cancel with 7 days notice
A senior lead reviews and tests every release before it goes live

Book a scoping call

Larger or longer

Dedicated team

Custom

for multi-channel rollouts or several chatbots in production

A small dedicated team - developers plus a senior team lead who reviews and tests every release
Add a developer (or a designer for the widget UI) at the same rate, in 48 hours
Pair a chatbot developer with an agent developer or an MCP server developer to ship related surfaces at once
Best for multi-channel rollouts, large knowledge bases, or several chatbots in production

Talk to us

Most AI chatbot engagements start small - a block of hours at $25/hr for a v1 RAG pipeline on a slice of your KB, or a first month at $2,000 - with a working chatbot answering from a real KB subset inside the first week. When you want more channels (Slack on top of site, WhatsApp on top of in-product), a multilingual rollout, or a team building related surfaces (an agent that takes actions, an MCP server the chatbot calls, an analytics dashboard), you add a developer (or a designer for widget UI work) at the same flat rate, in 48 hours, no re-contracting, and a senior team lead reviews and tests every release and keeps the chatbots consistent. Quality assurance is part of that lead's job, not an extra line item. Model and platform usage costs (LLM tokens, embeddings, vector DB seats) are yours, billed to your accounts at cost.

What the first 90 days of an AI chatbot engagement look like

Whether you are booking hours or on the monthly plan, the shape is the same. Here is a typical first three months on a chatbot build.

Week 1
Onboarding and the first slice of the chatbot
Repo and cloud-account access, a working local environment, the retrieval stack chosen, a sample of the knowledge base ingested and chunked, embeddings into a vector DB you control, and a first slice live - the chatbot answering real questions from a real KB subset with citations, an eval baseline on a curated question set, run logs, error alerting - shipped and reviewed. On the monthly plan, day 7 is the risk-free decision point.
Month 1
A chatbot answering real questions, in production
The full knowledge base ingested, the chatbot embedded in the priority channel (your site widget, your in-product chat, or your Slack), handoff to your helpdesk wired with conversation context, the prompt and citation logic tuned, guardrails (off-topic, PII, prompt-injection) in place, an eval suite running on every change, and a dashboard on deflection rate, CSAT, escalation rate, the unanswered questions, and per-conversation cost. By the end of month one a real chunk of support volume is being deflected.
Month 2
More channels, more KB, the long-tail questions
Edge cases month one surfaced - smoothed, the chunking and retrieval tuned for the questions that retrieved badly, the second channel added (Slack on top of site, or WhatsApp on top of in-product), multilingual handling if your users are not all anglophone, the KB grown to cover the gaps the chatbot reported, and a model-fallback path for outages. The chatbot stops being a v1 and starts being a deflection lever.
Month 3 and on
Deflection numbers, cost, and ahead of the roadmap
A reliability pass (retries, idempotent ingest, replay on failure), a cost pass on per-conversation model and platform spend, a quality pass on citation accuracy and the 'I did not know' rate, a model swap if a newer or cheaper one wins on your evals, and the next channel or the next KB source scoped. From here the developer is ahead of your backlog - the next channel, the next language, the next slice of the KB.

A dedicated AI chatbot developer - hourly or monthly - vs a fixed-price chatbot agency, a no-code chatbot platform, or a freelance contractor

	Empiric Infotech (AI chatbot developer - hourly or monthly)	Fixed-price chatbot agency	No-code chatbot platform (configure-it-yourself)	Freelance contractor (Upwork, Fiverr, Toptal)
What you actually get	A custom RAG chatbot answering from your KB with citations, embedded where your users are, owned by you, with the developer who built it still there to grow it	A chatbot built to a spec, then a maintenance retainer or you are on your own	A widget you configure yourself; the KB ingest, retrieval quality, and citations are limited by the platform	One chatbot built, then they are gone; it rots when a model changes or your KB does
Pricing model	$25/hr for hourly work, or the standard $2,000/mo for a full-time developer if you lock a month - your choice; model, embedding, and vector DB usage billed to your accounts at cost	$8K-$60K fixed bid for a v1 chatbot; change orders billed extra	Platform subscription ($50-$1,500/mo by traffic) plus your team's time tuning and curating	$25-$120/hr, quality varies; scope creep comes out of your budget
Estimate before you commit	An estimate both ways - hours per channel or what a month covers - plus a weekly time report and a demo	A fixed bid - you wear the overage as change orders	Platform demos; the real cost shows up after week two of ingest and tuning	An hourly quote, often optimistic
Retrieval and citation quality	Your corpus, your chunking strategy, the right embeddings and reranker, citations the user can verify, evals on every change	Per the spec; a new citation source or a new chunking strategy is a change order	Whatever the platform supports; citations are often shallow or absent	Whatever the contractor knows
Channels and handoff	Site, in-product, Slack, WhatsApp Business, Teams, iOS or Android - all from one brain, with helpdesk handoff carrying the conversation context	Usually one or two channels; more channels are change orders	Whatever the platform supports; handoff context is often thin	Usually the channel they have done before
Evals and guardrails	Eval-driven from week one - regression on every change, off-topic and prompt-injection handling, PII redaction, citation grading, an audit trail	Per the spec; new evals and gates are change orders	What the platform offers, often shallow; advanced cases are on you	Often skipped unless you ask
Quality control	A senior lead reviews and tests every release before it goes live - built in, no extra charge	Per agency - often the same people who built it	On you to review and verify	On you to review and verify
Who owns the chatbot, KB, and embeddings	You - your repo, your cloud account, your knowledge base, your embeddings, your model keys, from day one	You, on final payment	The platform owns the runtime; you own the configuration; embeddings often locked to the platform	You per contract - check the IP-assignment clause
When the model changes (or breaks)	The same developer swaps the chat or embedding model, re-runs the evals, and ships the fix - book an hour, or it is in the monthly plan	A support ticket, or a new maintenance retainer	Wait for the platform to support it; in the meantime you are stuck	Re-hire the contractor, or it stays broken
Time to start	48 hours	2-6 weeks (proposal, SOW, kickoff)	Days (sign up); a week or two of configuration and ingest before it does real work	Days to weeks (post a job, review, interview)

Figures are typical market ranges, not quotes. Model, embedding, and vector DB usage costs (LLM tokens, embeddings, vector DB seats) apply on top of any build cost in every option and are billed to your own accounts in ours. A fixed-price agency build of a comparable RAG chatbot commonly lands in the $8K-$60K range before change orders, depending on the corpus size, the number of channels, and the depth of helpdesk handoff.

Working hours and meeting availability

Our AI chatbot developers work 09:30 AM to 07:30 PM IST, Monday to Friday. A project manager is reachable 07:30 AM to 10:30 PM IST. Live overlap by region:

Region	Developer live overlap	PM available for meetings	What this means
USA East (ET)	1 hr 9:00-10:00 AM ET	9:00 PM previous day - 12:30 PM ET	Morning standup, then most of a working day's ingest, retrieval tuning, and prompt work shipped async before your day starts.
UK and Ireland (GMT/BST)	5-6 hr 9:00 AM - 2:00 PM	Full UK working day	Live eval reviews, retrieval walkthroughs, and watching a hard question get answered together across the morning.
Western Europe (CET/CEST)	6-7 hr 9:00 AM - 4:00 PM	Full CET working day	Effectively a same-time-zone working relationship - live design, pairing, and review through most of the day.
Sydney and Melbourne (AEST/AEDT)	3.5 hr 2:00 - 5:30 PM AEST	12:00 noon - 3:00 AM next day AEST	Afternoon standup and live review, then overnight async builds, KB ingest, and deploys.

Why teams build their AI chatbot with a dedicated developer, not a fixed-price agency

Production chatbots are rarely the part of the build that ships on the demo day, they are the part that earns its place in month three: the retrieval tuning when a customer asks a question the corpus only half-answers, the handoff that does not drop context, the dashboard the support manager actually opens. An in-house AI engineer to carry that work is roughly $9,200 to $13,300 a month once you add benefits, payroll tax, and equipment, and a strong AI-fluent one is hard to hire and slow to start. A fixed-price chatbot agency build of a v1 RAG bot typically runs $8,000 to $60,000 before the first change order, then a separate maintenance retainer. Empiric Infotech is billed two ways - $25 an hour for a defined scope, or the standard $2,000 a month per developer for 160-172 hours of full-time, exclusive work - with the same person on your chatbot the next month and the month after, and a senior lead reviewing and testing every release at no extra cost.

Most chatbot builds fail in the same places: a corpus chunked badly so retrieval misses the obvious answer; a confident hallucination on the question the corpus does not cover; a 'handoff' that drops the conversation context and asks the customer to start over; citations the user cannot click; no eval suite, so the day after a model swap no one notices retrieval got worse; no dashboard, so the support manager cannot tell if it is working. A dedicated Empiric developer has shipped retrieval-augmented chatbots in production for B2B SaaS, support, and product teams, with the evals and the handoff discipline that turn a chatbot from a feature into a deflection lever.

We have built AI and LLM features into products since the current wave began - retrieval, agents, structured extraction, model integration - and shipped web and mobile products since 2014. The depth shows up in the parts a quickstart skips: chunking modelled to your corpus, an 'I do not know' path the user actually trusts, citations that survive a re-index, helpdesk handoff with the conversation transcript and the retrieved sources, PII redaction in logs and observability, and the honesty to say when a single tool-using LLM call (or an agent that takes actions, see /services/ai-agent-development) beats a chatbot for what you are actually trying to do.

Empiric dedicated AI chatbot developer

$2,000/mo

the standard flat monthly rate - 160-172 hrs full-time, exclusive; or $25/hr for a defined scope; senior-lead review on every release; model and vector DB usage at cost

Fixed-price chatbot agency build

$8K‑$60K

One-time fee. A v1 RAG chatbot; change orders and maintenance extra; usage costs still yours

In-house AI engineer (fully loaded)

$9.2K‑$13.3K/mo

$110K-$160K salary + benefits + payroll tax + equipment - and rarely a full-time job on its own

Recent AI, product, and integration work

AI product, USA client

Superintelligence - an AI product engineered end to end

An AI product built for a USA client by an Empiric Infotech team - the application, the model integration, and the backend behind it. The kind of LLM-and-retrieval work a chatbot build sits inside.

Read case study

EdTech, German client

Vedu - AI image generation and TTS in an EdTech app

An AI-powered German language learning product built for a German client by a two-person Empiric Infotech team - the Flutter app, the flashcard and quiz engine, text-to-speech, and AI image generation wired into the content backend. The integration and evals discipline a chatbot build needs.

Read case study

Travel and hospitality, USA client

Roamate - AI features inside a travel platform

A solo travel companion platform built for a USA founder by a two-person Empiric Infotech team - the Flutter app, the web surface, real-time chat, and the APIs and AI-assisted features behind them. Chat-style assistance inside a real product.

Read case study

Ready to build your AI chatbot?

Tell us who the chatbot is for, what knowledge base it answers from, where you want to embed it, and what would count as a real outcome - deflection rate, tickets avoided, CSAT, first-response time. Within 24 hours we will send back a chatbot scope (audience, channels, topics in and out of scope), the retrieval stack we would use, a KB ingest plan, an evals plan, a team proposal, and an estimate both ways - hours per channel or what a month covers. Your developer starts inside 48 hours, and a senior lead reviews and tests everything before it reaches you.

Who We Help:

Built for Businesses That Need
Conversations That Convert

We partner with founders, support teams, and growth leaders who’ve outgrown basic chat widgets and need AI-powered chatbots that actually understand, respond, and drive results.

This Is for You If:

Your customers ask complex questions that scripted bots can’t handle

You’ve tried off-the-shelf chatbot tools - but hit limits in customization or accuracy

You’re still relying on live chat agents for repetitive, high-volume queries

You need a bot that can handle multiple intents, languages, and integrations

You want AI that represents your brand voice, not a generic template

You’re ready for a chatbot that boosts conversions, reduces support load, and scales with your business

What We Do

We Build AI Chatbots That Talk, Think, and Deliver Results

We don’t just embed a script into a chat window. We design fully customized, AI-powered chatbot systems - tailored to your brand voice, customer needs, and business goals.

No generic templates. No shallow automation. Just deeply intelligent bots that understand, respond, and convert - like your best support agent, available 24/7.

What We Build:

AI chatbots that handle complex, multi-turn conversations

Natural language understanding (NLU) to interpret user intent accurately

Context-aware responses that adapt as the conversation evolves

Multi-channel bots for websites, apps, social media, and messaging platforms

Seamless integrations with CRMs, helpdesks, eCommerce, and internal tools

Scalable systems that grow with your customer base and product range

Platforms & Tools We Work With (and Beyond):

We’re platform-agnostic - if it can chat, we can make it smarter.

Core Capabilities

Conversational AI for Every Touchpoint

Design intelligent chatbots that handle sales, support, onboarding, and FAQs - across web, mobile, and messaging platforms - with context awareness and natural conversation flow.

Built with: Chat-GPT, OpenAI APIs, LangChain, Dialogflow, Rasa

Multi-Channel Deployment

Launch your chatbot on WhatsApp, Facebook Messenger, Instagram, web chat widgets, or custom in-app solutions - with unified logic across all channels.

Powered by: WhatsApp Cloud API, Meta APIs, Telegram Bot API, Twilio

Context-Aware Conversations

Enable your chatbot to remember past interactions, understand user intent, and adapt responses based on conversation history and business rules.

Tech behind the scenes: LLMs, vector databases, custom NLP pipelines

Seamless System Integrations

Connect your chatbot with CRMs, ERPs, payment gateways, booking systems, and custom APIs - so it can do more than just talk.

Integrated into: HubSpot, Salesforce, Zoho, Stripe, n8n, Make

Analytics & Continuous Improvement

Get real-time insights into chatbot performance, user behavior, and drop-off points - then optimize conversations for better engagement and conversions.

Integrated with: Google Analytics, PowerBI, custom reporting dashboards

Our Chatbots in Action

Real Conversations. Real Automation. Real Results.

Here’s what happens when we replace static forms and endless email threads with AI-powered chatbots that work 24/7, answer instantly, and integrate seamlessly with your systems.

From Website Visitor - Qualified Lead in Seconds

Engage, qualify, and route prospects instantly - no forms, no waiting.

Used by: B2B SaaS companies, agencies, and service providers who want faster conversions.

Instant Support, Zero Queue Times

Answer FAQs, track orders, and resolve issues automatically while handing complex cases to humans.

For: E-commerce brands, D2C businesses, and support-heavy operations.

Smart HR & Recruitment Chatbots

Screen candidates, schedule interviews, and answer job FAQs automatically - all inside WhatsApp or your careers page.

Perfect for: Growing companies tired of repetitive HR tasks.

E-commerce Upsell & Abandoned Cart Recovery

Chatbots that recommend products, apply discounts, and recover lost sales across WhatsApp, Messenger, and web chat.

Perfect for: Online stores that want to boost sales and win back customers.

Internal Ops Chat Assistants

From generating reports to fetching documents, your team’s AI chatbot handles repetitive internal requests instantly.

Designed for: Teams that want faster workflows without learning new tools.

Want a chatbot this smart - or smarter?

How We Build Chatbots That Scale

A Collaborative Process, Built Around Your Conversations

We don’t just drop a chatbot on your website - we design conversational systems that understand your users, connect with your tools, and scale with your business. Our process ensures your chatbot delivers measurable value from day one.

Discovery & Conversation Mapping

We analyze your customer touchpoints, support tickets, sales scripts, and FAQs to identify where a chatbot can make the biggest impact.

Outcome: Conversation strategy and ROI-backed implementation plan

Chat Flow & Tech Stack Design

We design conversational flows, choose the best AI/NLP models, and plan integrations - ensuring your chatbot matches your brand voice and technical requirements.

Outcome: Custom chatbot blueprint, platform-agnostic architecture

Prototype & Validate

We ship a focused v1 of your highest-value chatbot use case - testing with real users to refine responses, flows, and handover logic.

Outcome: Proven chatbot concept with early wins and stakeholder buy-in

Full Development & Integration

From multi-channel deployment (web, WhatsApp, Messenger, Slack) to CRM and payment gateway integrations - we deliver a production-ready chatbot.

Outcome: Fully functional chatbot running live in your chosen channels

Training & Handover

We train your team to update content, review analytics, and manage the bot without needing a developer for every change.

Outcome: Self-sufficient chatbot management with complete control