The AI Chatbot Development Agency That Builds LLM Chatbots, Not Chat Widgets

Both, plus helpdesk and internal-knowledge bots. The channel changes the integration, not the method: retrieval that grounds answers in your data, tools that update your CRM or helpdesk, and a clear hand-off to a human. For one client, a tourism board, the WhatsApp agent now handles 80% of requests without a human.

Yes. We deliver chatbots in English, French and Arabic, and test each language against your real questions before launch.

A chatbot development agency designs and builds the LLM chatbot end to end: the conversation flow, the retrieval layer that grounds answers in your data, the tool calls that let the bot take action instead of only responding, and the escalation logic for when it should hand off to a human. The build is only half the job. The agency should also cover the eval harness that catches regressions and the maintenance plan that keeps the bot useful as your data and models change.

A simple FAQ chatbot on a managed platform can launch for under $10,000. Custom LLM chatbots with retrieval and integrations typically run in the low five figures to start, scoped after an audit call, because the honest number depends on how many systems the bot needs to read from and write to. Enterprise programs with compliance, multi-channel deployment, and ongoing maintenance run well past six figures.

A managed platform trades flexibility for speed: faster to launch, vendor-supported, but you are limited to the retrieval, integrations, and logic the platform already ships. A custom build from an agency costs more upfront but gives you full control over retrieval, prompts, tool calls, and the exact systems the bot touches. Most teams start with a platform for a single simple FAQ flow and move to a custom build once the bot needs to read from more than one internal system.

No. A chatbot marketing agency typically builds lead-capture and campaign bots that live on a landing page or ad flow. A chatbot development agency builds the underlying LLM system: the model, the retrieval, the integrations, and the maintenance behind it. ManasFlow is a development agency, not a marketing shop; the chatbots we build are wired into your CRM and helpdesk, not just a lead form with a chat skin on top.

Both. A website chatbot needs the same underlying architecture as an internal one: retrieval grounded in your docs or product data, escalation to a human when the bot hits its limit, and a maintenance plan so answers stay accurate as your site and product change. We scope the surface, website widget, in-app assistant, or internal tool, during the audit call rather than assuming one template fits every use case.

Conversational AI is the broader term for chat, voice, and messaging systems that hold context across turns. A chatbot is one surface for that same underlying technology. We build the LLM layer, the retrieval, and the integrations that make conversational AI work in text-based chat; if you need voice specifically, that is a separate scoping conversation.

The reply is the visible part, but the retrieval step that finds the right information and the classification step that routes intent are just as important, and both benefit from the same LLM. Where a plain rules-based flow does the job, we do not add AI just to add it: a chatbot that only needs to answer three fixed questions does not need a full retrieval pipeline.

Yes, and it should. A chatbot that cannot look up a contact, create a ticket, or update a deal is leaving most of its value on the table. We wire chatbots into Salesforce, HubSpot, Zendesk, Intercom, and internal APIs as part of the standard build, not as a separate add-on.

A focused, well-scoped chatbot MVP typically ships in four to eight weeks. Production hardening, integrations, evals, observability, and escalation logic, adds another four to eight weeks. Most production-ready chatbots take two to four months from kickoff.

Either a clean handover with documentation, or a maintenance retainer where we keep tuning prompts and retrieval as your data grows, rerun evals to catch regressions, and update the bot as language models improve. A chatbot without a maintenance plan tends to drift within months as user intent and your underlying data change.

Yes, English, French, and Arabic, which makes us a fit for EU, MENA, and bilingual North American teams that struggle to find a chatbot partner who delivers cleanly in their users' first language rather than machine-translating an English-only bot.

A freelancer typically builds the happy path: the bot answers the questions it was designed for. We build the happy path plus retrieval grounding, escalation logic, an eval harness, and a maintenance plan, and stay accountable for the system after launch. That difference does not show up in a demo; it shows up three months in, when the underlying data has changed and the bot either keeps up or does not.