AI Chatbot Development Services
Most chatbot pilots fall apart between the demo and production. We build the kind that don't. Our US-based team designs, ships, and supports AI chatbots that integrate with the software your team already uses, and stays involved long after launch.
AI Chatbots That Reach Production
AI chatbot development is the work of building conversational software, in text or voice, that runs on a large language model and connects to your data, tools, and day-to-day workflows. Models like Claude, GPT, and Gemini have made the conversation part easy. The hard part is everything around it.
Most chatbots fall into one of two camps. Number one: RAG-backed assistants answer questions using your own content, your internal docs, your product manuals, your policy library, and your support history.
Agentic chatbots go further. They take real actions in your tools: closing a ticket, updating a record, kicking off a workflow, and handing off to a person when they should. A lot of chatbot pilots fail because someone bought one and the team built the other.
We help you figure out which one you actually need, and then we build it. That includes the retrieval architecture, the prompts, the evaluation work, the cost controls, the observability, and the integration with your CRM or product.
Custom AI chatbots built for real business workflows.
We design and develop chatbots that help teams support customers, automate operations, surface knowledge, and embed AI directly into the software they already use. From retrieval-backed assistants to fully agentic workflows, we build production-ready chatbot systems with the integrations, guardrails, and operational support required to scale reliably.
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RAG-Backed Knowledge Chatbots
Chatbots that answer questions using your own content. Internal documentation, product manuals, policy libraries, and support history. We design the retrieval layer carefully, choose the right vector database, and build the evaluation work that keeps answers accurate as your content grows.
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Agentic Chatbots & Tool Use
Chatbots that do more than answer. They take actions inside your tools: closing tickets, generating reports, processing documents, qualifying leads, and scheduling meetings. Built with the right guardrails, human checkpoints, and audit logs so you can trust them with real work.
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Customer Support & Conversational AI
Support chatbots built to handle real customer volume. Triage, deflection, ticket creation, sentiment routing, and clean handoffs to humans when the conversation calls for it. We integrate with Zendesk, Intercom, Salesforce Service Cloud, or whatever support stack you already run.
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Embedded Chatbots in Your Software
Chatbots that live inside the software your team already uses. A Salesforce copilot, an assistant in your internal CRM, an AI feature inside your SaaS product. Same team for the chatbot and the integration, so nothing gets lost in a handoff.
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Chatbot Operations
The work that keeps a chatbot reliable after it ships. Cost monitoring, prompt versioning, evaluation pipelines, hallucination guardrails, content moderation, and security reviews aligned to SOC 2 or HIPAA where it matters. Less visible than the build, but it’s what keeps a pilot from quietly falling apart.
Problems We Solve
Most chatbot pilots fall apart in the same few places. The retrieval misses the right document. The prompts break on questions nobody thought to test. The bot makes up a policy that doesn't exist. The team has no way to tell when answer quality starts to slip. We've shipped chatbots in regulated industries where a wrong answer has real consequences, so we know where these projects tend to break. We build the systems that catch it early.
How We Build AI Chatbots
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Use Case & Architecture
We start with a two-week engagement to figure out what to build and how. We map the workflow the chatbot will own — support deflection, knowledge retrieval, agentic task completion, or an internal copilot — and decide whether you need RAG, agentic, or a mix of both. We pick the right LLM for the job, design the retrieval and integration architecture, and put together a clear plan. You leave with an architecture diagram, a recommended scope for the proof of concept, and a realistic estimate of what it will take to build.
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Proof of Concept
Next, we build a working prototype with your real data and real conversations. This usually takes two to four weeks. It’s not a scripted demo. It’s a functional pipeline with prompts, retrieval, tool use, and evaluation already in place. Your team uses it. We measure deflection rate, retrieval accuracy, and how well the bot hands off to humans, all against the success criteria we set up front. If it clears the bar, we move forward. If it doesn’t, we tell you why.
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Production Build & Ongoing Support
Once the PoC is solid, we build the production version with the engineering work most teams underestimate: integrations, prompt versioning, evaluation, cost monitoring, observability, content moderation, and security reviews aligned to SOC 2 or HIPAA where it matters. We stay on top of all that after launch. The team that built it is the team that supports it.
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2
weeks
from use case to a validated chatbot architecture
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10x
faster
reporting cycles for an actuarial firm using AI-enhanced workflows
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90%
AI determination accuracy
on clinical claim reviews
Featured AI Projects
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AI-Enhanced Self-Service Hub for Bullhorn
Bullhorn needed to improve efficiency and reduce support overhead across its platform. LaunchPad Lab built an AI-enhanced self-service hub that helps users find answers, navigate workflows, and complete tasks faster without relying on support teams. The result was improved efficiency and a better user experience across the platform.
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AI-Powered Knowledge Assistant for Prosci
Prosci needed a better way for users to access and use its large library of research, training content, and tools. Information was spread across systems and was difficult to search. We built an AI-powered assistant using OpenAI that pulls from Prosci’s content and delivers clear, relevant answers. This improved how users find information and learn from it. The platform now supports over 10,000 users across 50+ countries, with faster access to the content they need.
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AI Workflows for an Actuarial Firm
An actuarial firm was relying on spreadsheets and manual reconciliation, with reporting cycles taking up to 60 days. LaunchPad Lab built an AI-enhanced platform that automates data intake, reconciliation, and reporting. The result was a 10x faster turnaround and a projected $2M annual profit increase.
Frequently Asked Questions
What are AI chatbot development services?
AI chatbot development services are engineering engagements that build conversational software, in text or voice, on top of large language models like Claude, GPT, or Gemini. The work covers picking the right model, designing how the chatbot retrieves information, integrating it with your data and tools, building the evaluation work that keeps it accurate, and running it in production. It’s different from buying an off-the-shelf chatbot tool. You get something built around your workflows and your data, not a generic assistant retrofitted to fit.
What's the difference between a rule-based chatbot and an AI chatbot?
A rule-based chatbot follows a scripted decision tree, while an AI chatbot uses a large language model to interpret what someone actually means and respond accordingly. Rule-based bots work well for simple, predictable flows but break the moment a user asks something unexpected. AI chatbots handle variation much better, but they need evaluation, guardrails, and ongoing monitoring to stay reliable. Most production chatbots end up being a mix of both: scripted flows for known tasks, LLM-powered responses for everything else.
How long does AI chatbot development take?
AI chatbot development timelines depend on what the chatbot needs to do, but they tend to follow a similar pattern. The architecture phase is usually two weeks. A proof of concept takes two to four weeks. A production-ready chatbot most often takes eight to twelve weeks. A full agentic system with tool use and deeper integrations runs four to six months or longer. We give you a clearer estimate after the architecture phase, once we know what we’re actually working with and how clean your data is.
Which LLM should power my chatbot — Claude, GPT, or Gemini?
The right LLM for your chatbot depends on the use case, not the brand. Claude tends to do well on safety-critical reasoning, long context, and structured tool use. GPT has a deep tooling ecosystem and broad capability, and works well in most general-purpose chatbots. Gemini is a strong choice when cost matters, and you need multimodal. For high-volume tasks like routing, smaller models like Haiku or GPT-4o-mini often make more sense than reaching for the bigger one. We’re not loyal to any one provider, and we’ll usually test a couple before committing.
What's the difference between a RAG chatbot and an agentic chatbot?
A RAG chatbot answers questions grounded in your own content, while an agentic chatbot takes real actions in your tools. RAG is the right fit when the goal is to help someone find an answer in your knowledge base, your documentation, or your support history. Agentic is the right fit when the chatbot needs to do something: close a ticket, update a record, or kick off a workflow. Many production chatbots are both. RAG handles the question, agentic handles the task.
How do you handle hallucinations in production chatbots?
Hallucinations are managed with a layered approach, not a single fix. We ground answers in your own content using retrieval, enforce citations so the chatbot has to point to its source, and build in clean refusal handling so the bot says “I don’t know” instead of inventing an answer. We also run an evaluation harness with regression tests every time a prompt or model changes, and we add human-in-the-loop checkpoints anywhere a wrong answer would have real consequences. Hallucinations don’t disappear, but with the right systems around them, they’re catchable.
Can you build chatbots for healthcare, financial services, or legal industries?
Yes. We’ve shipped AI software in healthcare, financial services, insurance, and legal, where compliance, access controls, and audit logging aren’t optional. That includes HIPAA-aligned data handling for healthcare, SOC 2 controls for financial services, and role-based access for legal teams that need to keep certain conversations privileged. Compliance is part of the architecture from the start, not something we retrofit before launch.
Can the chatbot integrate with our CRM, support stack, or internal software?
Yes. Most of the chatbots we build live inside the systems our clients already use. That includes Salesforce, Zendesk, Intercom, Salesforce Service Cloud, custom CRMs, internal apps, and older systems that don’t have clean APIs. For proprietary integrations, we use MCP servers to connect the chatbot to internal tools without rebuilding them. More than a decade of custom software work helps a lot here, especially when the system you’re integrating into is older or messier than you’d like.
Start with a Real Conversation
Tell us what you're trying to build. Whether you're scoping your first chatbot pilot, stuck on retrieval quality or hallucinations in production, or ready to ship something to your customers, a 30-minute call with one of our senior engineers will be more useful than a stack of vendor decks. No deck. No pipeline pressure. Just a conversation.