Hoop Interactive builds LLM-powered chatbots grounded in your actual data through retrieval-augmented generation, integrated with your systems, and evaluated for accuracy before a single real user talks to them.
AI chatbot development is the process of building a conversational system that understands natural language, retrieves relevant information, and responds accurately - instead of following a rigid decision tree. Modern chatbots run on a large language model such as GPT-4o or Claude, paired with retrieval-augmented generation, which searches your actual documents before the model generates an answer.
Not every chatbot needs the same architecture. A simple FAQ bot with 30 to 50 fixed questions works fine on decision-tree logic. A chatbot that needs to answer from your product documentation, policies, or internal knowledge base needs RAG to ground its answers in real content. Choosing the wrong architecture is the single biggest reason chatbot projects go over budget or underperform.
Three chatbot architectures, and the use case each one genuinely fits.
| Architecture | What it is | Best fit |
|---|---|---|
| Rule-based | Fixed decision trees answering a defined set of questions. | Simple, well-defined FAQ scenarios with 30 to 50 questions. |
| NLP / intent-based | Recognizes user intent and routes to pre-written responses. | Structured support flows with moderate variation in phrasing. |
| LLM + RAG | Retrieves relevant documents, then generates a grounded, natural response. | Open-ended questions against a real knowledge base or product catalog. |
Why retrieval made modern chatbots viable, and the choice between retrieving and retraining.
Before RAG, a chatbot answering from a language model alone could confidently state incorrect information, since the model has no way to check its answer against your actual content. RAG retrieves the relevant document first, then asks the model to answer using only that content, which sharply cuts fabricated answers and lets the chatbot cite its source.
Use RAG if your chatbot needs to answer from a knowledge base that changes over time - documentation, policies, product catalogs. Use fine-tuning if the task is narrow and needs a very specific tone, format, or terminology more than broad knowledge retrieval.
A chatbot that answers from your real content, with sources attached, earns trust and actually resolves conversations instead of escalating them.
60 to 80% of routine, repetitive queries handled without a human.
Responses grounded in retrieved source documents, not model guesswork.
Questions get resolved outside business hours without extra headcount.
Query volume grows without licensing cost growing alongside it.
People handle the complex cases that actually need a person.
Expanding the knowledge base directly improves what the bot can answer.
We scope the engagement around your use case and existing systems, not a fixed template.
Chatbots that answer product and policy questions from your documentation, with escalation to a human when needed.
Employee-facing assistants that search across Confluence, Notion, SharePoint, and internal policy documents.
Chatbots that qualify inbound leads, answer product questions, and route hot leads to your sales team.
Phone and mobile voice assistants built on the same LLM and RAG architecture as our text chatbots.
The same chatbot deployed across your website, WhatsApp, Slack, and SMS from one backend.
Migrating a rule-based or NLP chatbot to LLM and RAG architecture without starting the project from zero.
Four situations send most product and support leads to us for chatbot work.
The same 20 questions get asked hundreds of times a week, and your team answers each one manually.
An existing rule-based or ungrounded chatbot frustrates users with generic or incorrect responses.
Answers live across dozens of documents nobody has time to search manually for every question.
Customer or employee questions come in outside business hours, and no one is there to answer.
These five mistakes account for most of the over-budget or unreliable chatbot projects we get asked to fix.
Trying to automate every use case at once delays launch and inflates cost. We start with the highest-volume use case, validate it, then expand.
Fine-tuning costs far more than RAG and solves a different problem. We default to RAG unless the use case genuinely needs fine-tuning.
A chatbot with no handoff traps frustrated users in a loop. We build clear escalation logic into every chatbot.
An ungrounded chatbot confidently states wrong information. We ground every answer in retrieved documents with confidence scoring.
Shipping without testing accuracy against real questions means users find the failures first. We run a structured evaluation before any live traffic.
Six stages, from first call to a live, evaluated chatbot. We work in weekly sprints with a demo every Friday.
We define the highest-volume question set and pick the architecture - rule-based, NLP, or LLM with RAG - that fits it.
1–2 weeks · DiscoveryWe ingest, chunk, and index your documents so retrieval returns the right passage for each question.
1–4 weeks · Data PipelineWe select the language model, design the retrieval pipeline, and define escalation and confidence-scoring logic.
1–2 weeks · ArchitectureWe build in one-week sprints with a working demo every Friday, refining prompts against real test questions.
2–14 weeks · DevelopmentWe test accuracy against a real question set, measure hallucination rate, and tune before any live traffic.
1–3 weeks · EvaluationWe deploy, set up conversation monitoring, and hand over a documented, commented codebase.
Ongoing · MonitoringThree factors drive the price: architecture choice, knowledge base size, and integration count. Ongoing LLM API usage typically runs $200 to $5,000 per month, depending on volume.
Proven, well-documented tools, chosen for accuracy and long-term maintainability.
Pick the model that fits your project and team. All four include weekly demos and full code ownership.
A defined use case, timeline, and price agreed before we start. You know the exact cost up front.
Best for fixed budgetsAI engineers and a solutions architect working as an extension of your team through launch and beyond.
Best for larger buildsA senior AI engineer added to your existing team to close a skills gap or add build capacity.
Best for existing teamsOngoing prompt tuning, knowledge base updates, and monitoring for a chatbot already live, billed monthly.
Best for live chatbotsNo hidden gaps. Each engagement includes everything you need to launch and maintain the chatbot.
The process stays the same. The knowledge base and compliance needs change by industry.
Product support bots grounded in documentation and release notes.
Order status, product, and return-policy chatbots.
Patient FAQ and scheduling assistants built for compliance.
Account and policy chatbots with strict grounding requirements.
Listing and inquiry assistants that qualify leads automatically.
Student support bots answering enrollment and policy questions.
Internal knowledge bots for legal, consulting, and agency teams.
Booking and itinerary assistants across web and messaging channels.
Real work, real numbers, see how we did it for BeesApp.
99.9%
BeesApp came to us with a broken patchwork of PHP, Django, and Vue.js. We tore it down and rebuilt the entire system from scratch using Next.js, Flutter, and FastAPI - delivering 99.9% uptime, 120+ API endpoints, and enabling market entry in Saudi Arabia.
120+
API Endpoints
74%
Faster Load
40%
Lower Costs
AI chatbot development is one of six AI services we cover, under AI Development.
The full AI service this sits under.
ExploreOpenAI models wired into your existing product and workflows.
ExploreTask-executing assistants beyond question answering.
ExploreCustom models trained on your own data.
ExploreForecasting churn, demand, and revenue from historical data.
ExploreInternal AI tooling built around your specific workflows.
ExploreThe API layer your chatbot reads and writes through.
ExploreBackend services behind retrieval and conversation state.
ExploreThe questions product and support leads ask us most before starting a chatbot build.
An off-the-shelf platform such as Intercom Fin or Zendesk AI works well for standard customer support on a platform you already use. A custom chatbot makes sense if you need it to search proprietary data, integrate deeply with internal systems, or handle a use case those platforms don't cover.
AI chatbot development costs $5,000 to $250,000 or more, depending on architecture. A rule-based or simple FAQ bot costs $5,000 to $20,000, an LLM chatbot with RAG costs $20,000 to $100,000, and an enterprise AI agent costs $100,000 to $250,000 or more.
Building an AI chatbot takes 2 to 24 weeks or more. A simple FAQ bot takes 2 to 6 weeks, an LLM chatbot with RAG takes 8 to 14 weeks, and an enterprise AI agent takes 14 to 24 weeks or longer.
RAG (Retrieval-Augmented Generation) retrieves relevant information from your documents before the language model generates a response, grounding answers in your actual content instead of the model's general training data. It matters because it sharply reduces incorrect or fabricated answers compared to an ungrounded chatbot.
A well-built RAG chatbot rarely hallucinates, because it answers from retrieved source documents rather than guessing. We add confidence scoring and source citations, so low-confidence answers get flagged instead of stated as fact.
Yes. We integrate chatbots with Salesforce, HubSpot, Zendesk, and most CRM or helpdesk platforms, so conversations create tickets, update records, and pull customer context automatically.
Yes. We build voice-enabled assistants for phone and mobile app use cases, using speech-to-text and text-to-speech layered on the same LLM and RAG architecture as our text chatbots.
RAG retrieves relevant documents at answer time without changing the underlying model. Fine-tuning retrains the model on your data, which costs more and suits narrow, high-volume tasks needing a consistent tone or format rather than broad knowledge retrieval.
Yes. We build escalation logic that hands off to a human agent when the chatbot's confidence is low, the user asks for a person, or the query falls outside the chatbot's defined scope.
Yes. You own 100% of the code, prompts, and configuration, delivered in a documented repository with no vendor lock-in beyond the LLM API provider itself.
Yes. We offer maintenance retainers covering prompt tuning, knowledge base updates, and performance monitoring for a chatbot already live.
Yes. We sign an NDA before the discovery call, before you share any product or business data with us.
OpenAI GPT-4o
Anthropic Claude
Google Gemini
Pinecone
LangChain
Node.js
AWS
