Remote AI Chatbot Development for USA A Dedicated Developer, Hourly or Monthly

A chatbot (this page) answers questions from a knowledge base in a back-and-forth - it retrieves the relevant chunks 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 in Zendesk or Stripe), and one engagement can cover both - the scoping call sorts out which you actually need.

Two ways, billed in USD with a W-8BEN-E on file. 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, a SOC 2-friendly evals pass, or a model swap. Or monthly at the standard $2,000 USD per dedicated developer for 160-172 hours of full-time, exclusive work in your repo and US cloud, on US morning overlap, with a 7-day risk-free trial - the better value when the KB is a rolling thing. 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.

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 (help-centre, product docs, Notion, Zendesk macros, past tickets, RDS 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 wins instead.

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 Service, Salesforce Service Cloud, 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.

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.

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. Eval suites that run on every change and catch a regression before it ships. PII redaction in logs. A SOC 2-friendly audit trail.

Direct integration with your helpdesk - Zendesk, Intercom, Front, Freshdesk, HubSpot Service, Salesforce Service Cloud, or your custom system. The chatbot creates the ticket, attaches the conversation transcript, the retrieved sources, the user's account, and a brief summary, so an agent picks up at the right place. Routing rules on confidence, topic, sentiment, or US business hours.

You own it - your repo, your AWS or Azure or GCP account, your knowledge base, your embeddings, your model keys, your data - from day one. We work inside your accounts, not ours. Model, embedding, and vector DB usage goes against your accounts at cost, not marked up.

Within 48 hours of sign-off: a scoping call on a US-morning slot, a written scope and team proposal, then onboarding - your repo, your cloud, a working local environment - on day one. The first 7 days on the monthly plan are a risk-free trial with a full refund if it is not a fit and a no-cost developer swap inside that window. After that it is monthly billing with 7 days notice to stop, or hourly with stop-any-time - no auto-renewal, no minimum term.