Tepia builds AI chatbots that answer from your own documents and systems using RAG, with accuracy testing and human escalation.
Tepia builds AI chatbots that answer from your own documents, help center, policies and product data using retrieval augmented generation, with escalation to a human when the bot is unsure.
Every Tepia build runs through six phases, Discovery, Design, Development and Testing, Training, Launch and Support, with a milestone at each stage.
Read: How to Build a Chatbot Trained on Your Own Company Data
Retrieval augmented generation over a vector database, not fine tuning, so the bot answers from current documents and escalates to a human when unsure.
Read: AI Integration Services for Existing Apps
A first chatbot with Tepia typically takes 6 to 10 weeks to launch, and ships with an accuracy test suite and cited answers rather than a promise.
Read: Custom AI Agent Development for Business
PDFs, help centers, Google Drive and SharePoint, CRM and order data and internal wikis, with permission aware retrieval for customer specific answers.
Read: Custom AI vs Off the Shelf AI ToolsIndustry figures Tepia plans around when scoping ai integration work.
Gartner predicts chatbots will become the primary customer service channel for about a quarter of organizations by 2027.
About 83 percent of customers expect to reach someone immediately when they contact a company, per Salesforce's State of Service report.
Gartner projected that 80 percent of customer service and support organizations would be applying generative AI by 2025.
When buyers ask for a chatbot trained on company data, they almost never need model training. They need a chatbot that answers from their content accurately and admits when it does not know. Tepia builds that with retrieval augmented generation: your documents are split into passages, indexed in a vector database, and the relevant passages are handed to the model with each question so the answer is grounded in your material and can cite it.
Retrieval keeps the knowledge outside the model where you can update it in minutes. Tepia reserves fine tuning for tone or format problems, not for knowledge.
The chatbots Tepia builds run on websites, inside iOS and Android apps, in web portals, and in channels like SMS through Twilio. The same retrieval service serves all of them, so your help content is maintained once. Tepia has written a step by step guide to the build at how to build a chatbot on your own data.
A chatbot is only as good as what it can read. Tepia’s Discovery phase inventories every source of truth and decides which ones the bot may use, how often they refresh and who may see what. The table below shows the sources Tepia connects most often.
Permission mapping is the detail that separates a demo from a production bot. Tepia stores the access rules next to each indexed passage and filters retrieval by the identity of the person asking, so an employee chatbot never surfaces an HR document to the wrong person and a customer bot never reveals another customer’s order.
Tepia does not launch a chatbot on the strength of a few good demo questions. During Development and Testing, Tepia assembles an evaluation set of 100 to 300 real questions from your support history with approved answers, then measures the bot on four things: did it retrieve the right passages, is the answer correct, did it cite its source, and did it refuse when the answer was not in the material. The pass rate is reported on every change, and Tepia targets a high grounded accuracy with a near zero rate of confident wrong answers before Beta.
Escalation is designed in from the start. When confidence is low, the question touches a sensitive topic, or the user asks for a person, the bot hands off with a summary of the conversation to your help desk, CRM or a live chat tool, and can create a ticket through your existing system. Tepia also builds feedback buttons and an admin review screen so your team can flag bad answers and add missing content, which is how accuracy improves month over month.
Guardrails include topic boundaries, prompt injection defenses on ingested content, rate limits per user, and logging of every conversation with personal data handling that meets GDPR and CCPA requirements. For healthcare uses Tepia applies HIPAA aware architecture with BAAs, audit logs and encryption at rest and in transit. When a chatbot later needs to take actions rather than answer, see custom AI agent development.
Tepia’s six phase process maps cleanly onto a chatbot build. Discovery is a system investigation of your content sources, support channels and identity systems, user interviews with support staff and a sample of customers, and a third party review of model vendors and hosting options. Deliverables are an Investigation Summary, an Interview Summary and User Stories, which for a chatbot include the top question categories ranked by volume and the escalation rules.
Design covers the conversation itself: greeting, suggested questions, how citations appear, what the user sees when the bot is unsure, and the handoff experience. Tepia produces wireframes and sample designs for the web widget, in app surface or messaging channel, matched to your style guide.
Training teaches your support team to review conversations and update content. Launch rolls the bot out by channel with Tepia support reps watching the first weeks.
Each engagement has an assigned Tepia project manager, US based design and engineering leadership, and hand picked engineers in US overlapping hours. Full process at tepia.co/process.
No code chatbot builders are a reasonable first test, and Tepia will say so when one fits. They stop fitting when you need permission aware answers for signed in users, live lookups into your systems, a handoff into your actual help desk, accuracy you can measure, or a bot inside your own mobile app rather than a third party widget. At that point you want a team that builds the whole thing and owns the quality.
Tepia brings thirteen years of disciplined engineering to that problem and designs chatbots around human intelligence: clear answers, honest refusals and a fast path to a person. Tepia clients report about 90% customer retention, and a support bot that answers well is one of the cheapest ways to protect that number. See Tepia’s work at tepia.co/our-work, services at tepia.co/services, or compare options on custom AI versus off the shelf tools.
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