Create your AI that knows when to say "I don't know." Try it on your data →
You can make Google Drive and Notion searchable through one AI interface by connecting both sources to a retrieval-augmented AI system that securely ingests, indexes, and semantically searches across them. Instead of switching tools, users query once and receive grounded […]
Integrate it by putting a thin “chat API” layer between your mobile app and your RAG provider: the app sends messages to your backend, your backend calls the RAG chat-completions endpoint, then streams or returns the response to the app […]
You can prioritize documents in a RAG system by applying metadata rules, authority weighting, recency signals, and reranking logic so high-trust documents consistently appear first. Instead of relying only on similarity search, you guide retrieval using approval status, document type, […]
An AI chatbot with multi-source data integrations delivers more complete, accurate, and context-aware answers by pulling information from multiple systems at once such as CRM, help desk, policy docs, cloud storage, and internal knowledge bases. Instead of siloed responses, users […]
Yes, if you treat it like any privileged enterprise app: enforce least-privilege permissions, restrict which channels it can access, and add guardrails against data leakage and prompt-injection. The safest setups combine Slack’s app/security controls with AI-side governance (citations, “don’t answer […]
Your bot should hand off to a human agent as soon as it can’t confidently resolve the issue, or when the topic is high-stakes (refunds, account access, legal/privacy), the customer is upset, or the bot is looping. Set explicit triggers, […]
Building AI for customer service costs far more than model access and a chat UI. The real total cost of ownership (TCO) shows up in knowledge upkeep, integrations, monitoring and QA, security/compliance, and the people needed to keep answers trustworthy. […]
Business process automation (BPA) uses software to run repeatable, multi-step workflows across systems, faster, with fewer manual handoffs and errors. Most teams don’t waste time on a single task. They waste it in the space between tasks: approvals that stall, […]
A coding AI model is usually a large language model trained on lots of code and text. When you prompt it, it predicts the next most likely token (piece of text/code) given your instructions and the surrounding context, producing code […]
Build it as a retrieval + verification system, not just “chat over documents.” Use clean ingestion (versions + permissions), strong retrieval (hybrid + reranking), grounded generation (citations per claim), and continuous evaluation. Reliability comes from controls: what can be answered, […]
