A surprising number of visitor questions are easier to show than to describe. A photo of an error screen, a product on a shelf, a damaged shipment, a rash, a plant, a circuit board. Text-only chatbots make the visitor describe the visual in words, which is slow, lossy, and often impossible. The visitor gives up or escalates to email with the image attached.

SleekAI accepts image uploads in the chat widget, validates the MIME type and dimensions server-side, then routes the image to a vision-capable model alongside the visitor's question. Files are stored in a private subfolder of wp-content/uploads with no public URL. Models like GPT-4o, Claude with vision, and Gemini with vision all work through the same SleekAI provider abstraction using your own API key.

The hard parts are cost and safety. Vision tokens are more expensive than text, so SleekAI auto-resizes large images to the smallest dimensions that preserve usable detail, and caps per-conversation image counts. Some images need a content-safety pass before being sent to a provider. Generic chatbots ship without these guardrails, which means either no image support at all or surprise bills.

Visitors who can show instead of describe finish their question in seconds instead of minutes. That speed compounds. A text-only support flow that takes ten clarifying messages becomes a two-message flow when the visitor uploads the actual error screen.

Multiply that by every support conversation in a month and the savings are real, both for the user and for whoever has to read the transcript later. Image input also opens whole categories of bot that were never feasible in text. Product identification from a photo, visual triage on health or veterinary sites, parts matching on industrial supply sites, and reading whatever a customer photographs on a shelf to suggest the right SKU.

Each of these is a niche where text descriptions either lose detail or are impossible to provide. The hard part is doing it without exploding the bill or compromising privacy. SleekAI handles both by resizing images server-side, stripping EXIF before transmission, capping per-conversation image counts, and keeping the files inside your own WordPress install.

The vision model is just another provider behind the same abstraction, and you keep the same control plane you already have for text. The result is a chatbot that uses vision when it helps and stays out of the way when it does not.