Convert PowerPoint decks to images + JSON for LLM pipelines
Vision models and RAG pipelines don't read .pptx — they read
images and text. Tekilio Frames produces both in the right shape:
per-click-state PNGs plus structured notes,
keyed by slide so they join cleanly.
Prepare a deck for your pipeline →
Why this output fits multimodal models
Feeding a model one flattened image per slide throws away the build. For a tutorial, a data walkthrough, or any deck where points appear in sequence, the order things were revealed is the signal. Tekilio Frames gives the model each state as its own image, so it can reason about step 2 without step 3's content leaking in. And because real PowerPoint does the rendering, the images look like the slides — no headless layout drift to confuse the model.
The image + text pairing
Alongside the PNGs you get notes.json keyed by slide number — the
presenter's own narration, which is high-quality ground-truth text for captioning,
embedding, or grounding answers. Each slide-NNN-step*.png shares its
NNN with a notes.json key, so assembling image+text pairs
is a join on the slide number.
A minimal pipeline
- Upload the deck to Tekilio Frames and download the result zip.
- Unzip; list
slide-NNN-stepN.pngand parsenotes.json. - For each slide number, pair its PNG(s) with its note.
- Send the image to your vision model with the note as context; embed or index the result for RAG.
Related
For the mechanics of the image export see animation states to images and PPTX to PNG; for the text side see exporting speaker notes as JSON.
FAQ
- How do I convert a PowerPoint to images for an LLM?
- Upload the .pptx to Tekilio Frames and download the zip of PNGs plus notes.json. Feed each PNG to a vision model and pair it with the matching slide's note from notes.json as ground-truth narration.
- Why per-click-state instead of one image per slide?
- Each animation build is a distinct visual the model should reason over separately. A single flattened image hides the sequence; per-state images let a vision model see what was revealed at each step, which matters for tutorials, walkthroughs, and data builds.
- What do I feed a vision model from a deck?
- The per-state PNG as the image input and the slide's speaker note as accompanying text. Tekilio Frames produces both, keyed by the same slide number, so building image+text pairs is a simple join.