KINISI

The Object With No Image Doesn't Exist.

Most material held in natural history, archaeological, and archival collections has no image attached to its database record, where a database record exists at all. I've worked in collection environments; this is normal, not exceptional.

An object without a digital record is accessible only to researchers who already know exactly where it is. The expertise to document that material by hand is retiring faster than it's being replaced. The backlog is not getting smaller.

A Robot That Photographs the Same Way Every Time

Consistency of capture is what makes digitisation at volume useful. KINISI runs a fixed, preprogrammed sequence: a 5-axis robot delivering the same angles, the same lighting, and the same order for every object on the rig.

Photography

Multi-angle stacks, merged into focus-stacked stills with full depth of field.

Video

The same sequence run as continuous footage, frame-identical to the stills pass.

3D Models

Photogrammetry or Gaussian Splatting reconstruction from the full angle set.

Swipe for photo, video, 3D →

Where This Actually Got Tested

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Thousands of specimens, sub-millimetre to over 4cm, captured on the same rig without re-tooling, delivered straight into Senckenberg's AQUILA database in months rather than the years a manual equivalent would have taken. I worked on that project myself. The collection happened to be beetles; the job (consistent capture, at volume, integrated into a working database) is identical whether the backlog is fossils, artefacts, or anything else worth keeping a record of.

Different Collections, Same Rig.

Ten Objects an Hour.

KINISI sustains that rate across a working run. Here is what it compounds to over a month:

Illustrative, based on a sustained rate of 10 objects/hour across an 8-hour day, 5-day week. Real collections vary: object size, fragility, and the formats you need all change the maths.

Objects Get Re-examined. Plan For It.

• 01Collections get revisited (a new dating technique, a provenance question, a re-attribution, an exhibition nobody had planned when the object was first catalogued), so the capture has to be good enough to support questions nobody's asked yet.
• 02One master capture, done properly, can feed an archive record, an exhibition label, an AR model, and a published figure without ever re-touching the object.
• 03Delivered as archival masters (TIFF, AVI, FBX) plus ready-to-use compressed copies (JPG, MP4, GLB), benchmarked against FADGI or another named standard if your institution requires it. Full specification in the appendix slides ahead.

Four Steps, Not a Black Box

What "High Quality" Means Here

• 01Photography: 24-megapixel captures, multi-angle stacks merged into focus-stacked stills. A colour reference target is shot once per session, not per object, to calibrate colour across the run; scale is overlaid afterwards from the rig's own real coordinates, not a ruler placed in shot. For larger or flat-format objects (paintings, murals, oversized panels), the same rig stitches multiple passes into a single gigapixel image, so resolution doesn't fall off as object size goes up.
• 023D: texture resolution held at 4K. Mesh density is set per object, not one fixed number, a coin and a cup don't need the same triangle count.
• 03Standards: benchmarked against FADGI, Metamorfoze, or whatever your own imaging officer already works to, agreed before the job starts.

The Questions Your Registrar Will Ask

• 01Handling: done either by me directly or by the institution's own staff, whichever each institution's protocol requires. Every project to date has run with me working alone; no larger team stands behind the rig yet.
• 02Files: masters delivered as TIFF (photo), AVI (video), and FBX (3D), chosen to stay legible in decades rather than years, alongside a JPG/MP4/GLB compressed set for everyday use.

Nothing Stays With Me Afterwards

• 01Ownership: the museum owns everything, raw captures included, not just the processed deliverables.
• 02Database: I don't build to one fixed schema. Senckenberg's integration worked because I adapted to AQUILA's own structure, not the other way round. Direct working experience with Darwin Core for natural history collections; the same schema-agnostic approach applies to CIDOC-CRM or whatever an archaeological collection already runs.

Text You Can Lift Into Your Application

Everything above argues this is worth doing. A funding panel needs a different case, in a different voice. Copy the paragraph below straight into your own application if it helps.