The questions processors actually ask.

That variability is the whole point. Fixed automation fails because it repeats one motion; Slicium perceives each animal’s anatomy — bone position, seam location, fat/lean boundary — and plans a cut path, blade force and seam-follow specific to that carcass. The cut is validated against a digital twin that predicts yield before the blade moves.

Slicium runs a graduated autonomy model: shadow, then advisory, then supervised autonomy, each gated by measured accuracy and twin validation. Below the confidence threshold the cell degrades to a safe state, escalates to a human, or routes the product to rework — it never guesses on a food-safety decision.

No. The perception and control loop runs entirely on the plant edge. Cloud is used for training, fleet management and reporting, and can be disabled for on-prem or air-gapped deployments. Federated learning shares model improvements without exposing plant recipes, supplier data or tenant images.

A typical pilot runs one line for 8–12 weeks: two weeks of integration and shadow-mode baselining, four to six weeks of advisory operation, then graduated autonomy against the agreed success metric. Connectors for common cutting, grading, X-ray, portioning and MES systems are pre-built. [PLACEHOLDER]

Cells are specified for cold, wet, IP69K washdown environments with daily caustic sanitation. Perception enclosures, cabling and edge compute are selected for condensation, temperature swing and high-pressure cleaning, and validated with your sanitation crew during commissioning.

No. Slicium is the autonomy layer above the equipment you already own. We integrate with robotic cutting cells, graders, X-ray units, portioners and packing systems through APIs, OPC UA and vendor SDKs, and write decisions back into them. New hardware is only needed where perception coverage is missing.

Model families are trained per species and continuously updated from supervised corrections. Drift detection monitors supplier, breed and seasonal changes; when drift crosses a threshold the affected agent degrades its autonomy level and flags for retraining rather than quietly degrading yield.

Typically an OT engineer for network and equipment access, a yield or processing engineer to define specs and validate cut programs, and a quality representative for the food-safety review. Slicium solutions engineers do the commissioning on site.

Yes — that is the default. Shadow mode observes and predicts without acting, so you get a like-for-like comparison against your current performance with zero production risk before anything is automated.

As an append-only, immutable record linking sensor frames, model version, confidence, autonomy level, approver, actuation and realised outcome. It can be retained on-site, exported in HACCP and USDA-FSIS-friendly formats, and queried by lot, carcass or time window.

Three things: validating a cut plan before the blade moves, generating synthetic rare events such as bone chips and contamination that are too infrequent to capture in the field, and gating every model or program change in CI so nothing ships without simulated evidence.

Yes. The full perception and control loop runs on the plant edge with no outbound connectivity required. Model updates arrive as signed artefacts through your own change-control process.

Three tiers: Line at $12,000 per processing line per month for one workflow, Plant at $80,000 per month for the whole loop including the twin, and Enterprise custom agreements for multi-site fleets. Annual prepay saves 15–20%, and outcome-based components can be tied to yield, giveaway, labour or safety.

A typical pilot runs one line for 8–12 weeks: two weeks of integration and shadow-mode baselining, four to six weeks of advisory operation, then graduated autonomy against the agreed success metric. Connectors for common cutting, grading, X-ray, portioning and MES systems are pre-built. [PLACEHOLDER]

In practice, plants redeploy people from repetitive knife work to supervision, quality and exception handling — the roles that are hardest to fill and least likely to cause injury. Given cut-floor vacancy rates, most of our design partners are automating work they cannot staff at all.