Cut-and-Debone agent
Controls robotic primal cutting, deboning and trimming. Adapts cut path, blade force and seam-following to each animal’s anatomy to maximise saleable yield without bone chips or over-trim.
Products
Six agents. One plant orchestrator.
Each agent owns a workflow end to end — perception, decision and control. The orchestrator sequences them, enforces the safety envelope and writes the assurance trail.
The agents
What each agent does.
Grade-and-Yield agent
Senses and predicts grade, marbling, lean/fat ratio, weight and yield from vision and hyperspectral imaging — before and after the cut.
Detect-and-Safety agent
Fuses X-ray, RGB and hyperspectral streams for foreign-material, defect and contamination detection, plus hygiene and pathogen-risk intelligence.
Portion-and-Pack agent
Controls portioning, slicing, weight-giveaway and fixed-weight batching so every pack lands on target instead of above it.
Robot-and-Handling agent
Drives robotic loading, hanging, handling, packing and palletising in cold, wet, washdown environments rated for daily sanitation.
Yield-and-Throughput agent
Optimises line balance, scrap, giveaway and cold-chain flow across the plant, and flags off-spec risk before it reaches the pack.
Slicium Cut
The agent that holds the knife.
Per-carcass cut planning
Segment anatomy, localise bone, find the seam, then generate a cut path with force and approach angle specific to this animal — validated against the twin before the cell executes.
- Bone, seam and fat/lean segmentation
- Physics-informed cut-response modelling
- Yield-versus-spec trade-off resolution
- Operator correction capture for retraining
Slicium Twin — hit the yield before the cut
Simulate anatomy, cut sequences and yield across thousands of synthetic carcasses. Test a new customer spec without scrapping a shift of product, and gate every model change on twin validation in CI.
- Omniverse-based carcass and cell simulation
- Synthetic rare-event generation
- cuOpt cut-sequence and batch optimisation
- Predicted-versus-realised reconciliation
Product detail
Explore each agent.
Slicium Grade
Consistent, explainable grade and yield prediction from vision and hyperspectral imaging — with confidence bounds, not just a class label.
- Marbling and lean/fat ratio
- Weight and yield prediction
- Species-specific grade standards
- Write-back to MES and pricing
Slicium Detect
Fused X-ray, RGB and hyperspectral inspection that rejects, routes and records — and reduces false rejects that quietly destroy yield.
- Bone, metal, plastic and defect classes
- Deterministic reject timing
- Hygiene and pathogen-risk signals
- Recall evidence graph
Slicium Portion
Predictive weight control across portioners, slicers and fixed-weight batchers, closing the loop with checkweighers.
- Geometry-to-weight prediction
- Batch composition optimisation
- Per-SKU giveaway reporting
- Scrap and rework routing
Slicium Handle
Robotic loading, hanging, handling, packing and palletising in cold, wet, washdown environments where conventional automation corrodes and fails.
- IP69K-rated cells
- Grasp planning for deformable product
- Pack pattern and palletising
- Sanitation-cycle aware scheduling
Slicium Optimise
The agent that runs the whole floor.
Line balance and flow
Sequence product, balance cells, route rework and manage cold-chain flow so the constraint moves where you want it, not where it lands. Optimisation runs on cuOpt against live plant telemetry.
Giveaway reduction
Measured across pilot fixed-weight pack lines. [PLACEHOLDER]
Shift intelligence
Yield and giveaway attribution by shift, cell, program and operator — without turning it into surveillance.
Cold-chain aware
Flow decisions account for temperature exposure and dwell, protecting shelf life alongside throughput.
Off-spec risk flagging
Predict when a run is drifting off spec and intervene before the pack, not after the customer complaint.
Orchestration
How the agents cooperate.
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1
Perceive
Fuse RGB, depth, hyperspectral, X-ray, scale, line-speed and robot-pose streams into one synchronised view of the carcass and the line.
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2
Plan
Compute the cut path, blade force and seam, the grade call, the detection verdict and the portion batch — validated in the twin before the blade moves.
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3
Act
Write back into the robotic cell, portioner, grader, rejector and MES with adaptive, per-carcass control at line speed.
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4
Verify & learn
Measure realised yield, grade, weight and rejects, log the assurance trail, and feed supervised corrections back into training.
One console
Every agent, one pane of glass.
Saleable yield82.4%+4.1% vs baseline
Giveaway0.7%−61% this week
FM rejects120 escapes
Line uptime99.4%+2.2 pts
Line 4 · Front-half deboning1,412 /hr83.1% yieldAutonomous
Line 2 · Breast portioning2,980 /hr0.6% giveawayAutonomous
Line 7 · X-ray screening3,140 /hr4 rejectsAdvisory
Line 1 · Primal fabrication640 /hr78.9% yieldShadow
Illustrative console data from a pilot deboning line. [PLACEHOLDER]
Outcomes
What our design partners measure.
4.1%
Average saleable-yield uplift on pilot deboning lines [PLACEHOLDER]
61%
Reduction in weight giveaway per fixed-weight pack
38 ms
Median foreign-material reject decision at the plant edge
99.9%
Plant-edge runtime uptime target with fail-safe line stop
Connectors
Every agent writes back.
Cutting & deboning
- Robotic primal cutting cells
- Deboning and trimming lines
- Blade force and seam controllers
- Cell safety and E-stop interlocks
Grading & inspection
- Vision and hyperspectral graders
- Carcass grading cameras
- X-ray and metal detectors
- Checkweighers and rejectors
Portioning & packing
- Portioners and slicers
- Fixed-weight batching
- Packing and palletising robots
- Labelling and traceability
In production
Agents on the floor.
“The line does not care that every bird is different — Slicium does. We stopped programming a machine and started supervising an operator that adapts to each carcass.”
“Giveaway was the quiet leak nobody could close. Watching the portioner hold target within a couple of grams, shift after shift, changed the economics of the whole pack line.”
“What sold my team was the audit trail. Every reject links back to the frame, the model version and who approved the autonomy level. That is what a recall investigation actually needs.”
Design-partner quotes are composite and pending publication approval. [PLACEHOLDER]
Governance
Model change control, built in.
SOC 2 Type II (in progress)HACCP-alignedUSDA-FSISEU 853/2004GDPRISO 27001 (planned)
Data residency & on-prem
Run fully on-premise on the plant edge, in your VPC, or hybrid. Sensitive producers can keep every frame inside the facility.
SSO, SAML and RBAC
Enterprise identity, role-based approval gates, and separation of duties between operations, quality and engineering.
Signed, versioned edge OTA
Models and runtimes ship as signed artefacts with staged rollout, instant rollback and twin-validated gating in CI.
Product FAQ
How the agents behave.
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.
Start with one line. Prove the yield.
Run a paid pilot on a single processing line with a defined yield, giveaway or labour success metric. Shadow mode first, autonomy only when the numbers earn it.