Milkeon is small and at the design stage. The roles below are the ones the first design-partner deployments need. They open as the cohort forms; there is no openings count to inflate.
Illustrative scenario: Plant run · skim-milk-powder line + set-yogurt line · shared intake. Not a measured run.
Each role is written as a shape, not a job ad with a requisition number.
Own the camera and analyzer pipeline: contamination, fill and seal vision, NIR and mid-IR composition models, TensorRT and Triton serving on Jetson, and the latency budget that makes a reject decision useful.
Build the connectors that read separators, pasteurizers, vats and dryers and write guarded setpoints back, without ever touching an interlock. Own the allow-lists, rate limits and the audit record of every write.
Build Lactwin: GPU-accelerated simulation of separation, heat exchange, evaporation and spray-drying, surrogates that run at the edge, and the reconciliation that keeps the twin honest against real runs.
Translate between the plant and the platform: define what in-spec means per product, review agent recommendations in assist mode, design the food-safety and ROI gates, and sit with design partners on the floor.
Grounding, audit, human-in-the-loop and graduated control come before any write to a line.
A technologist's correction is a labeled training event, not a complaint.
One line and one number first. The plant and the group follow the number.
Critical control points, interlocks and release decisions stay with the plant's HACCP plan and its people.
Recipes and supplier data never cross a customer boundary. On-prem is an option, not an exception.
Golden datasets and model-as-judge evaluations gate model and prompt changes in CI.
Every Milkeon run is an ordered, inspectable sequence. This is run_scn_01, an illustrative scenario: Plant run · skim-milk-powder line + set-yogurt line · shared intake. Scenario complete · both streams inside spec · one human approval at the dryer.
Plant Orchestrator · Pulled both product specs, the HACCP plan with its pasteurization critical control point, the antibiotic screen for the tanker lot and the standing CIP window from MES and the lab system; locked the target envelope for the run.
Plant Orchestrator · Simulated 24 candidate recipes across separation, pasteurization, evaporation and spray-drying on the Lactwin as-processed twin; ranked them on composition margin, pasteurization margin, energy per kilogram of powder and takt, and discarded every candidate that thinned the pasteurization margin.
Intake-and-Separate · Fused inline NIR and mid-IR with the latest lab calibration, then trimmed the cream-skim split and the standardization blend valve so skim solids and the yogurt-stream fat sat at target with the minimum safe margin rather than the usual over-standardization.
Pasteurize-and-Homogenize · Held HTST at or above 72 °C for 15 s through the holding tube with the flow-diversion valve armed; every critical control point reading was written to the audit log against the HACCP plan.
Ferment-and-Culture · Predicted the acidification curve for the culture lot from inline pH and the upstream composition, held the vat temperature profile and timed the hold so pH landed on the set-yogurt target curve; no abnormal acidification was flagged.
Evaporate-and-Dry · Ran the dryer against the twin's moisture and stickiness prediction; stepped inlet temperature and feed solids toward the minimum-energy setpoint while predicted outlet moisture stayed inside spec and fouling risk stayed low.
Defect-and-Inspect · 16 camera streams across the two packing lines at the plant edge: fill, seal, cap and label checks on every unit, foreign-body and contamination models on the powder stream; two rejects were attributed back to a seal-jaw temperature drift and the packing line was notified.
Hygiene-and-CIP · Placed the pasteurizer and dryer CIP into the cheapest energy window, sequenced wash phases against tank availability and set the release checks that must pass before the next product.
Plant Orchestrator · The twin's lowest-energy candidate wanted a dryer inlet temperature above the site's bounded envelope. Held for the shift dairy technologist, who approved the step with a tighter outlet-moisture alarm.
Quality-and-Conformance · Both batches released: composition, CCP records, culture lot and pH curve, drying setpoints, inspection rejects and every human decision written to the batch genealogy and checked against spec.
Milk is perishable and the lines are continuous, so perception has to be local and fast. Milkeon runs GPU inference at the plant edge and keeps training, simulation and optimization in the cloud, in a private cloud or on-prem. Items marked planned are on the roadmap, not running.
8 to 24 camera and inline-analyzer streams per line. Design target: 50 to 150 ms reject and alert decisions, 250 to 1,000 ms composition estimates, all at the plant edge.
Defect, composition, shelf-life, process-state and dairy-reasoning models behind one orchestrator, with version routing by product, line and sensor package. Design target: 10 to 40 concurrent models per plant.
Vision, spectral, time-series and dairy-reasoning models fine-tuned from labeled batch outcomes, operator corrections, lab results and validated deviations. Initial design target: 2k to 10k GPU-hours.
GPU-accelerated CFD and process simulation of separation, heat exchange, fermentation, evaporation and spray-drying, run as-processed before the batch to find safe, low-energy recipes.
Rare contamination, foreign-body, fouling, seal, fill and drying-upset scenarios generated with domain randomization and always validated against real plant events before use.
Product sequencing, tank allocation, dryer scheduling, CIP timing, energy and water constraints and line balancing, recomputed on the GPU when the plant is disrupted.
Packaging, palletizing and material handling for product, packs and pallets, with perception and motion policies built in simulation before they touch the packing hall.
A plant does not go from manual to bounded control in one step. Milkeon makes the stage explicit per loop, auditable and reversible at any time, and some decisions never leave the person.
| Stage | What the agent does | What the person does | When |
|---|---|---|---|
| Shadow | Predicts composition, contamination, moisture and takt; writes nothing | Runs the plant as today; compares predictions to lab results | First phase with every design partner |
| Assist | Recommends separation, pasteurization, culture and drying moves with the evidence | A dairy technologist or process engineer approves or corrects each one | After accuracy is proven against the baseline |
| Bounded | Writes low-risk separation, drying and inspection setpoints inside explicit limits | Approves anything above the limits; reviews the shift record | After the food-safety and ROI gates agreed up front |
| Exceptions | Escalates anything outside its envelope, CCP-adjacent moves and release decisions | Always decides these; the agent never does | Permanent |
The three people in a dairy plant whose week changes when the process runs itself, and what each of them needs from Milkeon.
Owns production rate, quality, loss and yield, energy and water, and food safety. Wants a plant that holds spec at higher rate with fewer interventions, and a number that justifies the spend.
Owns separation and standardization, pasteurization, culture and drying recipes, process windows and yield. Approves and corrects the agents in assist mode; those corrections train the models.
Owns HACCP, FSMA, ISO 22000 and FSSC conformance, composition and contamination conformance, and batch genealogy. Needs every action grounded, cited and written to an immutable record.
IT/OT and sustainability leads approve the deployment. There are no customer quotes on this site yet; design partners will speak for themselves when they choose to.
Milkeon writes to production equipment in a food plant. Every capability is scoped, every write is policy-checked, every output is cited, and every action is written to an append-only audit log the plant owns.
| Item | Scope | Status |
|---|---|---|
| SOC 2 Type I | Cloud control plane | QUEUED Roadmap, first 6 months |
| SOC 2 Type II | Cloud control plane | QUEUED Roadmap, 6 to 12 months |
| HACCP · FSMA · ISO 22000 · FSSC · Codex | Grounding and record-keeping | RUNNING Design basis for retrieval and the audit log |
| Per-tenant isolation | Data, memory, models | RUNNING Design rule |
| On-prem deployment | Recipe-sensitive producers | RUNNING Design option |
Milkeon supports your food-safety records. It is not a certification and it does not replace your HACCP plan.
The questions plant directors, technologists and quality managers ask in the first meeting.
Only at the autonomy stage your plant has set, and only to an explicit allow-list of low-risk loops inside explicit limits. Every design partner starts in shadow mode, where Milkeon predicts and writes nothing. Assist mode follows, where a technologist approves each recommendation. Bounded autonomy on separation, drying and inspection loops comes after the food-safety and ROI gates agreed up front.
Critical control points are not optimization variables. The pasteurization CCP, the flow-diversion interlock and release decisions stay with your HACCP plan and your people. Milkeon verifies readings, writes them to an immutable audit log and attaches them to the batch genealogy. It supports HACCP, FSMA, ISO 22000 and FSSC records; it is not a certification and does not replace your plan.
Milkeon sits above your existing automation as a supervisory layer. If it stops, the plant keeps running on its current PLC and SCADA control and the interlocks it already has. A Milkeon outage degrades the plant to the way it runs today, not to a stop.
The first phase is shadow mode on one line: Milkeon reads your analyzers, PLC history, lab results and MES while you run the plant as usual, and we measure its predictions against your lab. Recommendations start when accuracy is proven against that baseline, and improve as plant-specific history and technologist corrections accumulate.
Recipes, product specs, tolerance models and supplier data are tenant-scoped and never used to train another customer's models. Retrieval is multi-tenant isolated and permission-aware. On-prem deployment is available for processors protecting sensitive recipes.
You are, the same as with any control strategy. That is why every write is policy-checked, bounded, logged and reversible, every output carries a citation to the recipe or procedure it relied on, and anything above your threshold waits for a named approver. The audit log records the request, the reasoning, the limits applied and the human decision.
As a design partner, on one line, with one hard metric: fat or protein give-away, energy per kilogram of powder, moisture or composition variance, or contamination escapes. We instrument the line in shadow mode, measure the baseline, move to assist mode when accuracy is proven, and expand only when the number holds.
Full multi-module autonomy across the whole plant, long-tail integrations beyond the design partners' stacks, and self-serve onboarding. The first release is one wedge workflow, its connectors, the agent loop with human checkpoints, the review console and the food-safety audit log.
Send what you have built. A person reads it.
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