The Brewira platform is a line-edge inference runtime, a plant orchestrator with a policy engine, the Cuvera twin, and a cloud or on-prem training loop. Agents perceive from cameras, inline analyzers, lab data and PLCs, predict gravity, attenuation, carbonation, fill and seal faults, contamination and takt, and act through guarded connectors.
Illustrative scenario: Plant run · one lager batch from brewhouse to can · brewhouse, cellar, filler, tunnel pasteurizer. Not a measured run.
Perceive the wort, blend, beverage and container; plan brew, ferment, carbonate, fill, pasteurize and inspect; run with adaptive recipe and line control; sense and predict flavor, carbonation, fill and seal and contamination; optimize yield, loss, water, energy and takt; flag off-spec and safety risk; log; retrain.
Cameras, inline gravity, CO2, dissolved-oxygen and turbidity analyzers, PLC and SCADA, brewhouse and fermentation systems, filler, seamer, pasteurizer and CIP telemetry, inspection tools, robots, MES and LIMS are normalized into one plant event bus at the edge.
Fine-tuned fill-level, seal, cap, label and foreign-body vision, inline analyzer fusion, fermentation and carbonation models, plus time-series prognostics for brewhouse, filler and pasteurizer equipment, served with TensorRT and Triton.
Retrieves recipes, brewing and carbonation specs, HACCP and CIP procedures with citations, asks the agents for recommendations, runs constraint checks, rehearses high-impact moves on Cuvera and applies the autonomy policy.
Approved actions go back through OT connectors inside tag, rate and magnitude limits. Every action and every brewmaster or quality correction becomes a labeled event for the training loop.
Every Brewira run is an ordered, inspectable sequence. This is run_scn_01, an illustrative scenario: Plant run · one lager batch from brewhouse to can · brewhouse, cellar, filler, tunnel pasteurizer. Scenario complete · batch inside spec · one human approval at the pasteurizer.
Plant Orchestrator · Pulled the recipe, the package spec, the HACCP plan with its pasteurization critical control point, the yeast lot record and the standing CIP window from the beverage MES and the lab system; locked the target envelope for the run.
Plant Orchestrator · Simulated 24 candidate brew, fermentation and carbonation plans on the Cuvera as-produced twin; ranked them on gravity margin, predicted finish time, CO2 target, PU margin, takt and CO2 and energy use, and discarded every candidate that thinned the pasteurization margin.
Brew-and-Blend · Held wort gravity to recipe from the inline gravity analyzer and the brewhouse PLC, trimming the blend and utilization setpoints inside the recipe window rather than correcting after a spot lab check.
Ferment-and-Yeast · Predicted the attenuation curve and finish time for the tank from inline gravity, dissolved oxygen and CO2, pH and temperature; held the temperature ramp and timed the crash inside the brewmaster-approved window; no stuck or runaway fermentation was flagged.
Filter-and-Carbonate · Fused inline CO2, dissolved oxygen, turbidity, temperature and pressure; held carbonation to the package target with the minimum CO2 and timed the filter backwash from the predicted loading instead of a fixed schedule.
Fill-and-Seal · Adjusted fill valves toward the target level inside the approved window using drift feedback from inspection, and ran seam checks on the seamer; the filler corrected upstream instead of rejecting downstream.
Defect-and-Inspect · 16 camera streams at the line edge: fill level, seam and cap, label and date code, foreign body and container checks on every can; three low-fill rejects were attributed to one filler valve and Fill-and-Seal corrected it.
Pasteurize-and-CIP · Held the tunnel pasteurizer on the validated PU profile at the lowest zone energy inside the envelope; every critical control point reading was written to the audit log against the HACCP plan and the CIP release checks were armed.
Plant Orchestrator · The lowest-energy candidate wanted to move the pasteurizer CIP into a window that overlapped the next brand's start. Held for the shift quality manager, who kept the validated PU profile untouched and approved the CIP move with a tighter release check.
Quality-and-Conformance · Batch released: gravity and attenuation curves, yeast lot, CO2 and clarity, fill and seal outcomes, inspection rejects, PU readings, CIP release and every human decision written to the batch genealogy and checked against spec.
Every tool invocation, argument and result is written to an immutable, human-readable log, and every reasoning step is expandable. Nothing about a run is hidden from the plant. The log below is the scenario run, not a measured one.
Goal, plan, thought, action, observation, exactly as the orchestrator would record it.
Brew batch L-0931 to recipe gravity, ferment it to target attenuation, carbonate and fill it to spec at line speed, pasteurize it on the validated PU profile and release it with full genealogy.
Simulate the whole batch on the twin first. Lead with gravity so the cellar gets the right wort and the filler gets the right CO2. Verify the pasteurization critical control point continuously. Let the filler chase target level only inside the approved window; hold anything beyond the envelope for a human.
The standing practice over-fills to avoid low fills and over-gasses to avoid flat product. With continuous fill-level estimation and inline CO2 the filler and the carbonator can sit at target. The saving is giveaway, not safety margin: the pasteurization CCP is never a candidate for optimization.
twin.simulate(candidates=24) → plan #7: gravity on recipe, finish day 5, CO2 at package target, PU margin unchanged, lowest predicted CO2 and energy per hectoliter.
Plan #7 wants the pasteurizer CIP in a window that overlaps the next brand's start. The policy engine routes that single move to the shift quality manager and permits the other seven writes.
Execute plan #7 at the bounded stage: brewhouse blend, tank ramp, CO2 dosing and fill-valve writes proceed; PU is verified continuously; the CIP move waits.
Approval granted at 06:33:44 with the PU profile untouched and a tighter CIP release check. Three low-fill rejects were attributed to filler valve 14 and corrected by Fill-and-Seal inside the approved window.
Scenario complete. The batch is released with gravity and attenuation curves, yeast lot, CO2 and clarity, fill and seal outcomes, PU readings, CIP release and the human decision in the genealogy. No measured plant result is claimed; this is the run the platform is designed to execute.
Containers move at tens of thousands an hour and the lines never stop, so Brewira's inference runs beside the equipment and keeps working if the cloud does not.
| Workload | Where |
|---|---|
| Line vision, analyzer fusion, control inference | Line edge |
| Orchestrator, policy engine, audit log | Plant edge |
| Training and fine-tuning | DGX/HGX, cloud or on-prem GPUs (planned) |
| Cuvera twin and process CFD | OVX-class or cloud GPUs (planned) |
| Synthetic rare-defect generation | GPU cloud (planned) |
| RAPIDS analytics, model governance | Cloud or private cloud |
Cuvera couples CUDA-accelerated simulation of fermentation, carbonation, mixing, pasteurization heat and line flow with the live plant state, so a brew, fermentation and carbonation plan is rehearsed before raw materials, tank time and line hours are spent.
Candidate plans are ranked on gravity margin, finish time, CO2 target, PU margin, takt and CO2 and energy use. Anything that thins the pasteurization margin is discarded before it is ever proposed.
Optimized setpoints are handed to Fermix, Carbyn and Sealix. Distilled surrogates run at the edge for on-line envelope checks.
Twin-versus-actual drift is measured on every batch and recalibrates the simulation. Validated synthetic rare defects harden every model.
Lines fill tens of thousands of containers an hour, so perception has to be local and fast. Brewira runs GPU inference at the line 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 feeds per line through DeepStream pipelines and TensorRT detectors, segmentation, OCR and fill-level models; Holoscan synchronizes cameras with gravity, CO2 and clarity analyzers and filler and pasteurizer telemetry. Design target: sub-100 ms reject decisions.
Vision, time-series, anomaly, process and food-safety reasoning models behind one orchestrator, with version routing by brand, package and line. Design target: 20 to 60 model versions per enterprise tenant.
Line vision, time-series forecasters, anomaly models and NeMo-based beverage reasoning fine-tuned from labeled batch outcomes, brewmaster corrections, lab results and release decisions. Design target: weekly retraining for wedge models, monthly for validated process models.
The Cuvera as-produced line twin: CUDA-accelerated fermentation, carbonation, mixing and pasteurization heat simulation run before the batch, for new SKU setup and batch planning when a trial is expensive.
Rare leaker, low-fill, overfill, cap-skew, seam, label-shift, condensation, glare and foreign-body scenarios generated with domain randomization and validated against real plant events before use.
Brew and fill schedules, changeovers, CIP cycles, water and energy allocation and takt balancing under line, tank, product and sanitation constraints, recomputed on the GPU when the plant is disrupted.
Image, time-series and batch-genealogy ETL for feature generation, quality correlation and cross-plant benchmarks on the GPU.
Pack, case and palletizing handling, with perception and motion policies built and validated in simulation before they move a pallet.
A plant does not go from manual to bounded control in one step. Brewira 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 gravity, attenuation, carbonation, fill and seal faults, contamination and takt; writes nothing | Runs the plant as today; compares predictions to lab, QA and PLC outcomes | First phase with every design partner |
| Assist | Recommends bounded brew, fermentation, carbonation, fill and seal, pasteurization and CIP moves with the evidence | A brewmaster, process engineer or quality manager approves or rejects each one | After accuracy is proven against the baseline |
| Bounded | Writes low-risk filling, carbonation and inspection setpoints inside documented recipe, quality, food-safety, equipment and rollback 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 |
Two agents will want the same actuator. The orchestrator arbitrates on the run goal, with food safety above everything, and the handoff is logged like any other step.
Yield-and-Takt wants more belt speed through the tunnel pasteurizer so the filler is not held back and the shift meets takt.
Pasteurize-and-CIP requires the belt speed that keeps every container inside the validated PU profile. That is the HACCP critical control point.
Food safety outranks takt under every run goal. The critical control point wins the actuator; the request for more speed is logged with its score and the reason it lost.
Yield-and-Takt recovers the schedule by pulling the next changeover earlier and shifting the CIP window instead. Takt is met without touching the CCP.
Each agent owns a stage of the plant, a bounded tool set and a measured outcome. They negotiate for shared actuators through the orchestrator, never directly. Five ship as products; the rest arrive with the Plant tier, and the knowledge agent has no write tools at all.
Brewhouse, blending, syrup, gravity, recipe
Reads inline gravity, flow and temperature against the recipe and holds mash or blend, syrup ratio and utilization at target, so wort and blend leave the brewhouse on gravity and on flavor spec instead of drifting between spot lab checks.
Design scope: gravity and recipe on target, utilization held
Ships in: Plant tier
Fermentation, yeast and culture, attenuation, flavor profile
Fuses inline gravity, dissolved oxygen and CO2, pH, temperature and pressure with a physics-informed fermentation model to predict each tank's attenuation curve, flavor profile and finish time, then holds temperature ramps, pressure and pitching, cropping and harvest inside brewmaster-approved windows.
Design scope: attenuation and flavor on target, less tank time
Ships in: Fermix
Filtration, clarity, carbonation with CO2 or nitro
Fuses inline CO2 and dissolved oxygen, turbidity, temperature, pressure and flow to predict in-line carbonation and clarity, then controls dosing, pressure and filtration inside spec windows to hold CO2 and clarity to target with the least CO2 wasted.
Design scope: CO2 and clarity to spec at minimum CO2 waste
Ships in: Carbyn
High-speed filling, fill level, seaming, capping, sealing
Takes drift signals from inspection and adjusts fill valves, carbonation pressure at the filler, seam checks and capper settings inside approved windows, so the filler corrects toward target level instead of over-filling to avoid low fills.
Design scope: target fill level with minimal giveaway, no leakers
Ships in: Sealix
Tunnel and flash pasteurization, PU, CIP, hygiene
Monitors and controls tunnel and flash pasteurization to the target pasteurization units at minimum energy and thermal flavor impact, verifies CIP hygiene from sensor and analyzer signals, writes every critical control point reading to the audit log and holds release until the evidence is complete.
Design scope: target PU at minimum energy, CIP release proven
Ships in: Pastura
Fill level, seal and cap, label, foreign body, clarity, container faults
Runs multi-camera vision at the line edge for fill-level estimation, seam and cap integrity, label placement and OCR, clarity, container defects and foreign bodies, rejects off-spec containers and attributes each defect back to the filler, seamer or upstream state that caused it.
Design target: sub-100 ms reject decisions, sub-250 ms drift alerts
Ships in: Sealix
Flavor, clarity, carbonation, shelf life
Predicts shelf life and finished-product quality from gravity, attenuation, carbonation, PU margin and inspection outcomes, and proposes the recipe and process moves that protect both before the batch is released.
Design scope: quality and shelf life predicted before release
Ships in: Plant tier
Yield, loss, water, energy, line balancing
Prices every candidate move in hectoliters, kilograms of CO2, kilowatt-hours and cubic meters of water, sequences brews, tanks, changeovers and CIP cycles, and balances the moving line so takt is met without starving the filler or the pasteurizer.
Design scope: yield up, loss, water and energy down
Ships in: Plant tier
Pack, case and palletizing handling
Drives pack, case and palletizing handling of containers, packs and pallets with perception and motion policies validated in simulation first, inside safety zones, with audited actions and operator approval. It does not claim plant-wide robotic labor.
Design scope: constrained handling, safety zones, approvals
Ships in: Plant tier
Right-first-time, non-conformance, batch genealogy, traceability
Builds the batch genealogy record, gravity and attenuation curves, CO2 and clarity, fill and seal outcomes, PU readings, CIP release checks and every human decision, and gates release against product spec and the plant's food-safety procedures.
Design scope: complete, immutable release records
Ships in: Plant tier
Grounded process and food-safety questions
Answers brewers', process engineers' and quality managers' questions over recipes, brewing, fermentation and carbonation specs, HACCP, FSMA, FSSC 22000 and BRCGS procedures and CIP records, with a citation on every answer. It has no write tools and cannot act on the plant.
Design rule: every answer cites a source; no write tools
Ships in: Every tier
Planning, arbitration, human approval, twin rehearsal
Plans the run, rehearses high-impact moves on the Cuvera twin, arbitrates between agents that want the same actuator, enforces the autonomy stage per loop and routes anything above the site threshold to a named human. Grounds its reasoning in the plant's recipes, HACCP plan and CIP procedures through cited retrieval.
Design rule: every write policy-checked, every output cited
Ships in: Every tier
The numbers Brewira is engineered against, from the GPU utilization plan.
Design targets from the GPU utilization plan, all marked aspirational in the architecture. Nothing on this page is a measured plant result; measurements start when the first design-partner line runs in shadow mode.
There is no SDK to download yet. What design partners get is the integration surface: connectors, an autonomy policy declared per loop, and an audit record for every reading, recommendation, write and approval. This is the shape of one record, illustrative.
// one write, as the audit log would hold it
{
"run": "run_scn_01",
"step": "fill.seal",
"agent": "agent.fill_seal",
"stage": "bounded",
"action": { "tool": "plc.write_setpoint", "tag": "FIL1.VALVE_14" },
"limits": { "max_step": "declared per tag", "per": "declared per tag" },
"evidence": ["vision.stream", "filler.read_telemetry"],
"citation": "spec:CAN-330 fill-level v3 §2",
"policy": "permitted",
"models": { "fill_level": "versioned", "policy": "versioned" },
"human": null,
"outcome": "on target level · inside approved window"
}The three people in a beverage plant whose week changes when the process runs itself, and what each of them needs from Brewira.
Owns production rate, quality, loss, giveaway and yield, water and energy, and food safety. Wants a plant that holds spec at higher rate with fewer interventions, and a number that justifies the spend.
Owns recipes, brewhouse and blend, fermentation and carbonation process windows and yield. Approves and corrects the agents in assist mode; those corrections train the models.
Owns product quality and safety under HACCP, FSMA, FSSC 22000 and BRCGS, contamination and fill and seal 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.
Brewira reads and writes through the systems already on the floor. No parallel historian, no new HMI, no rip-and-replace. Connectors are built against each design partner's stack first; long-tail integrations are out of scope for the first release.
PLC and SCADA of brewhouse, blending and syrup systems, fermentation tanks and yeast handling
Setpoint reads, guarded writes, interlocks untouched
Filtration and carbonation skids and their inline CO2, dissolved-oxygen and turbidity analyzers
Continuous carbonation and clarity state
High-speed filler, seamer and capper controls
Telemetry in; fill-valve and seam settings inside approved windows
Tunnel and flash pasteurizers, CIP skids and hygiene sensors
PU readings verified and logged; CIP release checks
Multi-camera stations for fill level, seal and cap, label, foreign body and container defects
Frames, defect events, reject commands
Orders, recipes, product specs, batch genealogy, LIMS results
Targets in, release records out
Plant and asset meters; Isaac-driven pack, case and palletizing cells
Hard constraints for scheduling; bounded handling
SSO and role-based access; SOC 2 on the roadmap
Named approvers, scoped roles
Brewira 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 · FSSC 22000 · BRCGS | 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 |
Brewira supports your food-safety records. It is not a certification and it does not replace your HACCP plan.
One policy model, one audit trail and one set of yield, loss, giveaway, water and energy benchmarks across every plant in the group, with each site's recipes and tolerance models kept private to that site.
The questions plant directors, brewmasters 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 documented limits. Every design partner starts in shadow mode, where Brewira predicts and writes nothing. Assist mode follows, where a brewmaster or process engineer approves each recommendation. Bounded autonomy on filling, carbonation and inspection loops comes after the food-safety and ROI gates agreed up front.
Critical control points are not optimization variables. The pasteurization PU profile, the CIP release checks and the batch release decision stay with your HACCP plan and your people. Brewira verifies readings, writes them to an immutable audit log and attaches them to the batch genealogy. It is grounded in HACCP, FSMA, FSSC 22000 and BRCGS procedures; it is not a certification and does not replace your plan.
Brewira 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 Brewira outage degrades the plant to the way it runs today, not to a stop.
The first phase is shadow mode on one line: Brewira reads your analyzers, PLC history, lab results and MES while you run the plant as usual, and we measure its predictions against your lab, QA and PLC outcomes. Recommendations start when accuracy is proven against that baseline, and improve as plant-specific history and brewmaster 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. Cross-plant learning, where a customer opts in, is federated and IP-isolated. On-prem deployment is available for producers 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: fill giveaway and low-fill rejects, leaker and seal faults, attenuation variance and tank time, CO2 use and clarity rejects, or water and energy on pasteurization and CIP. 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 assurance-grade food-safety audit log.
One line, one baseline, shadow mode first. We will show you the run record before anything is written.
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