Datapro-V™: The DNA of your supply chain process

Datapro-V™ is our core proprietary patented framework and technology that grounds every decision the AI makes to the DNA of your process and KPIs that matter to your business.

Receive
Local step
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Receive
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No employees
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Putaway
Workflow
ID: 0
: 1 Putaway
KPI parameters 0
Pick · Zone C
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Pick · Zone C
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Replenish
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Pack
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: 1 Pack
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Your value stream, mapped node by node.

Every step — person, system, machine — on one live canvas. Not a diagram: the ground truth every insight stands on.

Vendor Booking Confirmation
Local step
ID: 5
97.1%
Vendor Booking Confirmation
Metadata 6
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
+1
Albion ASN Portal Slot Booking Calendar
EDI handover Customer SLA-A
KPI parameters 4
Time
Quality
Risk
Cost
The KPIs that matter to your business, on the step where they happen.

Baselines defined by your standards, not industry averages.

Pick · Zone C
Local step
ID: 0
Pick · Zone C
Metadata 0
No employees
No attributes
No tags
KPI parameters 0
Op #T-114 · temp
Zone C · Pick Area
SKU 4421–4438
Device 07
A knowledge graph of your operation.

Who owns what, what connects to what — so every answer is specific, never generic.

Datapro-V™

The live canvas of your operation.

Your operation mapped node by node — every step, every owner, every system on one live canvas. This is the process map every DNAI™ insight is grounded in.

Organisation / / WM Warehouse
Main workflow 1 Inbound & Receiving
1 0 0 %
Gate-In Inspection
Local step
ID: 2
98.6%
Gate-In Inspection
Metadata 5
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
Gatehouse Booking Desk ANPR Camera Array
Inbound Yard SLA-A
KPI parameters 3
Time
Quality
Cost
Unload & Dock Transfer
Local step
ID: 3
88.7%
Unload & Dock Transfer
Metadata 6
Sarah Mitchell Sarah Mitchell
Viktor Schultz Viktor Schultz Mei Tanaka Mei Tanaka
+1
Dock Door 7 — Leveller Reach Truck RT-114
+2
Dock Bottleneck-watch
KPI parameters 4
Time
Quality
Risk
1
Cost
Quality Check
Local step
ID: 4
96.2%
DNAI DNAI 2 insights
Quality Check
Metadata 5
Aisha Rahman Aisha Rahman
Tomasz Nowak Tomasz Nowak
QA Handheld — Bartec Inspection Checklist v3
GFSI-audited Hold-on-fail
KPI parameters 3
Time
Quality
Risk
Putaway & Replenishment
Workflow
ID: 5
97.4%
: 2 Putaway & Replenishment
WM Warehouse
KPI parameters 3
Time
Cost
Sustainability
DNAI™

Intelligence grounded in your operation.

Not a generic AI assistant. DNAI™ reads from your VSM, your KPI thresholds, and your Knowledge Graph — so every answer is specific to your process, your people, your data.

KPI Guard monitors every published VSM continuously. Alerts fire only when they're real — tuned against your baselines, never a wall of red. Each alert appears as a badge on the process step where it is occurring, not in a separate dashboard.

/ The badge opens an Insight Card — not just a number, but a ranked list of contributing drivers with data lineage citations and a confidence rating.

/ Recommend-and-confirm. Every action awaits supervisor approval — explicitly not autonomous.

A persistent chat interface on every page. Ask a question in plain language — DNAI™ translates it into a query against your data warehouse and Knowledge Graph, returns the answer with source citations, and stays on context across turns. Use @ to reference any VSM, employee, org unit, or asset directly.

/ Every answer cites which data it used — so you can verify the reasoning, not just accept the conclusion.

Copilot edits the map through conversation — and nothing publishes without sign-off.

Organisation / / WM Warehouse
Main workflow 1 Inbound & Receiving
1 0 0 %
Gate-In Inspection
Local step
ID: 2
98.6%
Gate-In Inspection
Metadata 5
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
Gatehouse Booking Desk ANPR Camera Array
Inbound Yard SLA-A
KPI parameters 3
Time
Quality
Cost
Unload & Dock Transfer
Local step
ID: 3
88.7%
DNAI DNAI New Insight
Unload & Dock Transfer
Metadata 6
Sarah Mitchell Sarah Mitchell
Viktor Schultz Viktor Schultz Mei Tanaka Mei Tanaka
+1
Dock Door 7 — Leveller Reach Truck RT-114
+2
Dock Bottleneck-watch
KPI parameters 4
Time
Quality
Risk
1
Cost
Quality Check
Local step
ID: 4
96.2%
DNAI DNAI 2 insights
Quality Check
Metadata 5
Aisha Rahman Aisha Rahman
Tomasz Nowak Tomasz Nowak
QA Handheld — Bartec Inspection Checklist v3
GFSI-audited Hold-on-fail
KPI parameters 3
Time
Quality
Risk
Putaway & Replenishment
Workflow
ID: 5
97.4%
: 2 Putaway & Replenishment
WM Warehouse
KPI parameters 3
Time
Cost
Sustainability
Organisation / / WM Warehouse
Main workflow 1 Inbound & Receiving
1 0 0 %
Gate-In Inspection
Local step
ID: 2
98.6%
Gate-In Inspection
Metadata 5
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
Gatehouse Booking Desk ANPR Camera Array
Inbound Yard SLA-A
KPI parameters 3
Time
Quality
Cost
Unload & Dock Transfer
Local step
ID: 3
88.7%
Unload & Dock Transfer
Metadata 6
Sarah Mitchell Sarah Mitchell
Viktor Schultz Viktor Schultz Mei Tanaka Mei Tanaka
+1
Dock Door 7 — Leveller Reach Truck RT-114
+2
Dock Bottleneck-watch
KPI parameters 4
Time
Quality
Risk
1
Cost
Quality Check
Local step
ID: 4
96.2%
DNAI DNAI 2 insights
Quality Check
Metadata 5
Aisha Rahman Aisha Rahman
Tomasz Nowak Tomasz Nowak
QA Handheld — Bartec Inspection Checklist v3
GFSI-audited Hold-on-fail
KPI parameters 3
Time
Quality
Risk
Putaway & Replenishment
Workflow
ID: 5
97.4%
: 2 Putaway & Replenishment
WM Warehouse
KPI parameters 3
Time
Cost
Sustainability
1 0 0 %
Gate-In Inspection
Local step
ID: 2
98.6%
Gate-In Inspection
Metadata 5
James O'Sullivan James O'Sullivan
Rachel Patel Rachel Patel Daniel Cooper Daniel Cooper
Gatehouse Booking Desk ANPR Camera Array
Inbound Yard SLA-A
KPI parameters 3
Time
Quality
Cost
Unload & Dock Transfer
Local step
ID: 3
88.7%
Unload & Dock Transfer
Metadata 6
Sarah Mitchell Sarah Mitchell
Viktor Schultz Viktor Schultz Mei Tanaka Mei Tanaka
+1
Dock Door 7 — Leveller Reach Truck RT-114
+2
Dock Bottleneck-watch
KPI parameters 4
Time
Quality
Risk
1
Cost
Quality Check
Local step
ID: 4
96.2%
DNAI DNAI 2 insights
Quality Check
Metadata 5
Aisha Rahman Aisha Rahman
Tomasz Nowak Tomasz Nowak
QA Handheld — Bartec Inspection Checklist v3
GFSI-audited Hold-on-fail
KPI parameters 3
Time
Quality
Risk
Putaway & Replenishment
Workflow
ID: 5
97.4%
: 2 Putaway & Replenishment
WM Warehouse
KPI parameters 3
Time
Cost
Sustainability
Cross-Dock Staging
Local step
ID: 6
96.2%
Cross-Dock Staging
Metadata 5
Aisha Rahman Aisha Rahman
Tomasz Nowak Tomasz Nowak
QA Handheld — Bartec Inspection Checklist v3
GFSI-audited Hold-on-fail
KPI parameters 0

Turn recurring incidents into permanent improvement.

DNOVA™ — the Process Intelligence & Orchestration Engine. Where DNAI™ fixes today's incident, DNOVA™ reads the pattern across many incidents and makes the cause impossible to repeat — process-owner approved, codified into your operation.

  • The same equipment hold cannot recur.

    After eight weeks of DNAI™ incident data, DNOVA™ identifies: Equipment E-06 calibration events correlate with batch yield suppression at 91% confidence across 7 incidents.

    DNOVA™ proposes three changes — an automated pre-batch calibration check for E-06 before Line 2 assignments, a dynamic line routing rule (if E-06 calibration age exceeds 48 hours, auto-assign batch to Line 3), and a maintenance interval reduction from 14-day to 10-day for the E-06 class. Awaits process owner and QA sign-off.

    On approval, rules are codified in Datapro-V™ and executed automatically for all future batches. E-06-related batch holds eliminated. DNOVA™ loop restarts on remaining drivers.

    Pharma & Medtech Read more
  • The same incident cannot recur.

    After six weeks of DNAI™ data, DNOVA™ identifies the pattern: 73% of Zone C pick errors occur during temp-operator shifts on look-alike SKU clusters.

    It proposes three changes — a mandatory scan-confirm rule for all 44xx SKUs, an automated shift-start briefing for temp operators in Zone C, and a packaging differentiation flag in the Knowledge Graph.

    Process owner approves. Rules deploy. Chronic issue eliminated. The improved baseline feeds back into Datapro-V™.

    3PL & Contract Logistics Read more
  • The same cross-channel stockout cannot recur.

    After eight weeks of DNAI™ data, DNOVA™ identifies the pattern: cross-channel stockouts for priority SKUs (1102–1120 class) occur when Carrier R-02 or R-07 run more than 90 minutes late and online channel sell-through exceeds 40% of available stock.

    DNOVA™ proposes an automated cross-channel stock reallocation rule — triggers when both conditions are met, reallocates online-channel stock to the shared pool, notifies store replenishment and online fulfilment simultaneously.

    Merchandising director approves. Rule codified and deployed. Next R-02 delay: no stockout. DNOVA™ monitors for new trigger conditions.

    Omnichannel Retail Read more

Automation design codifies each validated action into a standing rule — every rule gated by human sign-off, never autonomous.

Want the mechanics? Architecture, the KPI catalogue, alerting logic and the full implementation plan are walked through when we talk — under NDA where your process demands it. We keep the deep end off the open web deliberately.

Built for operations where minutes cost money:

Impact is modelled against your own value stream in the first 30 days — we put ranges on it with you, not before.

Built with Supply Chain domain expertise, from the ground up using AI and humans in the loop — not retrofitted into legacy tools.

Legacy supply-chain software and generic AI copilots bolt intelligence onto architectures that were never designed for it. Datanoetic was built AI-native in 2024 — the intelligence layer, the process model, and the action engine are one system.

How legacy & generic AI works

Bolted on, after the fact

  • AI retrofitted onto 20-year-old architectures, or an LLM wrapper with no process context.
  • Batch analytics that show what happened — rarely why, never in real time.
  • Answers in industry averages, blind to your specific operation.
  • Black-box outputs you can't audit or trace to source.
How Datanoetic works

AI-native, end to end

  • VSM + Knowledge Graph built to your process before go-live.
  • Near-real-time root cause with ranked drivers, cited to your data.
  • Cross-value-stream reasoning — inbound to outbound, one model.
  • Full data lineage on every answer; audit-logged, tenant-isolated.

Connects to your stack. Stays in your tenant.

Read-only connectors to WMS, ERP, TMS, IoT, and T&A systems. Tenant-isolated BigQuery layer. Every data source is bound explicitly — nothing is inferred or assumed.

  • WMS wms_picks · wms_quality
  • ERP erp_orders · erp_inventory
  • TMS tms_shipments · tms_otd
  • IoT device_events · scan_confirm
  • T&A shift_roster · attendance
Your existing systems stay

Datanoetic connects to what you already use — warehouse management, ERP, transport management, IoT sensors, time and attendance. Connections are read-only. We never write to your source systems; we read from them and materialise KPI calculations inside your isolated tenant.

Your source systems on-prem SaaS
Your tenant
BigQuery layer isolated
KPI views materialised
Knowledge graph your entities
DNAI™ reasoning in tenant
Your data never leaves your tenant

Every organisation runs in a fully isolated BigQuery tenant. KPI calculations, Knowledge Graph data, and DNAI™ reasoning all run inside your boundary. No cross-tenant data sharing, ever.

First Pass Yield traced
100 × AVG( fpy_flag )
Source WMS · wms_quality
Scope Warehouse-1 Zone C
Cited in · KPI Guard #147
96.4% baseline 96.2%
Every calculation is traceable

Every KPI formula shows its source column, scope filter, and refresh interval. Every DNAI™ output cites the data it used. Every KPI Guard alert carries a confidence rating and data lineage. Nothing is a black box.

Explore a live demo with us. 30 minutes. No commitment.

We'll show you Datapro-V™ live, run a KPI Guard alert on a mapped scenario, and walk through DNAI™ Chat and Copilot on real data — including a batch-release scenario on a sample pharma VSM if that's your world.