What is AI for operations?

AI for operations is artificial intelligence applied to the day-to-day running of operations-heavy businesses — warehouses, e-commerce fulfilment centres, logistics networks, cold-chain operations and manufacturing plants. Unlike generic chatbots or analytics dashboards, operational AI reads live data from WMS, ERP, MES, cameras and voice systems, decides the next-best action under your governance rules, and executes it in the field — 24/7. Datanomous WALI is an operational AI platform built around four digital roles (Planner, Supervisor, Manager, COO) that run every shift, every site, every second.

Datanomous is an operational AI company that builds WALI — an AI platform for operations-heavy businesses. WALI deploys four AI digital roles (Planner, Supervisor, Manager, COO) on top of your existing systems: WMS, TMS, ERP, MES, e-commerce, camera, voice and documents. It reads your data, decides the next-best action under your governance rules, and executes — 24/7. Think of it as an operations co-pilot that never sleeps and never misses a signal.

Which industries does Datanomous WALI serve?

Datanomous WALI serves operations-heavy companies in five focus configurations: warehouse operations and 3PL providers, e-commerce fulfilment supply chain (order to last-mile), logistics and distribution networks, cold-chain operations, and manufacturing (shop-floor to multi-plant). Typically 50+ staff per site, multi-system coordination, where decision quality drives cost and SLA outcomes.

How is operational AI deployed? Will it disrupt my operation?

Zero disruption. WALI doesn't change your WMS, ERP or MES — it sits on top as a read-only layer over your existing APIs and cameras. Within 48 hours we start collecting data in shadow-execution mode. Week 1: connect and learn your data. Weeks 2-4: configure the four digital roles for your operation. Week 5: live on one workflow with KPI tracking active. Week 9+: scale to more workflows and sites. Go-live doesn't pause your operation for a single minute.

When will I see measurable results from AI in operations?

Pilot goes live in 30 days. First measurable outcomes typically within 90 days: 25%+ more operational output, 25%+ reduction in operational cost, 25%+ reduction in overtime — all from the same team, on your existing WMS. Real customer deployments have shown up to 30% efficiency gains and 40% reduction in SLA breach rate.

What about data security and governance for operational AI?

Your data stays where it is — on-premise, hybrid or your cloud. Every AI decision is auditable end-to-end with full run lineage. Critical actions require human approval. WALI is SOC 2 and ISO 27001 ready; GDPR and KVKK compliant. We follow your existing IAM and security rules.

Will operational AI replace my operations team?

No — WALI works alongside your team. Routine decisions run automatically; humans handle judgement, exceptions and customer relationships. The four AI digital roles (Planner, Supervisor, Manager, COO) multiply the decisions of your real team. AI multiplies decisions — it never replaces people.

Will my floor staff need to learn new software?

No new app. No training overhead. WALI sends the next-best task as a notification to your operators' existing handheld scanners or voice devices — the same tools they use every day. Real-time guidance adapts to each operator's pace and skill, reducing errors with zero learning curve. Works the same for permanent, seasonal or temporary staff, including non-native speakers.

How much does an operational AI platform cost?

Datanomous WALI Managed starts from $1,999 per month per warehouse — includes four AI digital roles, up to 5 user accounts, up to 3 connected systems, daily manager report, continuous tuning, 30-day go-live and cancel any time. Camera and computer-vision integration is priced separately. WALI Enterprise — for two or more warehouses — is custom-priced and includes multi-site rollout, camera integration, dedicated success team and SLA-backed support.

How does AI help 3PL providers win SLAs?

WALI's Operations Planner and Supervisor roles convert SLA priority into shift-level and task-level decisions. The Planner books labour, dock slots and equipment against the day's SLA mix; the Supervisor watches live and re-prioritises when a client's order is at risk. Typical outcome for 3PL deployments: 40% reduction in SLA-breach rate within 90 days, without changing the underlying WMS. WALI is multi-client and multi-site, so a single decision layer covers every client account on every site.

Can WALI run AI for multi-client 3PL warehouses?

Yes. WALI is built around multi-tenancy from day one. Each client account has its own SLAs, governance rules, KPI dashboards and decision-audit trail. Decisions made for one client never reveal data to another. WALI's four digital roles run independently per client where needed, or unified across a shared workforce — your choice.

How does AI handle e-commerce fulfilment peak season?

Peak season is where WALI earns its keep. The Operations Planner rebuilds the shift plan every hour from inbound demand and outbound SLA risk; the Supervisor reroutes labour mid-shift; the Manager runs what-if scenarios on overtime vs hire decisions. Same team carries 25-30% more volume without adding headcount. Returns processing is orchestrated through the same four-role decision layer.

How does AI prevent cold chain temperature excursions?

WALI ingests temperature telemetry, route progress and forecast data, predicts excursion risk per shipment, and triggers the right corrective action — reroute, swap unit, escalate to driver — before the threshold is crossed. The Operations COO role provides a network-level SLA tower across every cold-chain shipment in flight. Audit-ready decision records satisfy GDP, FSMA and KVKK requirements.

How does AI improve manufacturing OEE?

OEE = Availability × Performance × Quality. WALI's Operations Manager detects bottlenecks live, traces them to root cause across availability/performance/quality, and proposes the next-best action (changeover sequence, operator reassignment, maintenance trigger). The Planner adapts tomorrow's schedule to the bottleneck pattern. Typical uplift: 5-10 OEE points in 90 days on a single line, scaling across multiple plants without ripping out the MES.

What is the difference between WALI and a traditional WMS like Manhattan or Blue Yonder?

WMS platforms like Manhattan Active WM, Blue Yonder and SAP EWM are systems of record — they manage inventory, orders, locations and tasks. WALI is a decision layer that sits on top. It doesn't replace your WMS; it reads from it, decides under your governance rules, and writes the next-best action back into the same WMS tasks or to operator handhelds. The 30-day go-live is possible specifically because WALI doesn't rip out the underlying system.

Is Datanomous WALI an alternative to Kinaxis or o9 for supply chain planning?

Different layer. Kinaxis Maestro and o9 Solutions focus on planning and what-if scenarios at the network level. WALI focuses on execution-layer operational decisions at the site, shift and task level — the layer below planning. Many customers run WALI alongside a planning platform; WALI consumes the planning output and executes it on the floor, with audit records flowing back to the planner.

Can WALI be deployed on-premise?

Yes. WALI deploys on-premise, hybrid, or in your private cloud. All inference, decision logging and operator interfaces can run inside your network if your security policy requires it. We follow your existing IAM and audit standards. This is one of the reasons cold-chain, defence-adjacent and regulated manufacturing customers choose WALI over SaaS-only alternatives.

What systems does WALI integrate with?

WALI integrates with all major WMS (Manhattan Active WM, Blue Yonder, SAP EWM, Körber, Logfire, Mintsoft, custom WMS), ERP (SAP S/4HANA, Oracle, Microsoft Dynamics, NetSuite, Odoo), MES, TMS, e-commerce platforms (Shopify, Magento), camera systems, voice and RF terminals, and document-AI pipelines. WALI Managed includes up to 3 connected systems; WALI Enterprise is unlimited.

How does Datanomous price operational AI?

WALI Managed: from $1,999 / month per warehouse, all four AI digital roles included, up to 3 connected systems, 5 user accounts, 30-day go-live, cancel any time. WALI Enterprise (2+ warehouses): custom-priced, includes multi-site rollout, camera and computer-vision integration, dedicated success team, SLA-backed support. The first 30-minute Operational Diagnosis is free.

Where is Datanomous based?

Datanomous Ltd is headquartered in London (Piccadilly Circus) with an engineering office in Istanbul (Levent). We serve customers across the United Kingdom, European Union, Türkiye, and worldwide. Founded in 2023 by Cengiz Kayay.

REAL DEPLOYMENTS

Case Studies

Measured outcomes from operational AI deployments. No vanity metrics.

FEATURED · WAREHOUSE OPERATIONS

Active deployment

How a warehouse operator unlocked 30% efficiency in 90 days.

Warehouse Operator18 warehouses240 staffLive deployment

✕ Before

×Manual coordination across WMS, TMS, and customer systems
×Late discovery of SLA breaches — customers found errors first
×Overtime decided last minute, paid in panic
×Operator performance reviewed quarterly — too late to act

✓ What we built

We deployed WALI on top of their existing WMS — four digital roles working in parallel. The Planner builds tomorrow's shift from demand and capacity, the Supervisor handles live alarms and floor decisions, the Manager runs root-cause and what-if scenarios, and the COO sees cross-site KPIs in one tower.

📋Demand-driven shift planning (Planner)

🌅Shift open with SLA priority (Supervisor)

⚡Live alarms during operation (Supervisor)

💰Overtime vs SLA penalty what-if (Manager)

📊Multi-shift trend & manager report (Manager)

🗼Cross-site KPI tower (COO)

Results — 90 days

+30%

Operational performance

−40%

SLA breach rate

−25%

Overtime cost

30 days

Time to live

"We catch problems before customers do. The shift report alone saves our manager 3 hours every day."

— Operations Director · Warehouse

More case studies, coming soon.

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30 days to live, 90 days to measured results.

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