Same Project.
Two Realities.
Every number below is from a real implementation. Scroll to experience the difference.
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We build products AND deliver services. Pick your path.
Watch the Race
Same project scope. Same requirements. Radically different outcomes.
~18 months · 51% over budget · Manual testing · Weekly status reports
~7 months · 45% under budget · AI-powered testing · Real-time dashboard
Every Phase, Reimagined
Click any phase to see the traditional approach vs the Netray approach side by side.
Traditional
With Netray
105+ Agents. One Command.
Watch a real deployment spin up. Each agent is purpose-built for a specific ERP task.
Calculate Your ROI
Adjust the sliders to see projected savings for your implementation.
Head-to-Head on Every Metric
Click any card to see the methodology behind the numbers.
Different Tools. Different Results.
Traditional consulting uses commodity tools. Netray uses purpose-built AI agents.
Real Results. Real Companies.
From actual Netray-powered implementations and product deployments.
Common Questions
Compare Specific Options
Vendor-neutral head-to-head breakdowns with side-by-side criteria tables and clear guidance on which option fits which situation.
ERP vs ERP
Infor SyteLine vs Epicor Kinetic
SyteLine fits make-to-order and engineer-to-order shops that need deep scheduling and multi-site control; Epicor Kinetic fits mixed-mode manufacturers that want a broad out-of-the-box feature set on Microsoft Azure with a large North American partner network.
Infor SyteLine vs SAP Business One
SyteLine fits manufacturers whose shop floor complexity is the core problem; SAP Business One fits smaller distribution and light-assembly companies that value SAP brand continuity, broad local partner coverage, and a simpler footprint they can implement in months.
Infor LN vs SAP S/4HANA
Infor LN fits engineer-to-order and project-based manufacturers that want deep native capability without heavy configuration; SAP S/4HANA fits large diversified groups that need one global platform across manufacturing, finance, and non-manufacturing lines of business and can fund the program.
Infor SyteLine vs Oracle NetSuite
SyteLine fits manufacturers whose competitive edge is production execution; NetSuite fits product companies where finance, multi-entity consolidation, and commerce matter more than shop floor depth and where assembly work is relatively straightforward.
Infor LN vs Oracle Fusion Cloud ERP
Infor LN fits discrete and project manufacturers that want manufacturing depth out of the box; Oracle Fusion Cloud ERP fits organizations prioritizing best-in-class finance, procurement, and HCM on one continuously updated cloud, with manufacturing as one workload among many.
Infor SyteLine vs Microsoft Dynamics 365 Business Central
SyteLine fits manufacturers with real scheduling, configuration, and multi-site complexity; Business Central fits smaller manufacturers already standardized on Microsoft that need solid financials plus light production, extended through ISV add-ons.
Infor M3 vs SAP S/4HANA
Infor M3 fits process-leaning, distribution-heavy, and multi-country manufacturers in food, fashion, chemicals, and equipment service; SAP S/4HANA fits large groups needing one platform across highly diverse business models with the widest global consultant supply.
Infor SyteLine vs Infor LN
SyteLine fits single or few-site discrete plants that want fast time to value; Infor LN fits multi-site, multi-country groups with engineer-to-order, project, and service complexity that justify a heavier but far broader platform.
Infor vs Epicor
Choose Infor when your plants need deep industry-specific manufacturing capability across multiple sites; choose Epicor when you want a mid-market-focused vendor with a dense North American partner channel and a single-product roadmap rather than a portfolio.
ERP vs MES
ERP and MES are not alternatives. ERP plans and costs the order; MES executes and records what actually happened on the machine. Add MES alongside ERP when second-level traceability or machine data drives your margins.
Deployment Models
On-Premise ERP vs Cloud ERP
On-premise ERP fits manufacturers with ITAR or CUI data residency rules, deep customization, and plant-floor systems that cannot tolerate WAN outages. Cloud ERP fits multi-site companies that want vendor-run upgrades and predictable operating spend. The deciding variable is who must control the data and the upgrade calendar.
SyteLine On-Premise vs CloudSuite Industrial (Cloud)
SyteLine on-premise fits shops with deep customizations, direct database integrations, or controlled-data obligations. CloudSuite Industrial fits companies that want continuous platform capability without owning infrastructure or upgrade projects. The deciding variable is how much of your value sits below the API layer.
Single-Tenant ERP vs Multi-Tenant SaaS ERP
Single-tenant ERP fits regulated manufacturers who need an isolated stack, negotiated upgrade windows, or deeper per-customer configuration. Multi-tenant SaaS fits companies prioritizing lower cost, faster feature delivery, and forced version currency. The deciding variable is whether isolation is a compliance requirement or a preference.
Upgrading Baan to LN vs Replacing Baan Entirely
Upgrading Baan to Infor LN fits manufacturers whose processes still work and whose pain is technical currency. Replacing Baan fits companies whose process model is genuinely broken or whose customizations have made the estate unmaintainable. The deciding variable is whether your problem is the platform or the process.
Lift-and-Shift vs Re-implementation
Lift-and-shift fits manufacturers under time or budget pressure whose processes are basically sound. Re-implementation fits companies whose configuration has accumulated years of workarounds worth discarding. The deciding variable is whether your current configuration is an asset you want to preserve or a liability you want to leave behind.
Big Bang Rollout vs Phased Rollout
Big bang fits single-site or tightly integrated operations where temporary interfaces would cost more than the risk they mitigate. Phased fits multi-site manufacturers with distinct plants and enough time to apply lessons. The deciding variable is whether your sites can operate independently during a transition.
Single Global Instance vs Multi-Instance
A single global instance fits manufacturers whose plants share customers, products, and planning and who want enforced standardization. Multi-instance fits diversified groups with independent business models, divergent regulations, or an active acquisition strategy. The deciding variable is whether your plants are one business or several.
In-House ERP Upgrade vs Managed Services
In-house upgrades fit manufacturers with a stable, experienced ERP team and control over priorities. Managed services fit organizations with key-person risk, thin coverage, or upgrade cycles they keep deferring. The deciding variable is whether you have at least two people who could run an upgrade without heroics.
Private Cloud vs On-Premise
Private cloud fits manufacturers who want dedicated infrastructure without owning hardware, staffing infrastructure specialists, or funding a disaster recovery site. On-premise fits organizations with strict physical control requirements, latency-sensitive plant systems, or fully depreciated hardware. The deciding variable is whether you want to own the metal.
Hybrid AI vs Full Cloud AI
Hybrid AI fits manufacturers with controlled technical data, high steady inference volume, or edge latency requirements. Full cloud AI fits organizations prioritizing frontier model quality, fast experimentation, and low operational burden. The deciding variable is whether any of your highest-value use cases touch data that cannot leave your boundary.
AI Platforms & Models
Self-Hosted LLM vs Commercial LLM API
Self-hosting wins when data cannot leave your boundary or token volume is steady and high; a commercial API wins when workloads are spiky, frontier reasoning quality matters most, and your data classification permits third-party processing.
Open-Weight Models vs Proprietary Models
Open-weight models fit teams that need deployment freedom, cost control, and audit evidence inside their own boundary; proprietary models fit teams that need peak reasoning quality and vendor accountability without building an inference platform.
RAG vs Fine-Tuning
RAG fits knowledge that changes weekly and must be cited; fine-tuning fits stable format, tone, or domain vocabulary the base model gets wrong. Most production ERP assistants need RAG first and fine-tuning only if RAG plateaus.
Small Language Models vs Large Language Models
Small models fit narrow, high-volume tasks with tight latency and GPU budgets; large models fit open-ended reasoning, long multi-step chains, and low-volume high-stakes work. Many plants end up running both behind one router.
vLLM vs Ollama
vLLM fits multi-user production serving where throughput and concurrency matter; Ollama fits developer laptops, pilots, and single-team internal tools where setup speed matters more than tokens per second.
GPU Inference vs CPU Inference
GPU inference fits interactive assistants, concurrent users, and models above roughly seven billion parameters; CPU inference fits batch classification, low-volume embeddings, and edge boxes where no GPU is available or permitted.
Qdrant vs Weaviate
Qdrant fits teams that want a lean, filter-heavy vector store with predictable memory behavior; Weaviate fits teams that want built-in hybrid search, embedding modules, and a schema-driven data model in one deployment.
AI Agents vs RPA
RPA fits stable, high-volume, rule-based steps where determinism and audit are non-negotiable; AI agents fit variable inputs, unstructured documents, and judgment steps. Most manufacturers get the best result by wrapping agents around an RPA or API backbone.
Off-the-Shelf Copilot vs Custom AI Agent
Off-the-shelf copilots fit broad productivity work across email, documents, and code; custom agents fit workflows that need deep ERP data, your business rules, and auditable actions. Buy the copilot, build the agent that touches SyteLine.
On-Prem AI Cost vs Cloud AI Cost
On-prem AI wins on unit cost once GPUs run above roughly forty to fifty percent sustained utilization; cloud AI wins below that and for spiky or experimental workloads. Utilization, not list price, is the deciding variable.
Build vs Buy
Building AI Agents In-House vs Buying AI Agents
Buy when the workflow is generic and the vendor can meet your data-residency rules. Build when the agent depends on proprietary ERP logic, must run air-gapped, or becomes a differentiator you cannot license from anyone.
In-House ERP Team vs Managed Services Partner
In-house wins when ERP change is constant and your labor market can supply Infor skills. Managed services wins when demand is lumpy, coverage must span nights and weekends, or a single resignation would strand the system.
Staff Augmentation vs Fixed-Bid Project
Use staff augmentation when scope will evolve and you have someone competent to direct the work. Use fixed-bid when requirements are genuinely stable, the outcome is well defined, and you need budget certainty more than flexibility.
Infor Direct Services vs Infor Channel Partner
Infor direct fits large, product-heavy programmes where roadmap access and internal escalation matter most. A channel partner fits mid-market manufacturers who need deep vertical process knowledge, senior attention, and flexible commercial terms.
Core Customization vs Extensions
Extensions should be the default because they survive upgrades. Core customization is justified only when the requirement sits inside a transaction path that extension points cannot reach without unacceptable performance or data integrity compromises.
Offshore ERP Consulting vs Onshore ERP Consulting
Offshore fits well-specified, high-volume build and support work with stable requirements. Onshore is mandatory for export-controlled data and strongly preferred for discovery, design, and change management where timezone overlap and shared context decide outcomes.
SQL/Crystal Reporting vs Modern BI Platform
Keep SQL and Crystal for pixel-perfect operational documents and regulated forms. Adopt a modern BI platform for exploratory analysis, cross-system metrics, and self-service, then run both rather than forcing one tool to do everything.
EDI vs API Integration
EDI is not optional when a customer or regulator mandates it, which covers most aerospace and defense supply chains. APIs win for internal systems, real-time workflows, and partners willing to integrate directly. Most manufacturers run both permanently.
Traditional ERP Consultants vs AI Agents + Small Expert Team
AI-assisted small teams win on documentation, analysis, code generation, and testing throughput. Traditional consulting still wins on accountability, process negotiation, and change management, so the realistic comparison is team size and cost, not replacement.
Perpetual License vs Subscription
Perpetual suits stable headcount, long horizons, and capital-friendly balance sheets that also want the option to stop paying maintenance. Subscription suits fluctuating users, cloud deployment, and organizations that prefer operating expense and continuous product currency.
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