info@swaransoft.com|+91 9220-313-650 (India)+971-50-9292-650 (UAE)

ISO Certified|NASSCOM Member|🇮🇳 Proud Indian Company

Unlock predictive intelligence from your SAP investment

Most SAP implementations tell your finance and operations teams what already happened. Swaran Soft embeds domain ML models and knowledge graphs directly into your SAP data estate — giving procurement, plant maintenance, finance, and EHS teams the intelligence to act before failures, fraud, and overspend occur.

20 ML models across 4 SAP domains. Built on SAP Business Data Cloud + Databricks. Fixed-fee pilot from ₹25 Lakhs.

See the 10-week pilot

Trusted by 350+ global enterprises for 25+ years

Your SAP system stores the signals. Your teams cannot see them.

Every SAP enterprise already has the data to predict supplier failures, prevent equipment downtime, accelerate financial close, and protect workers from safety incidents. The problem is not data — it is the intelligence layer sitting between your flat SAP tables and your business decisions.

What you experience today

What Swaran Soft delivers

Procurement teams run supplier risk reviews monthly — by the time the report is ready, the risk event has already occurred.

Real-time supplier risk scoring on every PO and invoice, with 'guilt-by-association' detection via knowledge graph — flags elevated-risk vendors before they fail a delivery.

Plant maintenance is reactive. Engineers discover failures when equipment stops, not before. Spare parts are either overstocked or unavailable.

Failure prediction 14–21 days in advance. Remaining Useful Life (RUL) models pre-position spare parts automatically. Unplanned downtime reduced by 30–40%.

Financial close takes 8–12 days. Fraud and duplicate payments are caught in quarterly audits, not at the point of posting.

Real-time AP fraud detection flags 3–8% of AP volume on first run. Financial close accelerated from 8 days to 3–5 days with ML-driven journal entry automation.

EHS incident reporting is retrospective. Near-misses are under-reported. Safety teams cannot identify which assets or locations carry the highest risk.

Incident probability scoring on every work order. Near-miss NLP model reads free-text SAP notifications. Cross-plant risk propagation flags identical assets before they fail.

30–40%

Reduction in unplanned downtime

3–8%

AP fraud flagged on first run

8→3 days

Financial close acceleration

The intelligence layer we build on: SAP Business Data Cloud

We build on the architecture SAP itself recommends for AI and ML workloads. SAP Business Data Cloud combines SAP Datasphere for semantic data governance; SAP Databricks natively embedded for ML model training; and SAP Analytics Cloud for dashboards and planning. What Swaran Soft adds is the knowledge graph layer — a property graph using GraphSAGE and Node2Vec that connects entities across SAP modules.

Layer 1 — SAP Source Systems

SAP MM (Procurement)

SAP PM (Plant Maint.)

SAP FI/CO (Finance)

SAP EHS (Health & Safety)

Layer 2 — Data Extraction & Integration

CDC · CDS views · RFC/BAPI · SAP BODS · Delta Live Tables

Layer 3 — Databricks Medallion Architecture

Bronze

Raw ingestion / Full fidelity SAP data

Silver

Cleansed & unified / Joined cross-module

Gold

Feature engineered / ML-ready datasets

Layer 4 — Knowledge Graph — Business Context Layer

Supplier · PO · Invoice · Vendor · Equipment · Work order · Spare part · GL account · Cost center · Employee · Hazard · Incident · Location
GraphSAGE · Node2Vec · Graph Attention Networks · Feature store

Layer 5 — Domain ML Models

Procurement

6 models

EHS

5 models

Plant Maint.

5 models

Finance

5 models

Layer 6 — Business Insights & Actions

SAP Fiori dashboards · Real-time alerts · Embedded analytics · API endpoints

SAP Business Data Cloud

SAP Datasphere + Databricks natively embedded for semantic data governance, ML training, and advanced analytics — the architecture SAP itself recommends for AI workloads.

Databricks Medallion Architecture

Bronze (raw SAP fidelity) → Silver (cleansed & joined cross-module) → Gold (feature-engineered ML-ready datasets). Every transformation is auditable and reversible.

Knowledge Graph Layer

GraphSAGE and Node2Vec algorithms build property graphs connecting entities across SAP modules. Supplier → PO → Invoice → Bank Account. Equipment → Failure Mode → Work Order → Spare Part.

20 ML models across 4 SAP domains — pick the one that solves your biggest problem first

Each domain operates as a self-contained intelligence layer. Start with one domain. The models within it are interdependent — in Plant Maintenance, the failure prediction model feeds the spare parts model, which feeds work order prioritisation. You choose where to start based on your biggest operational cost.

Most procurement teams manage supplier risk with spreadsheets — we replace that with six interconnected ML models running on your SAP MM, FI/AP, and Ariba data, all bound together by a vendor relationship knowledge graph.

The graph layer is what separates these models from simple tabular ML. The supplier risk model picks up 'guilt by association' — a vendor sharing a bank account or material group with a known bad supplier gets a higher risk score even before they've failed themselves. That signal is completely invisible to a model that looks at each vendor in isolation. And the maverick spend target of under 5% can only be achieved when supplier risk, contract compliance, and spend classification are all operating simultaneously.

Supplier Risk Scoring

Multi-factor risk score on every active vendor using payment history, delivery performance, financial stability indicators, and knowledge graph relationships.

Maverick Spend Detection

Classifies every purchase against approved supplier lists and contracts. Targets maverick spend below 5% of total procurement value.

Contract Compliance ML

Compares actual PO terms against master contracts. Flags deviations in price, quantity, and delivery terms before payment.

Spend Classification & Taxonomy

Automated UNSPSC/eCl@ss coding of all procurement spend using NLP on material descriptions. Eliminates manual taxonomy maintenance.

Duplicate Invoice Detection

Detects duplicate and near-duplicate invoices using fuzzy matching on vendor, amount, date, and line-item combinations.

Demand Forecasting

30/60/90-day material demand forecasts from SAP MM consumption history, production plans, and seasonal patterns.

Procurement — 6 models

The graph layer is what separates these models from simple tabular ML. The supplier risk model picks up 'guilt by association' — a vendor sharing a bank account or material group with a known bad supplier gets a higher risk score even before they've failed themselves. That signal is completely invisible to a model that looks at each vendor in isolation. And the maverick spend target of under 5% can only be achieved when supplier risk, contract compliance, and spend classification are all operating simultaneously.

Supplier Risk Scoring

Multi-factor risk score on every active vendor using payment history, delivery performance, financial stability indicators, and knowledge graph relationships.

Maverick Spend Detection

Classifies every purchase against approved supplier lists and contracts. Targets maverick spend below 5% of total procurement value.

Contract Compliance ML

Compares actual PO terms against master contracts. Flags deviations in price, quantity, and delivery terms before payment.

Spend Classification & Taxonomy

Automated UNSPSC/eCl@ss coding of all procurement spend using NLP on material descriptions. Eliminates manual taxonomy maintenance.

Duplicate Invoice Detection

Detects duplicate and near-duplicate invoices using fuzzy matching on vendor, amount, date, and line-item combinations.

Demand Forecasting

30/60/90-day material demand forecasts from SAP MM consumption history, production plans, and seasonal patterns.

Plant Maintenance — 5 models

Unplanned downtime costs 3–5x more than planned maintenance. These five models convert reactive SAP PM into a predictive system — with each model feeding the next in a dependency chain that pre-positions spare parts, ranks work orders by real criticality, and identifies root causes before engineers start guessing.

The five models work as an interconnected system. The failure prediction model feeds the spare parts model — when an asset is flagged at risk, parts are pre-positioned before a work order exists. The RUL model feeds work order prioritisation — an asset with 4 days of useful life ranks higher than one with 30 days, automatically. RCA closes the loop — every breakdown investigation makes failure prediction smarter for next time.

Failure Prediction

Predicts asset failures 14–21 days in advance using SAP PM notification history, measurement readings, and maintenance records. Accuracy: 85–92% on validated datasets.

Remaining Useful Life (RUL)

Calculates days-to-failure for each critical asset. Feeds work order prioritisation and spare parts pre-positioning automatically.

Spare Parts Optimisation

Recommends optimal stock levels for critical spares based on failure prediction outputs, lead times, and criticality classification.

Work Order Prioritisation

Ranks open work orders by real asset criticality, not just reported priority codes. Assets with 4 days RUL rank above assets with 30 days, automatically.

Root Cause Analysis (RCA)

Identifies failure root causes from SAP PM long-text notifications using NLP. Every confirmed breakdown makes the failure prediction model smarter.

Finance FI/CO — 5 models

Finance ML works across three time horizons: real-time fraud detection at the point of each posting, forward-looking 30/60/90-day treasury intelligence, and monthly close acceleration — together giving your CFO a finance function that acts on information rather than on 8-day-old data.

The combination of real-time fraud detection and financial close automation means your finance team stops being a reporting function and becomes a decision-support function. When close takes 3 days instead of 8, the CFO has 5 additional days per month to act on the numbers rather than produce them.

AP Fraud Detection

Real-time scoring of every AP posting for fraud indicators: duplicate vendors, shell company patterns, round-number amounts, and knowledge graph anomalies. Flags 3–8% of AP volume on first run.

Cost Centre Anomaly Detection

Identifies unusual spending patterns across cost centres using time-series anomaly detection on SAP FI/CO posting history.

Cash Flow Forecasting

30/60/90-day cash flow forecasts combining AR aging, AP payment terms, and historical payment behaviour. Accuracy within 5% on 30-day horizon.

Financial Close Automation

ML-driven journal entry suggestions, accrual automation, and intercompany reconciliation. Reduces financial close from 8 days to 3–5 days.

Budget Variance Prediction

Predicts end-of-period budget variance at week 6 of a 13-week period, giving controllers time to act before the period closes.

EHS & Safety — 5 models

In EHS, a false negative is not a metric — it is a life. Every model in this domain is tuned for maximum recall, and cross-plant knowledge graph traversal is the capability that makes EHS ML genuinely life-saving rather than just operationally useful.

Free text is the richest untapped EHS data source. Structured SAP damage codes and cause codes capture what your data entry process allows — which is rarely the full story. The near-miss model reads what workers actually wrote in the long text fields. That narrative contains the mechanism of the hazard in a way that a dropdown code never can.

Incident Probability Scoring

Assigns a risk score to every open work order based on historical incident patterns, asset type, location, and worker experience. High-risk work orders trigger mandatory safety checks.

Near-Miss NLP Model

Reads free-text SAP PM and EHS notifications to extract near-miss events that were never formally reported. Identifies hazard mechanisms invisible to structured data.

Cross-Plant Risk Propagation

When a root cause is confirmed at Plant A, the model immediately flags identical assets at Plants B, C, and D for pre-emptive inspection — before any near-miss is logged.

Regulatory Compliance Predictor

Predicts which assets and locations are at highest risk of regulatory non-compliance based on inspection history, incident patterns, and maintenance records.

Safety Culture Index

Quantifies safety culture maturity by plant, department, and shift using near-miss reporting rates, corrective action closure times, and training completion data.

The knowledge graph: the intelligence layer your BI tool cannot see

Every SAP implementation has the data. The problem is that it lives in separate flat tables — a vendor record, an invoice record, a purchase order record, a GL account record — with no structural connection between them. A knowledge graph changes this fundamentally.

When a Supplier node is structurally linked to Purchase Orders, Invoices, Materials, Vendors, and Bank Accounts, the ML model can detect that a vendor sharing a bank account with a known fraudulent supplier is itself elevated risk — before it has failed a single delivery. That signal is completely invisible to any model that looks at each vendor in isolation.

The same principle applies across every domain. An Equipment node connected to its Failure Modes, Work Orders, Spare Parts, and Responsible Employees enables cross-plant failure propagation: when the same root cause is confirmed on 3 assets at Plant A, the model immediately flags the 12 identical assets at Plants B, C, and D for pre-emptive inspection — before any of them have logged a single near-miss.

Capability

Without knowledge graph

With Swaran Soft knowledge graph

Data structure

Flat tables — vendor, invoice, PO each in separate rows

Connected graph — Vendor node linked to POs, Invoices, Bank Accounts, Materials

Fraud detection

Each invoice scored in isolation — misses network patterns

Vendor sharing bank account with known fraudster flagged immediately

Failure prediction

Each asset scored independently — misses cross-plant patterns

Root cause confirmed at Plant A → 12 identical assets at Plants B/C/D flagged

Spend intelligence

Spend classified by PO category only

Spend connected to supplier risk, contract compliance, and payment behaviour

Start with one domain. Prove the ROI. Then expand.

The ₹25 Lakh pilot is a fixed-fee, 10-week engagement that puts one domain ML model in production with live SAP integration. You choose the domain — procurement fraud, equipment failure prediction, financial close automation, or EHS incident prevention.

What the pilot includes — 6 deliverables in 10 weeks

Week 1–2

Data Assessment

Audit of existing SAP module data quality, completeness, and extraction readiness across your chosen domain. Confirmed scope document and data access plan.

Week 2–4

Medallion Architecture Setup

Bronze-Silver-Gold Databricks lakehouse configured on your cloud environment (Azure, AWS, or GCP). All SAP data pipelines established via CDC and CDS views.

Week 4–6

Knowledge Graph Build

Property graph constructed for your chosen domain using GraphSAGE/Node2Vec. Entity relationships seeded from your SAP data.

Week 6–8

ML Model Training & Validation

First domain ML model trained, validated against held-out SAP data, and tuned to your specific operational context. Model performance report delivered.

Week 8–10

Production Deployment

Model deployed with live SAP integration. Real-time predictions surfaced in SAP Fiori, dashboard, or API endpoint per your preference.

Week 10

ROI Report

Documented business impact: $ fraud flagged / ₹ overspend identified / downtime hours predicted / close days saved. Full multi-domain rollout scoping at no additional cost.

RECOMMENDED START

Single Domain Pilot

₹25 Lakhs

10 weeks · Taxes additional

One SAP domain (Procurement, PM, Finance, or EHS)
Databricks medallion architecture setup
Knowledge graph for chosen domain
1 ML model in production
SAP Fiori / API integration
ROI documentation at Week 10
Multi-domain rollout scoping (no charge)

Dual Domain

₹45 Lakhs

16 weeks · Taxes additional

Two SAP domains
Shared knowledge graph across domains
3–4 ML models in production
Cross-domain intelligence (e.g. PM + EHS)
All Pilot deliverables × 2
Dedicated ML engineer for 16 weeks

Full Platform

Custom

6–9 months · Taxes additional

All 4 SAP domains
20 ML models in production
Enterprise knowledge graph
Model monitoring & retraining pipeline
SAP Business Data Cloud full setup
Dedicated team + ongoing support SLA

Which SAP domain is your biggest operational pain right now?

Select your domain below. We'll send you a domain-specific ROI estimate and schedule a 30-minute data assessment call — no commitment required.

Procurement

Supplier risk, fraud, maverick spend

Plant Maintenance

Equipment failure, downtime, spare parts

Finance FI/CO

AP fraud, financial close, cash flow

EHS & Safety

Incident prevention, near-miss, compliance

Frequently asked questions — SAP intelligence & ML services

What are machine learning development services?

Machine learning development services focus on building intelligent AI models that can learn from your business data and improve over time. These solutions help you automate repetitive processes, detect patterns, and make faster, data-driven decisions. For businesses, machine learning services are used to create smart and scalable digital systems—such as recommendation engines, predictive analytics tools, and automation workflows—that improve efficiency, reduce manual effort, and support long-term growth.

What are machine learning solutions and how do they benefit businesses?

Machine learning solutions are AI-powered systems that analyze your business data to identify patterns, learn from them, and generate meaningful insights. Instead of relying on guesswork, these solutions help you make smarter, data-driven decisions. For businesses, machine learning improves operational efficiency by automating complex tasks, helps predict future trends like customer behavior or demand, and enhances customer experience through personalization. In simple terms, it turns your data into actionable intelligence that helps you grow faster and work smarter.

What are SAP AI solutions?

SAP AI solutions bring artificial intelligence into SAP systems to make everyday business operations smarter and more efficient. By combining AI with SAP, businesses can automate routine workflows, reduce manual effort, and get faster, more accurate insights from their data. These solutions also help improve decision-making across departments like finance, HR, supply chain, and sales by identifying patterns, predicting outcomes, and optimizing processes—ultimately helping your business run more smoothly and efficiently at scale.

What is SAP machine learning?

SAP machine learning refers to the integration of artificial intelligence and machine learning capabilities within SAP platforms to make enterprise systems smarter. It enables predictive analytics, process automation, and data-driven insights that support better decision-making. Businesses use it to improve efficiency, reduce manual work, and optimize operations across departments in real time.

What do SAP machine learning services include?

SAP machine learning services include everything needed to build and run intelligent solutions inside your SAP ecosystem. This covers model development, data integration, predictive analytics, process automation, and deployment of AI/ML models. Together, these services help businesses turn raw SAP data into actionable insights, improve decision-making, increase efficiency, and enhance overall business performance across departments.

How do SAP machine learning solutions improve business processes?

SAP machine learning solutions improve business processes by automating repetitive tasks, reducing manual effort, and increasing overall efficiency. They also enhance forecasting accuracy by analyzing historical and real-time data, helping businesses predict demand, trends, and outcomes more reliably. Additionally, these solutions provide real-time insights that support faster and smarter decision-making, enabling organizations to optimize operations, reduce errors, and improve productivity across different departments.

What is SAP machine learning implementation?

SAP machine learning implementation is the process of integrating machine learning models into SAP systems to make enterprise operations more intelligent and efficient. It includes configuring workflows, connecting data sources, and setting up models so they can process business data seamlessly. This helps organizations automate decisions, improve process accuracy, and optimize overall performance across departments like finance, supply chain, and operations.

Why should businesses invest in machine learning development services?

Investing in machine learning development services helps your business stay competitive by turning data into actionable insights. It enables automation of repetitive tasks, improves decision-making through accurate predictions, and supports smarter business strategies. With scalable AI-powered solutions, you can enhance efficiency, reduce operational costs, and quickly adapt to changing market demands—helping your business grow faster and stay ahead in a data-driven world.

How are SAP AI solutions different from traditional analytics?

Unlike traditional analytics, which mainly focuses on reporting past and current data, SAP AI solutions go a step further by using artificial intelligence and machine learning to predict future outcomes and recommend actions. This means instead of just telling you what happened, they help you understand what is likely to happen and what you should do next. As a result, businesses can make faster, smarter, and more proactive decisions, improving efficiency, reducing risks, and staying ahead of market changes.

Related Swaran Soft services

Agentic AI Services →About Swaran Soft →AI Consulting & Workshops →Contact us →

Fixed-Fee Pilot Programme

Start Your AI Pilot in 8 Weeks

Fixed-fee ₹4.5L pilot. Production-ready in 8 weeks. Data stays in India. No vendor lock-in.

No vendor lock-in

Data stays in India

350+ enterprise clients

ISO 9001 certified

Download AI Guide