Discover · Diagnose
Find the next improvement opportunity
Explore how process mining and dependency mapping reveal bottlenecks and help estimate their business impact.
Guided demonstration by request
Request process intelligence demoA breakaway strategy
ERP implemented at machine speed.
Less manual work. A faster path to business value. NeuralERP.AI brings AI agents and experienced practitioners together to accelerate SAP S/4HANA configuration, migration and operations—with your team in control.
A 30-minute assessment. One scoped opportunity. No obligation.
- 50–60%
- Time and cost reduction target
- 98%+
- Migration accuracy target
- 6–8 weeks
- Control Tower engagement
Illustrative targets, not guarantees. Scope and data quality determine results.
YOUR ENTERPRISE, CONNECTEDHuman-led · AI-enabled
Discover
Configure
Migrate
Operate
Enterprise knowledge graph
RequirementsProcessesConfigurationData
Your team reviews and approves
SAP S/4HANA Governed execution
The journey
Six stages. One continuous path to value.
From the first diagnostic to more autonomous operations, build value in manageable waves. Enter at the stage you need, agree the evidence for success, and let results guide the next investment. Timings below are indicative, not a fixed delivery schedule.
Stage 01
Discover
Baseline current-state cost, cycle time and data quality. Agree where a focused diagnostic will be most useful.
Week 0–2
Stage 02
Diagnose
A knowledge graph is built across your landscape and value opportunities are sized and gated before any commitment grows.
Week 2–6
Stage 03
Design
Agents draft configuration intent against the target operating model; every material decision still needs a human sign-off.
Week 6–12
Stage 04
Deploy
Migration, configuration and test evidence are written directly into S/4HANA — not recommendations left in a document.
Month 3–6
Stage 05
Operate
Incidents are logged and dispositioned automatically; your team arbitrates the exceptions instead of the whole queue.
Ongoing
Stage 06
Evolve
Control Tower agents keep managing the business by exception, so the estate becomes a standing change capability.
Ongoing
Agentic AI portfolio
Every product has a place in the journey
Connect ERP delivery with business improvement. Combine the agents and services your next priority needs, with human approval built into delivery.
| Product | Discover | Diagnose | Design | Deploy | Operate | Evolve |
|---|---|---|---|---|---|---|
| Process Intelligence AgentsFind process bottlenecks and establish cost and cycle-time baselines. | Primary | Primary | Supporting | Not a designated stage | Supporting | Not a designated stage |
| Knowledge Graph BuilderConnect processes, data and configuration to reveal enterprise dependencies. | Not a designated stage | Primary | Primary | Supporting | Supporting | Supporting |
| Configuration AgentsTurn requirements into proposed MM, SD, PP, FI and CO configuration. | Not a designated stage | Not a designated stage | Primary | Primary | Supporting | Not a designated stage |
| S/4HANA Data MigrationPrepare ECC data for S/4HANA through mapping, cleansing and reconciliation. | Not a designated stage | Supporting | Supporting | Primary | Not a designated stage | Not a designated stage |
| Agentic AMSAutomate incident logging and triage, with people handling exceptions. | Not a designated stage | Not a designated stage | Not a designated stage | Supporting | Primary | Primary |
| Control Tower AgentsFocus attention on working capital, procurement and compliance exceptions. | Not a designated stage | Not a designated stage | Not a designated stage | Not a designated stage | Supporting | Primary |
See it run
Explore the work, not just the promise
Watch pricing configuration in action, explore a guided demonstration or bring your ERP priorities to a tailored briefing.
Design · Recorded demo
Pricing configuration demonstration
Watch agent-driven SAP Sales and Distribution (SD) pricing configuration.
Demo description
Agent-driven pricing configuration in an ERP interface, focused on SAP SD. For a step-by-step accessible walkthrough, request a guided session. The recording includes audio; captions and a transcript are not yet available.
Request an accessible pricing walkthroughDeploy
ECC → S/4HANA migration run
Walk through data mapping, cleansing and reconciliation, from quality checks to cutover readiness.
Guided demonstration by request
Request migration demoOperate
Resolve incidents, manage exceptions
Explore automated logging and triage in a private GenAI platform, with risk thresholds, human review and learning from resolved incidents.
Guided demonstration by request
Request AMS demoEvolve
Control Tower exception review
See how business owners can review working capital, procurement and compliance recommendations and track their value.
Guided demonstration by request
Request Control Tower demoYour next value wave
A briefing tailored to your ERP landscape
Bring your priorities, SAP modules and constraints. Leave with a focused use case and a practical next step.
- Choose the opportunity and decision owners.
- Review data access and governance.
- Agree what success would look like.
Where it applies
Implement the ERP. Then improve how it runs.
Reduce manual delivery work and focus operations on the exceptions that matter. Each engagement fits the capabilities, controls and priorities of your SAP landscape.
Move from requirements to release with clear approvals and traceable evidence.
Requirements to configuration
Translate requirements into proposed BC Sets and IMG entries using a knowledge graph, module agents and task-specific agents. Review design and test evidence before people authorise changes.
Legacy data migration
Prepare ECC data for S/4HANA with object mapping, cleansing and reconciliation. Check quality throughout the migration, not just at cutover.
Illustrative scenario — not a customer case study or guaranteed result
The planning example models 50–60% lower migration cost and a 5–6 month timeline. An accelerated alternative models 65% lower cost and 3–4 months. Estimates depend on data quality, object scope, integrations and cutover readiness.
Integration & interface design
Map dependencies to propose IDoc and API interfaces. Assess release risk with machine-learning signals, named interface owners and security review before deployment.
Test & cutover
Build agent-generated regression packs into each Agile Release Train. Rehearse cutover against clear acceptance criteria, retain the evidence and keep the go/no-go decision with people.
What changes
People, process, technology and data
Plan for more than a technology rollout. These intended outcomes connect delivery improvements with new skills, accountable decisions and dependable data.
| Dimension | ERP implementation impact | Autonomous enterprise impact | Our agentic AI solution approach |
|---|---|---|---|
| People | Shift effort from manual build tasks to design, risk review and sign-off, supported by a practical reskilling plan. | Focus support teams on exceptions and complex decisions; size the team around workload and service quality. | Human approval at design, output review and risk sign-off, with benefit measurement and skills development for emerging roles. |
| Process | Plan smaller, approved releases rather than a single multi-year programme. An illustrative cadence is 30–90 days, subject to scope and readiness. | Manage by exception across the process chain, with clear escalation and approval boundaries. | Agile Release Trains, CI/CD controls and reusable industry playbooks your team retains. |
| Technology | Draft configuration against a live enterprise model, then validate and authorise changes before deployment. | Build an ongoing change capability alongside S/4HANA, with explicit permissions and autonomy limits. | An enterprise knowledge graph, modular agents and open interoperability designed to reduce protocol lock-in. |
| Data | Track migration quality throughout delivery, with reconciliation evidence at each approval gate. | Give agents queryable business context from master and transactional data within quality and access controls. | Connect process, configuration and master data in one knowledge graph, with traceable actions and ownership agreed in the engagement. |
People
- ERP implementation impact
- Shift effort from manual build tasks to design, risk review and sign-off, supported by a practical reskilling plan.
- Autonomous enterprise impact
- Focus support teams on exceptions and complex decisions; size the team around workload and service quality.
- Our agentic AI solution approach
- Human approval at design, output review and risk sign-off, with benefit measurement and skills development for emerging roles.
Process
- ERP implementation impact
- Plan smaller, approved releases rather than a single multi-year programme. An illustrative cadence is 30–90 days, subject to scope and readiness.
- Autonomous enterprise impact
- Manage by exception across the process chain, with clear escalation and approval boundaries.
- Our agentic AI solution approach
- Agile Release Trains, CI/CD controls and reusable industry playbooks your team retains.
Technology
- ERP implementation impact
- Draft configuration against a live enterprise model, then validate and authorise changes before deployment.
- Autonomous enterprise impact
- Build an ongoing change capability alongside S/4HANA, with explicit permissions and autonomy limits.
- Our agentic AI solution approach
- An enterprise knowledge graph, modular agents and open interoperability designed to reduce protocol lock-in.
Data
- ERP implementation impact
- Track migration quality throughout delivery, with reconciliation evidence at each approval gate.
- Autonomous enterprise impact
- Give agents queryable business context from master and transactional data within quality and access controls.
- Our agentic AI solution approach
- Connect process, configuration and master data in one knowledge graph, with traceable actions and ownership agreed in the engagement.
Why we are different
We scale with agents. Traditional consulting scales with people.
Reduce repetitive work rather than adding another layer of headcount. Our AI-native approach pairs automation with practitioner oversight, designed to improve delivery speed, quality and long-term maintainability.
Traditional consulting model
Our AI-native model
Scales with more people
Scales with AI agents and automation
Manual migration and testing
GenAI-driven autonomous migration
Static operating model design
AI-assisted continuous operating model optimisation
Governance through meetings
AI-enabled predictive governance telemetry
High post-go-live support cost
Lower cost of ownership through embedded intelligence
AI added as an afterthought
AI-native from the foundation
Performance beyond go-live — Industry 5.0 plus DevOps discipline, delivering higher velocity with stronger control, predictability and long-term maintainability.
Services · A living roadmap
The roadmap guides the journey — and evolves with it
Align growth, profitability, technical debt and day-to-day operations with your delivery partner. A living roadmap adapts each phase to business priorities and realised value.
- 01Target operating model
- 02Process discovery & benchmarking
- 03Data object mapping & cleansing
- 04Configuration, integration & design-to-build acceleration
- 05Business process improvement & continuous optimisation
Guide
Outcome-based governance
Judge progress by business outcomes and timely decisions, with clear owners and evidence.
Sense
Early risk signals
Monitor sprint risk, cycle-time drift and compliance gaps so teams can intervene earlier.
Re-plan
Fund the next priority
Turn a stalled programme into manageable phases. Use realised value to assess whether the next phase can fund itself.
Evolve
Next-wave opportunities
Review measured ROI and adoption with business leaders, then reprioritise the next improvement.
- Plan
- Deliver
- Measure realised value
- Reprioritise the roadmap
Feed measured value back into the next plan.
Plan your first value wave
Assessment · 30 minutes
Identify a focused opportunity, review constraints and agree the next step.
Control Tower · 6–8 week planning window
Target up to 3 opportunities with 1% P&L impact each, subject to validation against your data, scope and readiness.
Migration · Illustrative 5–6 months
Plan ECC to S/4HANA migration alongside your team and delivery partner. Timing depends on data quality, integrations and cutover scope.
Run & optimise · Ongoing
Review incidents, monitor value and prioritise improvements after go-live.
Our approach
Combine specialist consulting with AI agents to address scope, data, project governance and skills risks. Our illustrative effort model allocates 10% to analysis, 20% to design and 70% to configuration—not a guarantee of 70% automation. Business requirements become natural-language requests, grounded in a knowledge graph and specialised models. People review and authorise proposed changes before execution through SAP APIs.
Technical thought leadership
From open research to responsible ERP workflows
Evaluate emerging ideas against real ERP needs. Connect open research, enterprise architecture and outcome-based delivery—with evidence guiding deployment.
Open research perspective
Project NANDA
- Project NANDA at MIT explores how independent AI agents discover, communicate and collaborate.
- Evaluate open multi-agent frameworks for governed ERP automation.
- Develop the skills needed to turn emerging research into practical workflows.
Enterprise knowledge graph
Neo4j + SAP HANA Cloud
- A reference architecture connecting siloed business data and mapping the strength of dependencies.
- Combine deterministic controls with agentic reasoning to make answers and actions traceable.
- Keep AI close to transactional data while assessing residency, latency and access needs.
Control & responsibility
Governance by design
- Agree tenant isolation, agent ownership and retention of your knowledge assets before delivery.
- Embed AI within business process controls, with clear approval boundaries.
- Use predictive risk signals to support accountable human decisions.
- 01
Research
Evaluate open frameworks for practical ERP use cases.
- 02
Reference architecture
Combine deterministic controls, agent reasoning and enterprise knowledge graphs.
- 03
Business model
Explore outcome-based delivery and reinvestment of realised value, with agreed commercial terms.
- 04
Share validated learning
Plan to publish reproducible findings once results are validated and permission is secured.
Technologies and research are named for reference only; no formal alliance, certification or endorsement is implied.
Illustrative migration economics
Explore a $300K migration saving scenario
For a $3M ERP programme, this example assigns 20% of the budget to data migration and models a 50% cost reduction. It is a planning scenario, not a proven case study or a quotation.
| Migration aspect | Traditional scenario | AI-assisted scenario | Modelled difference |
|---|---|---|---|
| Timeline | 9–12 months | 5–6 months | ~50% faster |
| Team size | 20–35 data stewards | 10–15 business users, part time | ~50% smaller |
| Approach | Manual transformation | AI automation | Repeatable |
| Data checks | Manual checks | Automated reconciliation | Exceptions for review |
| Total cost | $600K | $300K | 50% modelled reduction |
Cost, staffing and timeline figures are illustrative assumptions, not measured customer results or guarantees. Outcomes depend on scope, landscape complexity, source data quality and the validation criteria agreed for your programme.
Granular control over the hardest part of the programme
Automated reconciliation helps compare source and target data and flag exceptions for review. Your team agrees validation criteria and resolves discrepancies before approving a migration wave; automation does not guarantee error-free data.
Walk through your migrationEstimate your saving
What would this be worth on your programme?
Move the sliders to explore the illustrative migration scenario above. These are planning estimates, not measured delivery results — no email required.
$3,000,000
10 months
Models data migration at 20% of the ERP budget, with a hypothetical 50% reduction in migration cost and duration and a three-month minimum timeline. Actual outcomes vary; this is not a guarantee or quotation.
Estimated migration saving
$300,000
Modelled baseline migration cost$600,000
AI-assisted cost scenario$300,000
Migration timeline10 → 5 months
Security and ownership
Enterprise AI without giving up control
Security, ownership and human review belong in the first conversation — and in the agreed delivery scope.
Tenant-specific isolation
Plan tenant-specific environments, user authorisations and segmentation around your security requirements. Confirm data flows and access boundaries as part of the architecture review.
Clear ownership terms
Agree data location, retention, model usage rights and ownership of configurations and operational artefacts before implementation. Review the terms with your security and legal teams.
Human in the loop
Define approval gates and validation criteria for each workflow. Automation supports the work; your practitioners review exceptions and retain responsibility for approving changes.
Questions
The things buyers ask us first
Straight answers to the objections that come up in most first conversations.
Who we work with
Built for programmes where the timeline is not negotiable
We work directly with enterprises and alongside consulting partners. If your programme looks like one of these, the assessment will be worth the 30 minutes.
Mid-market enterprises
$75M–$500M revenue, running an ERP implementation or upgrade where the budget leaves no room for a nine-month data migration.
PE-owned portfolio companies
Where the ERP programme sits on the value creation plan and the hold period does not tolerate slippage.
M&A and carve-out programmes
Separations, integrations and first-time implementations with fixed deadlines and incomplete source data.
Consulting partners
System integrators under margin pressure who want to break the resource-dependency model and win on delivery speed.
Partnering with a consulting firm?
We actively propose partnerships alongside direct engagements — our agents augment your delivery model rather than competing with it.
Next step
Start with one wave. Build the case for the next.
Turn your ERP priorities into a practical starting point. In a 30-minute conversation, we can explore where AI could help and what a first wave would need to demonstrate.
- Identify a focused first wave
- Define the outcomes and measures that matter
- Explore the right mix of AI products and delivery support
30-minute ERP assessment
Bring the context. Explore the next step.
A high-level overview is enough — no sensitive data needed.
- ERP landscapeYour current platforms and key dependencies
- GoalThe outcome or operational challenge you want to address
- TimingYour target dates and constraints
Choosing a time loads Calendly, an external booking service.