AI Engineering for SAP — Agentic Systems, Tabular & Text Models, Applied ML

The difference

SAP AI is not AI with an SAP connector.

The market is full of both halves. Horizontal AI consultancies that will learn your ERP on your budget. SAP integrators that will bolt a chat window onto a Fiori tile and call it intelligent.

We built the firm at the join — not to do more, but to do the one thing that requires both sides at once.

We already know where the data lives.
Not “we’ll discover your schema in discovery.” The table structures, the document flow, and the reasons your open item aging looks the way it does.
We know what breaks.
Authorization objects. Principal propagation. Change documents. Clean core. What an AI system can read versus what it is permitted to act on.

We know a demo from a deployment.
An agent that answers questions about a purchase order is a demo. An agent that creates one — inside the requester’s own authorization context, with an audit trail — is a deployment.
We pick the model class before the model.
Most of what an ERP is asked to predict is a tabular problem wearing a language-model costume. Knowing which is which is the job.

We are the right partner when the value sits inside SAP and the risk of getting it wrong is measured in journal entries.

Practices

Three practices. One constraint.

Everything we build has to survive contact with a live SAP landscape — its identity model, its change control, its auditors. That constraint is what shapes all three.

Practice 01

Agentic systems

Low-code and pro-code agents that reason, call tools, and take action across SAP and non-SAP systems through MCP — including customer-facing CX built on Amazon Connect.

Explore → Practice 02

AI engineering

Choosing between tabular and text models, then running the full LLM lifecycle — RAG for context, fine-tuning for domain, LLMOps for everything after go-live.

Explore → Practice 03

Applied machine learning

Classification, regression, forecasting, anomaly detection, and entity resolution on SAP master and transactional data — with calibrated, explainable output.

Explore →

Practice 01 · Agentic systems

Agents that can actually touch the system of record.

The failure mode in agentic projects isn’t model quality. It’s that nobody wrote down precisely what the agent was supposed to do, so nobody can tell whether it does it — and nobody scoped what it was allowed to change. We start there.

01.1 — Build surfaces

Low-code where speed wins. Pro-code where control does.

Most enterprises need both, and the mistake is choosing one for the whole estate. We match the surface to the workload: how much state it carries, how tightly it must be audited, and who maintains it after we leave.

Low-code agentic

n8n

Visual workflow and agent orchestration for the long tail — approvals, notifications, data movement, and human-in-the-loop steps that business teams need to read and change themselves. Self-hostable, which matters when the payload is SAP data.

n8nSelf-hosted Human-in-the-loopWebhook & queue triggers

Pro-code agentic

Framework-independent by design

Graph orchestration, tool-calling loops, multi-agent handoff, checkpointing, and evaluation are engineering patterns — not vendor allegiances. Choice follows state complexity, latency budget, and auditability requirements.

LangChainLangGraphLangSmith CrewAIOpenAI Agents SDK Microsoft Agent Framework

LangSmith is not an afterthought in that list. Tracing, evaluation datasets, and regression testing on agent behaviour are the difference between an agent you can put in front of a finance team and one you can only demo. Microsoft Agent Framework — the successor merging Semantic Kernel’s enterprise state management and telemetry with AutoGen’s multi-agent orchestration patterns — is our default where the landscape is already Azure-standardised.

01.2 — Platform coverage

Where the enterprise agent market actually is

Framework-level work covers custom builds. A large share of enterprise agent spend, though, runs through packaged agent platforms — and buyers increasingly shortlist from the analyst field rather than from first principles.

In Gartner’s Magic Quadrant for Conversational AI Platforms published in July 2026, four vendors were placed in the Leaders quadrant: Google, Salesforce, SoundHound AI, and Kore.ai — with Kore.ai a Leader for a second consecutive year, cited for its Arch tooling and Agent Blueprint Language. The wider evaluated field included Avaamo, Boost.ai, Druid AI, IBM, Netomi, NiCE Cognigy, Omilia, PolyAI, Sprinklr and Yellow.ai.

Kore.ai — in build Packaged platforms — in build

On our capability roadmap

We are building delivery capability in Kore.ai and other platforms from that evaluated field, so a client who has already standardised on a packaged agent platform gets the same SAP-side engineering rigour we bring to custom builds. Stated as roadmap, not as current certification — we will say so plainly when it ships.

Gartner does not endorse any vendor, product or service depicted in its research publications. GARTNER and MAGIC QUADRANT are registered trademarks of Gartner, Inc. and/or its affiliates. Quadrant placements reflect Gartner’s July 2026 report and may change.

01.3 — Connectivity

Tools and MCP: how an agent reaches the rest of your estate

An agent is only as useful as what it can call. Two mechanisms carry almost all of that weight. Tools — typed, permissioned functions the model may invoke, each with a clear contract and a clear blast radius. And the Model Context Protocol — an open standard for exposing those tools and data sources over a common interface, so the same agent reaches SAP and non-SAP systems without a bespoke adapter for each one.

Practically, one agent can hold a conversation while reading an S/4HANA sales order, checking an Ariba contract, looking up an employee record in SuccessFactors, and opening a ticket in ServiceNow — each call individually authorised and individually logged. The SAP-native path and the open-standards path stop being a choice.

MCP serversTyped tool contracts OData & RFC/BAPIBTP destinations Principal propagationPer-call audit logging

01.4 — Customer experience

Agentic CX on Amazon Connect, wired to SAP

Customer experience is where agentic AI meets the public, and where a wrong answer is most expensive. Amazon Connect — now delivered as a set of agentic solutions including Amazon Connect Customer — supports AI agents that understand, reason, and take action across voice and digital channels, with Model Context Protocol support and escalation to human agents that preserves full conversation context.

Our contribution is the half that decides whether it works: the SAP side. Order status, delivery blocks, billing documents, credit limits, contract terms, and returns all live in S/4HANA, Ariba, or a CRM sitting on top of them. We build the tool layer that lets a Connect agent read those records safely, act within authorisation, and hand off cleanly when it shouldn’t act at all.

Amazon Connect CustomerAmazon Q in Connect Amazon BedrockAgentCore Gateway Voice + digitalEscalation with context

01.5 — SAP use cases

What we actually get asked to build

Eight patterns account for most inbound agentic work in an SAP estate. Each one is scoped the same way: what may the agent read, what may it change, and who signs off.

Agentic systems · SAP customer base

Order to cashBlocked sales order triage — assembles credit, delivery and pricing context, proposes a release, executes under the approver’s own authorisation.
Procure to payThree-way match exception handling across PO, goods receipt and invoice — gathers evidence, drafts resolution, routes only judgement calls to a human.
Sourcing · AribaSupplier onboarding and qualification support, contract clause lookup, and sourcing event Q&A grounded in the client’s own repository.
HR · SuccessFactorsEmployee self-service across leave, payroll and policy — natural language in, Employee Central lookups behind it, escalation when non-deterministic.
Master dataGuided vendor, customer and material master requests — validated against governance rules before a record is ever created.
Financial closePeriod-end assistant for open item review, reconciliation prep, accrual proposals and variance explanation — agent prepares the close workpaper, controller approves it. Every suggestion traceable to source documents.
Customer experienceVoice and chat agents on Amazon Connect answering order, delivery and billing questions from S/4HANA, acting inside policy limits.
SAP operationsIncident triage from monitoring alerts, transport risk analysis, and change-impact summarisation for Basis and AMS teams.

Practice 02 · AI engineering

Tabular or text. Get that wrong and nothing downstream is fixable.

Ask a language model which of your open invoices will go past due, and it will give you a fluent, confident, structurally unreliable answer. Language models are trained to predict text. Your receivables ledger isn’t text.

02.1 — The routing decision

Two model classes, two jobs

Financial transactions, inventory records, payroll data and supplier information are built for accuracy, auditability and repeatability — a structure that constrains language models, which are optimised to predict text rather than reason across numerical relationships and field-level dependencies. Tabular foundation models exist for that second job. Most real SAP applications need both, with a routing layer deciding which handles what.

Model class ATabular

InputRows, columns, typed fields, referential keys
Learns byIn-context learning over a table; no gradient descent per dataset
Good atPrediction, ranking, scoring, anomaly detection
OutputCalibrated probabilities you can threshold and audit
In SAPFI-AR aging, MM consumption, supplier risk, posting anomalies
ModelsSAP-RPT-1, Prior Labs TabPFN

Model class BText

InputDocuments, tickets, contracts, specifications, code
Learns byPretraining plus retrieval, instruction tuning or fine-tuning
Good atInteraction, extraction, summarisation, explanation, tool selection
OutputLanguage, structured extractions, tool calls
In SAPContract review, incident triage, spec drafting, agent reasoning
ModelsFrontier and open-weight LLMs, selected per workload

02.2 — Tabular capability

SAP-RPT-1 and Prior Labs, held as one skill set

SAP-RPT-1 is SAP’s relational pretrained transformer — a table-native model handling classification and regression out of the box through tabular in-context learning, with no additional training or fine-tuning step. SAP positions it as complementary infrastructure rather than a universal AI layer: language models handle interaction and explanation, tabular models handle prediction inside transactional systems.

Prior Labs is the other half, and we maintain skills there deliberately. TabPFN is pre-trained on very large collections of synthetic tabular tasks so it can reuse statistical patterns across problems, then performs zero-shot inference from a context window in a single forward pass. It handles missing values, outliers and categorical features natively, returns calibrated probabilities, integrates SHAP for explainability, and exposes a scikit-learn-compatible interface. The underlying research was published in Nature.

Holding both matters because they are converging: SAP has moved to bring Prior Labs research into SAP AI Core, SAP Business Data Cloud and Joule. A team that only knows the SAP side will be late to what arrives; a team that only knows the research side won’t know where it lands.

SAP-RPT-1TabPFN Tabular in-context learningZero-shot inference Calibrated probabilitiesSHAP explainability scikit-learn interface

02.3 — LLM management

The whole lifecycle, not just the prompt

Where text is genuinely the right class, the work does not stop at model selection. We run the full lifecycle — and most of the risk in an enterprise deployment lives in the last two stages, long after the pilot demo has been signed off.

Stage 01Context & RAGChunking, embedding, hybrid and semantic retrieval, re-ranking and grounding over SAP documentation, contracts, Z-code and tickets — with citations back to source.

Stage 02Fine-tuningInstruction and parameter-efficient tuning where domain language is genuinely non-standard: transaction codes, module jargon, custom field semantics.

Stage 03EvaluationGolden datasets, task-level scoring, regression suites on agent behaviour, and red-teaming before anything reaches a production authorisation context.

Stage 04LLMOpsVersioning and prompt registries, guardrails, cost and latency budgets, drift and quality monitoring, PII handling and data residency — operated, not just configured.

02.4 — SAP use cases

Where the routing decision earns its money

The same landscape, split by model class. The left-hand column is where most firms reach for an LLM and shouldn’t.

AI engineering · SAP customer base

TabularDays-sales-outstanding and late-payment prediction across FI-AR open items, scored nightly and thresholded into collector work queues.
Text · RAGGrounded answers over functional specs, configuration documentation, custom ABAP and historical incident tickets — cited, not improvised.
TabularDemand and consumption forecasting at material-plant level, including slow-moving and intermittent series that defeat classical methods.
Text · RAGContract intelligence across Ariba agreements: obligation extraction, clause deviation and renewal exposure.
TabularSupplier risk and delivery reliability scoring from historical PO, goods receipt and quality-notification records.
Fine-tuningDomain adaptation for client-specific SAP vocabulary where a general model consistently misreads custom field and process naming.
TabularDuplicate and anomalous invoice detection in AP, with calibrated confidence so review thresholds are a business decision, not a modelling one.
TabularFinance reconciliation and costing — automated GL-to-subledger matching, intercompany elimination, and standard-cost variance detection, with model-scored confidence on every proposed match so controllers review exceptions, not every line.
TabularDemand planning signal extraction — promotional lift, seasonal decomposition and cannibalization effects modelled as tabular features, not text prompts.
LLMOpsProduction guardrails, evaluation pipelines and cost governance for agents already live in finance, procurement and HR.

Practice 03 · Applied machine learning

Sometimes the right answer is a smaller model.

Not every problem needs a foundation model. A well-specified classifier on clean features will beat an over-engineered AI pipeline on cost, latency and explainability more often than the market wants to admit — and when an auditor asks how a number was produced, the simpler model is the one you can defend.

03.1 — Technique inventory

The methods we actually reach for

Modern tabular foundation models have widened this practice considerably. Beyond the familiar classification and regression pair, the current toolkit spans time-series forecasting, anomaly detection, synthetic data generation for privacy-constrained environments, survival analysis for time-to-event questions, statistical feature testing, and fine-tuning where a pre-trained tabular model must adapt to a specific estate. Where latency binds, distillation compresses a large tabular model into a compact ensemble that scores in milliseconds.

ClassificationRegression Time-series forecastingAnomaly detection Synthetic data generationSurvival analysis Statistical feature testingFine-tuning Model distillationGradient-boosted ensembles CalibrationSHAP attribution

03.2 — Entity resolution

The ML problem that decides whether any of the rest works

Agents fed duplicate vendors and inconsistent business partners produce confident nonsense. Master data quality is not a prerequisite to the AI programme — it is part of it, and it is a machine learning problem in its own right.

The discipline has its own vocabulary, and it is worth being precise. Classical deterministic matching applies explicit rules; probabilistic and relevance-based matching apply weighted attribute scoring against thresholds. The current generation goes further: pre-trained, LLM-backed models perform rule-free matching with semantic understanding, using zero-shot learning to suggest matches without the hundreds of hours previously spent defining and iterating match rules. Reltio productised this as Flexible Entity Resolution Networks (FERN) — a direction that became strategically relevant to every SAP customer once SAP moved to acquire Reltio to make SAP and non-SAP data AI-ready.

Around the match itself sits the rest of the machinery: match and merge, survivorship rules determining which attribute wins, the resulting golden record, durable IDs and crosswalks keeping that record traceable to every source system, confidence bands routing borderline pairs to data stewardship queues, and automatic unmerge when underlying data changes.

Entity resolutionRule-free semantic matching Deterministic vs probabilisticWeighted scoring Match & mergeSurvivorship rules Golden recordDurable ID & crosswalks Confidence thresholdsStewardship queues Automatic unmergeMultidomain MDM

03.3 — SAP use cases

Applied to a real landscape

Master data first, because everything else inherits its errors. Then the prediction work that a clean foundation finally makes trustworthy.

Applied machine learning · SAP customer base

Master dataVendor and customer deduplication across ECC, S/4HANA and non-SAP sources ahead of a migration wave — semantic matching finding pairs rules never surfaced.
FinanceCash-flow and collections forecasting from open item history, with prediction intervals rather than single-point guesses.
Master dataBusiness partner harmonisation and golden-record construction with survivorship rules the finance and procurement owners actually agreed to.
FinancePosting anomaly and control-violation detection across document types, tuned to a review capacity the team actually has.
FinanceAutomated reconciliation matching across GL, subledger, bank and intercompany accounts — probabilistic pairing with configurable confidence thresholds replacing manual spreadsheet-based rec processes.
Master dataMaterial master normalisation and classification — free-text descriptions resolved into a governed hierarchy.
Supply chainDemand planning and consumption forecasting at material-plant level — incorporating promotional lift, seasonal decomposition, and intermittent-demand models for spare-parts and aftermarket portfolios. Safety-stock optimisation and slow-moving inventory classification driven by the same prediction layer.
GovernanceSynthetic tabular data generation so development and test environments carry realistic distributions without production records.
OperationsTime-to-event modelling for equipment failure and maintenance scheduling using survival analysis on notification history.
FinanceStandard and actual cost variance analysis across production orders and cost centres — model-flagged outliers routed to cost controllers, eliminating the monthly spreadsheet trawl.

We’ll tell you when you don’t need AI. That’s part of what you’re paying for.

Leadership

Built by leaders who ran SAP programmes before AI was the answer to anything.

This is a new venture, and we are direct about that. What isn’t new is the SAP half — and in this market, that is the half that is hard to hire.

Prakash Tripathi Co-Founder & Managing Principal 30+ years · SAP leadership

Three decades in senior SAP roles — programme leadership, delivery ownership, and accountability for landscapes a business could not operate without. Full-cycle implementations, migrations, global rollouts, and the years of run-and-support that follow, which is where you learn what an ERP actually does rather than what the blueprint said it would.

Reinciar.ai is the deliberate second act: taking that ecosystem knowledge and dovetailing it with frontier AI engineering, rather than approaching SAP from the outside as most AI firms must. The thesis is simple. AI capability is becoming commoditised and available to everyone. Knowing which SAP process can safely be handed to an agent, which prediction is a tabular problem, and which master data flaw will quietly poison both — that is not.

The engineering team is built to match: model and agent engineers who have learned the SAP side, working with SAP practitioners who have learned the AI side.

Depth

Full-cycle SAP implementation, migration and global rollout leadership across three decades.

Operating reality

Years of run, support and change control — where AI proposals meet authorisation and audit.

The dovetail

SAP ecosystem judgement paired with current agentic, model and ML engineering practice.

Nagender Kodali Co-Founder & Managing Partner 30+ years · ERP quality engineering

Three decades building and leading quality functions for enterprise ERP programmes — and the pattern holds: they rarely fail on configuration. They fail on quality, found too late, at the worst possible moment. That observation shaped a career and then became a business.

Co-founded Reinciar Technologies to lead test strategy and programme delivery on large-scale SAP S/4HANA and RISE transformations, so "are we ready to go live?" gets answered with evidence instead of optimism.

Now building the next thing: AI-enabled quality solutions. Three decades of building test and automation capability taught where the real cost sits. It isn't running tests — it's deciding what to test, maintaining scripts nobody owns, and finding the defect three weeks after someone introduced it. That is exactly the work AI is suited to.

Delivery across manufacturing and industrial — electronic components, agricultural and construction equipment, medical devices, packaging, food production — as well as defence and aerospace, telecom, satellite communications, retail, public sector, banking and life sciences. Manufacturing is where the quality discipline is most at home: multi-site, supply-chain integrated, and unforgiving of shortcuts.

Quality engineering

Enterprise Testing CoE built from the ground up — governance, tooling, people, and the operating rhythm that makes quality continuous.

Automation depth

Tricentis (qTest, TOSCA, NeoLoad), OpenText (ALM, UFT One, LoadRunner), SAP Cloud ALM, UiPath — wired into CI/CD across AWS, Azure, GovCloud and S/4HANA Cloud.

Delivery at scale

Teams of 200+ engineers. Test strategy across SIT and UAT for global rollouts spanning 30+ countries and every major SAP workstream.

Engage Reinciar

A narrow firm, deliberately.

We don’t publish client logos we can’t stand behind or implementation counts nobody can check. We’ll put an engineer in a room with your team and work through your actual problem — your data, your landscape, your constraints — and you’ll know within an hour whether we’re the right people.

Book a technical working session Call +1 (470) 550-3790

Headquarters

Johns Creek, Georgia +1 (470) 550-3790 info@reinciar.com

Global delivery centre

Hyderabad, India Engineering & delivery info@reinciar.com

EMEA Regional Office

Bracknell, United Kingdom +44 1344 955209 info@reinciar.com

Coming soon

Durban, South Africa Regional expansion