Service Decision Intelligence - The intelligence layer for AI agents in service
SDI combines machine data, CRM history and technical knowledge to create a knowledge base that AI agents can use reliably. With proof of source. On your infrastructure. LLM-independent.
Your benefits with Service Decision Intelligence
Domain knowledge instead of hallucination
AI answers with proof of source from service history, ERP, IoT and documents – no gut feeling.
Data sovereignty with your company
SDI runs on your cloud (Azure/AWS). No service data in external LLM trainings.
LLM- and agent-agnostic
Compatible with Agentforce, Claude, Copilot – connected via MCP. You can switch if you want.
Time-to-value: 10-12 weeks
6 ready-made skills instead of 6-12 months of in-house development.
Level 3: Decide
Digitize → Network → Decide → Automate
Each level delivers independent value. You decide where you want to start.
SDI is the third stage of service digitalization. It is based on structured data (level 1: machine file) and end-to-end processes (level 2: networking) – and provides the intelligence that turns data into decisions.
Even networked data does not deliver decisions.
Manufacturers of machines and equipment have consolidated their service data in recent years: Machine file in Salesforce, ERP connection, IoT telemetry, knowledge database, service history. The data is there. Nevertheless, the same problems remain:
- Claims, RMA and warranty triage continues to take hours – because service staff are searching for telemetry, contract status, spare parts history and diagnostic information from four systems.
- Agentforce, Copilot and similar frameworks are hallucinating if they only access Salesforce data – critical service context is in ERP, IoT and documents.
- Generic LLMs don’t know your machines – they provide convincing but incorrect answers about components, fault codes and maintenance steps.
- In-house developments take 6-12 months, are proprietary and linked to an LLM provider.
- Compliance (AI Act, data sovereignty) becomes an obstacle when service data ends up in external training systems.
What is missing is not another AI – but a layer of intelligence between your data and the agents that use it.
Our solution:
A knowledge base that makes AI agents reliable.
Service Decision Intelligence is the intelligence layer between your service data and your AI agents. SDI combines machine data from IoT, contract history from Salesforce, ERP data and technical knowledge from documents into a standardized, queryable knowledge base – and makes it available as skills that every agent can use via MCP.
The result: AI answers that are based on your domain knowledge, cite sources and remain comprehensible. Whether Agentforce, Claude or your own agent – the context they work with comes from SDI.
The 6 skills of SDI
Telemetry analysis
IoT data from your IoT platforms, time series databases (e.g. InfluxDB) or existing systems are analyzed, anomalies detected and trends extracted. Answers to questions such as “Which machines have shown unusual behavior in the last 30 days?” – with time series evidence.
CRM context
Salesforce histories are queried without API limits – via GRAX, which keeps Salesforce data permanently available. Service cases, contracts, complaints and spare parts orders for a machine are available in one response, even over several years.
ERP data
Material and batch data, order reference and delivery history from the ERP can be used for service decisions. Important for warranty and claims cases where the component batch, delivery date or order context are part of the diagnosis.
Technical knowledge (RAG)
Operating instructions, service bulletins, maintenance plans and error catalogs can be queried via Retrieval Augmented Generation. Answers quote the page number and source document – not a hallucination, but verifiable facts.
Diagnostic synthesis
Domain-specific triage across system boundaries: claims, RMA, warranty. SDI combines telemetry, CRM, ERP and knowledge, tests hypotheses anti-confirmatory (i.e. also against the obvious answer) and suggests diagnoses with a confidence score and proof of source. Data gaps are explicitly named – no pseudo certainty. Cross-asset reasoning recognizes patterns even across locations and fleets. Every diagnostic report is persistent, unchangeable and auditable – AI Act-compliant from day 1. Service employees decide, AI argues.
Proactive insights
Recognize patterns before they escalate: Anomalies in the installed base, accumulation of certain error patterns in a batch, emerging contract processes with risk fleets. SDI generates insights and transfers them to Salesforce workflows or Agentforce agents.
What distinguishes SDI from Agentforce alone and generic AI tools
Agentforce, Copilot and other agent frameworks are front ends. SDI is the intelligence level behind them. What makes us different:
- On a Salesforce-native machine context – SDI is not a decision overlay alongside CRM. The skills are based on the machine file and the Salesforce data model. Asset hierarchy, service history and installed base are not a data export, but a foundation.
- Domain knowledge mechanical engineering – Skills are tailored to industrial service data (components, error codes, service histories, warranty logic), not generic.
- Reasoning with confidence score and data gaps – every diagnosis mentions confidence, tests hypotheses anti-confirmatory and explicitly names data gaps. People make informed decisions, not based on LLM gut feeling.
- Persistent audit trail – AI Act-compliant from day 1 – every diagnostic report is stored unchangeably and can be audited. Proof of source per response, each statement can be quoted.
- Data sovereignty on your cloud – SDI runs on your Azure or AWS instance. No training data leaves your company.
- LLM- and agent-agnostic – Agentforce today, Claude tomorrow, a locally hosted model the day after tomorrow. SDI remains; you choose the front end.
- Bidirectional Salesforce connection – SDI skills are simultaneously available as a REST action for Agentforce (Headless 360) and as an MCP tool for other AI front-ends. Standard, not proprietary.
- GRAX as CRM backbone – Salesforce history without API limits, complete, even over years.
The result: productive in 10-12 weeks instead of 6-12 months of in-house development – with an architecture that does not chain you to an LLM provider.
The fields of action: From informed decisions to AI agents
Faster, well-founded service decisions
Claims, RMA and warranty triage significantly faster. Diagnoses with confidence score and proof of source. Service employees make decisions based on networked data instead of the empirical knowledge of individuals.
Proactive service instead of reactive fire department
Patterns in the installed base become visible early on. Anomalies in telemetry, clusters of certain components, imminent escalations – before the customer calls.
AI agents you can trust
Agentforce, self-service bots in the customer portal and internal service co-pilots work with domain knowledge instead of what happened in the LLM training. Answers are comprehensible, with a source, in your language.
For service and office work
- Noticeably faster triage for claims, RMA and warranty cases – networked data instead of four systems in parallel
- Diagnostic support with sources and confidence score – AI argues, humans decide
- Data gaps explicitly named – no pseudo-security, no gut feeling from the LLM
- Fewer escalations thanks to proactive pattern recognition
For digital managers and IT
- Data sovereignty – SDI runs on your infrastructure, not in external training environments
- LLM-agnostic – Agentforce today, another provider tomorrow, without re-implementation
- AI Act-compliant from day 1 – persistent, unchangeable diagnostic reports with source reference and audit trail
For management and service strategy
- Scalable service business – consistent service quality even with a growing installed base
- Strategic independence – no ties to an LLM provider, no lock-in
- Prepared for level 4 – Service as Software is the logical continuation
3 specific use cases
Claims and RMA triage
Incoming complaints are automatically enriched with machine, contract, batch and service history. SDI classifies (warranty / goodwill / chargeable), suggests follow-up steps, quotes relevant service bulletins – with confidence score and named data gaps. Processing time per case significantly reduced.
Proactive maintenance
SDI detects anomalies in IoT data, correlates with contract data and maintenance history, generates prioritized service tasks. Salesforce Field Service schedules before the customer calls.
Service co-pilot for office staff and technicians
Service employees ask natural language questions (“Why has machine 41788 broken down more frequently in the last 6 weeks?”). SDI responds with references from IoT, tickets and documentation – via Agentforce, Slack or an internal front end.
SDI needs a database. SDI prepares Service as Software.
SDI is level 3 and is based on the digital machine file and networked service processes(customer portal, IoT, Salesforce integration). If you do not have any structured data, you should start with the Installed Base Assessment.
SDI is also the prerequisite for level 4 – Service as Software: Service that is outcome-based and semi-autonomous. The skills you use for Agentforce today are the same skills that will support autonomous service workflows tomorrow.
Your path: Installed Base Assessment → Digital Machine File → Customer Portal & IoT → Service Decision Intelligence → Service as Software (Vistion)
You don’t have to implement everything at once. Each stage delivers independent value.
Where do you start?
Do you want to get Agentforce up and running properly? → SDI provides the skills that make Agentforce reliable on your data. See also Agentforce implementation.
Are you evaluating Agentforce vs. your own solution? → SDI is the bridge. You use Agentforce as the front end, but retain control over data and LLM selection.
Do you already have a service co-pilot that is not convincing? → Often it is not the LLM that is missing, but the domain knowledge. SDI supplements the knowledge base without changing the front end.
Do you have structured data but no AI strategy? → SDI is a concrete introduction to productive AI in service – without a 6-12 month lead time.
Why logicline?
Own products for faster solutions
Digital machine file, Service Decision Intelligence (SDI) and other modules are ready-made software with domain IP – no effort from zero, shorter time-to-value.
Industry depth for machinery manufacturers
Over 130 projects in service and aftermarket. Our team knows the processes before the first configuration begins.
Salesforce Platform, end-to-end
No system discontinuity, no integration project off track. Portals, spare parts stores, IoT, AI agents – all on one platform.
AI decision intelligence, product-ready
SDI combines machine data, CRM context and knowledge base into concrete recommendations for action. Not an experiment – a ready-to-use module.
Your pace, your order
The step model allows you to start where the need is greatest. Each level delivers immediate value and builds on the previous one.
Ready for the next step?
We will show you in 30 minutes what is possible for your company.
