Support that gets smarter with every case
Turn solved support cases automatically into usable knowledge for your service teams, your knowledge base and your self-service.
With an Agentforce-powered intelligence loop in Salesforce, your company spots recurring problem patterns in closed cases, produces structured solution proposals and generates knowledge articles from them in draft status. A person reviews, adds to and publishes them. Knowledge is created where it already lies: in your cases, comments, email threads and resolution fields.
Your support teams have long been solving many problems. It is just that the knowledge from them often never becomes usable.
In many support and service teams using Salesforce, valuable solution information is scattered across case comments, email threads and personal notes. Agents often know from experience how to solve certain problems. But the knowledge base does not fully reflect that knowledge, or it is out of date.
That leads to recurring problems:
- Agents solve similar cases from scratch again and again.
- Customers find no suitable or current answer in self-service.
- Knowledge managers have too little visibility into which articles are genuinely missing.
- Product and engineering receive no structured signal from the support volume.
- The knowledge base loses trust when content is neither current nor helpful.
made with AI
The raw material is already there in Salesforce. The decisive step is to recognise it systematically, structure it and transfer it into knowledge in a controlled way. That is exactly where we come in.
The solution
An intelligence loop for your Salesforce service processes
The solution evaluates closed cases automatically and detects recurring problem patterns, even when customers describe the same issue in different ways. Similar cases are not found through simple keywords but grouped semantically. That reveals topics which usually get lost in day-to-day business.
For every pattern detected, the system checks whether a matching knowledge article already exists. Where one is missing, the agent creates a structured draft with root cause, resolution steps, special cases and notes on how solid the statements are.
Nothing is published automatically. The article first stays in draft status and is reviewed, completed and approved by a person.
How the process works
01Analyse cases
Closed support cases serve as the starting point. Relevant fields include subject, description, comments, emails and resolution information.
02Detect patterns
Cases are not grouped by simple keywords but by semantic similarity. That way the system also recognises cases that describe the same problem but are worded completely differently. The demo flow shows exactly this difference between keyword search and meaning-based analysis.
03Make knowledge gaps visible
The system identifies recurring problem clusters and checks whether a matching knowledge article already exists. Clusters that are not covered are marked as a knowledge gap.
04Synthesise the solution
For a marked cluster the agent reads the actual resolution histories. From those it produces a structured summary with root cause, resolution path, special cases and confidence notes. Those confidence notes matter in particular, because they show where the data is unambiguous and where cases diverge.
05Create an article draft
The synthesis becomes a knowledge article in draft status. The article follows the existing template structure in Salesforce Knowledge.
06Review and publish with a person
A reviewer checks the draft, edits it where needed and publishes it. Only then does the article become usable for agents, agent assist, self-service or other channels.
07Check the effect
After publication the system checks again whether the knowledge gap has been closed. The demo scenario makes visible that only the published cluster switches to “covered”, while other open gaps remain.
Your benefit
Faster solutions in service
Agents receive verified solution proposals faster and do not have to analyse similar problems from scratch every time.
A better knowledge base
Knowledge grows out of real support cases and is transferred into knowledge articles in a structured way.
More trust in self-service
More current and more relevant content increases the chance that customers find suitable answers before they open a ticket.
More transparency for service management
Recurring problems become visible. Teams recognise which topics are systemic and which remain isolated cases.
Better signals for product and engineering
Support data turns into structured pointers to product problems, UX hurdles or recurring configuration questions.
Controlled use of AI
The agent creates drafts and flags uncertainties. Publication follows only after human review.
Our long-standing, satisfied customers
Why factory42
factory42 combines an understanding of Salesforce processes, technical delivery and hands-on AI use cases. We show not only what is possible with Agentforce, Data 360 and Salesforce Knowledge, but also where governance, data quality and human approval remain decisive.
We make visible what a modern service process can look like: data-driven, able to learn, and under control.
Your contact
Get in touch with our experts
Account manager Veronika Schmatz will answer your questions on the topic