SYNQ develops custom ERP and CRM systems with AI integration. We connect data sources, AI features and a clear user interface to help you transfer document data, review details and access customer and order information.
Less manual data entry. Easier checks and access to information.
Discuss your projectAI can meaningfully reduce effort on individual tasks. Two studies illustrate how differently that benefit can be measured. Their results relate to the tasks studied and are not a forecast for your ERP, CRM or a SYNQ project.
A study published in The Quarterly Journal of Economics in 2025, covering 5,172 support agents, found an average 15% increase in customer issues resolved per hour with AI assistance. Effects varied by experience and skill level; the most experienced agents benefited less. Study by Brynjolfsson, Li and Raymond.
In Noy and Zhang's experiment with 453 college-educated professionals, average completion time for professional writing tasks fell by 40% with ChatGPT. Rated quality increased by 18%. Published in Science in 2023, the study had limited coverage of precise fact-checking and company-specific context. Study and context from MIT.
A customer enquiry arrives by email. Your team needs to read it, enter the details in the CRM and note any open questions. We develop AI assistance for this workflow that prepares fields and makes it easier to check them against the original source. Your team can focus on reviewing and completing the record. We test which steps can usefully be supported against your typical cases.
Illustrative workflow: a concept, not an existing product feature or customer project.
In this model, a missing detail is visible instead of being silently filled in. This is precisely the behaviour that later testing needs to check. Errors must still be detectable and correctable in this workflow.
Enquiries, orders and meeting notes contain information needed later in your CRM or ERP. We plan data capture as a connected workflow: extract relevant details, match them to fields, present them for review and transfer them after approval. The aim is to reduce repeated typing and switching between applications.
For data transfer, we define the source, target fields and how records relate to customers or orders. Conventional interfaces can handle consistent formats and fixed mappings. AI can help where free text or varying document layouts need to be interpreted.
A date is missing, customer details differ or a message contains two different quantities: these issues should be visible before data is transferred. We combine fixed checks for required fields and formats with AI-assisted flags for text content. Your team sees each flag alongside its source and decides how to correct it.
The pilot should assess whether the feature detects relevant discrepancies and how often it raises false flags. An AI check does not prove that a record is error-free or replace approval by the responsible person.
What is still outstanding on this order? What commitments have been made to this customer? AI assistance can make information in your CRM and ERP easier to access. We plan for plain-language questions, concise summaries and direct links to the underlying records, so your team spends less effort gathering information manually.
The feature must only use approved data. When a source is missing or an answer is unclear, the user needs a visible indication and a way to open the original record. Drafting follow-up tasks can also form part of the agreed scope.
These examples show possible project scopes. The feature we implement depends on your workflow, the available data and the test results.
AI reads the request, identifies relevant details and matches them against your ERP. You can see where each suggestion comes from — and what still needs review.
Animated app example with fictional data. The simulated clicks illustrate a possible workflow; no real data is processed.
Assume entering a recurring order currently takes 10 minutes. With AI, 1 minute remains for transferring the data and 2 minutes for review and corrections: 3 minutes in total.
That would save 7 minutes, or 70% of processing time, for this specific task. Across 1,000 such tasks, that amounts to about 116.7 hours per month.
A hypothetical calculation, not a measured SYNQ client result. These assumptions need to be confirmed in a pilot. This refers to one administrative step, not all work across a company.
A rough estimate starts with a specific task. This example assumes 1,000 suitable cases a month, each taking 6 minutes: 100 hours in total. Assuming a 30% reduction in processing time and one extra minute of review per case leaves around 13.3 hours saved per month.
Potential net time saved per month
100 h before − 70 h processing − 16.7 h extra review.
Illustrative calculation, not a forecast or commitment.
You can change the assumptions. With all other inputs unchanged, a 20% gross time reduction yields around 3.3 net hours, and 40% yields around 23.3 hours. These are freely chosen scenarios, not an expected range derived from the studies. If review takes longer than the AI saves, the result can be additional effort.
The calculation applies only to the suitable cases entered. Initial implementation, ongoing support and model costs are excluded. Time freed up does not automatically translate into cost or staffing reductions. In the pilot, we measure processing, review and corrections together.
AI integration is especially relevant for teams that frequently transfer, find or compare similar information. Case volume, usable data and informed review are key. The following profiles are our assessment of possible use cases, not an industry ranking derived from the studies.
Large numbers of customer enquiries, quotes and orders rely on recurring product and customer data. Preparing CRM fields or checking an order can be a useful starting point. This requires well-maintained master data and unambiguous record matching.
Order documents, changes and queries need to move between sales, planning and administration. AI can prepare information for review and highlight open points. Responsibility for technical approvals and binding order changes remains clearly assigned.
Meeting notes, customer agreements and project updates often sit in several sources. Summaries with source links and draft follow-up tasks can make handovers easier. This requires accessible, current project records and defined permissions.
Recurring questions and extensive customer histories offer potential for draft replies and knowledge search. Reliable knowledge sources and review of the answers are essential. For rare one-off cases, unclear data or already very short workflows, review effort may outweigh the benefit.
We plan data connections, processing and the user interface together. This means establishing which system holds the authoritative customer or order data, which interfaces are available and how an approved result is written back. Read access, write access and record matching all belong to the same workflow.
Your team should be able to use the assistance where the work happens: in a customer record, on an order or while reviewing a document. Clear field names, source links and obvious next steps are therefore part of the implementation. An import error must remain visible and be available for targeted correction.
A new custom system can provide the foundation. Explore the scope of our ERP software and CRM software. For an existing third-party system, we first assess interfaces, access rights and technical constraints; we do not promise compatibility with every product.
We suggest starting with a limited test: one task, defined inputs and an expected result. Include typical cases as well as deliberately difficult examples, such as conflicting details or a missing link to the customer.
Before testing, agree how to recognise quality. For data capture, this may mean correctly transferred required fields. For a summary, accurate commitments and identifiable sources matter. If the proposed feature does not meet these requirements, it needs to be adapted, narrowed down or rejected.
The assessment also includes the work after the AI result: how long does review take? How often are corrections needed? What does each processed case cost, including model usage and support? These values need to be measured during the trial. They show whether the feature actually makes daily work easier.
After a successful test, we integrate the agreed workflow into the user interface and plan your team's training. We set the production timeline based on scope, data connections and acceptance criteria. Further tasks can then be added step by step.
We assess this based on your system and the intended workflow. An extension needs suitable interfaces or usable export and import options, along with the necessary access rights. Only then can we determine whether direct integration, an additional workspace or a change to the system makes sense.
That is not automatically the starting point. First clarify the task, data access, quality requirements and operating conditions. The appropriate technical solution follows from that assessment.
That would be a feature requiring explicit definition. The starting point shown here includes human approval before information is transferred. Further actions need their own limits, checks and responsibilities.
The price includes connecting data, implementing the workflow, testing and rollout. Operations may add model usage, infrastructure, support and adaptations. A reliable estimate needs the expected scope and the selected technical solution.
If clear rules already solve the task, the necessary data is not usable or nobody can assess the results, address that issue first. A pilot that provides insufficient benefit is also a useful result for your decision.
Describe your current ERP or CRM, a recurring task and the documents or data it uses. We will discuss where AI integration could simplify the workflow and what an appropriate first scope would be.
Discuss your project