Question 1

What is included in the SYMPRO AI free trial?

Accepted Answer

The SYMPRO AI free trial includes 14 days of access, 10,000 AI credits, and no credit card requirement . Trial users can explore the available SYMPRO AI workflow modules, including AccuSearch, AccuScreener, RoB Master, AQUA, AccuSynthesis, and AccuScripter . Trial users also have access to email support, and an onboarding call to help them get started.

Question 2

Where can users find training, user guides, technical support, and bug reporting?

Accepted Answer

Users can access training and user guides through SYMPRO AI’s product resources and onboarding support. For technical assistance or bug reporting, users can contact the support team through email, or in-app support tickets where available. Users can also reach the team at contact@accuscript.org . Onboarding support is also available to help teams get started with the platform and its different modules.

Question 3

Are credits shared between team members?

Accepted Answer

Credits are managed at the organisation or company level and can be used by authorised users according to their account access. This makes credits a shared organisational resource rather than a separate balance for every individual user. Usage tracking helps organisations understand how credits are consumed across modules, projects, and users. This is useful when several reviewers, analysts, or project teams are working inside the same organisation account.

Question 4

Can organisations purchase additional credits, and what happens when credits run out?

Accepted Answer

Yes. Organisations can purchase additional AI credits. If available credits run out, additional credits can be added. Existing project data and completed outputs remain available as well.

Question 5

What privacy and quality credentials support SYMPRO AI?

Accepted Answer

SYMPRO AI is supported by ISO 9001 and GDPR credentials , reflecting a strong commitment to quality management, privacy, and responsible data handling. The platform incorporates key trust and governance principles, including role-based access, organisation-level data separation, traceable decisions, configurable AI use, human oversight, and reviewable outputs . Together, these measures provide organisations and research teams with a structured and transparent environment for using AI across evidence-generation workflows.

Question 6

How do AI credits work, and which actions consume credits?

Accepted Answer

AI credits are consumed when a workflow runs an AI-assisted operation. The exact credit use depends on the module, action, file count, article count, field count, or processing scope. The product shows available credit information so users can monitor usage before and during a trial or organisation plan. Examples of credit-consuming actions include AI search generation, AI screening or full-text processing, risk-of-bias processing, AccuSynthesis extraction, AQUA processing, AccuScripter structure generation, and AccuScripter writing generation. AccuSearch credit use is typically tied to search or AI search-generation actions. AccuScreener credit use can be tied to article-level AI screening or file-level full-text processing. RoB Master and AQUA credit use is typically file-based. AccuSynthesis credit use can depend on files, extraction fields, or the configured extraction scope. AccuScripter credit use can depend on file count and generated structure or writing operations. Manual review, ordinary navigation, editing already generated text, and downloading existing exports generally do not consume credits unless they trigger a new AI run.

Question 7

Is customer data used to train SYMPRO AI models?

Accepted Answer

No. Public SYMPRO AI trust content states that project data is not used to train or improve SYMPRO AI models. Project information is treated as confidential within the configured workflow. This matters for research teams because uploaded protocols, references, documents, extracted data, risk-of-bias assessments, and draft reports can contain unpublished or client-sensitive information.

Question 8

Which input formats, output formats, and file sizes does AQUA support?

Accepted Answer

AQUA supports standalone projects and workflow-linked document use. In the current upload workflow, users can upload PDF, DOC, and DOCX files, process multiple documents, review each document separately, and export results. AQUA supports PDF, DOC, and DOCX input files. It can be used through standalone AQUA projects or with workflow-linked documents. Multiple documents are supported, with per-document processing and review. The current upload workflow supports files up to 50 MB each. AQUA results can be exported as DOCX and Excel files. Readable document content helps AQUA present cleaner abbreviation results.

Question 9

What information does AQUA extract from uploaded documents?

Accepted Answer

AQUA extracts abbreviation-related information from uploaded documents. It separates abbreviation findings by document area, including text, tables, and figure-related content. It can also surface relevant candidates that may need user confirmation. This is useful when a research document contains many short forms, acronyms, technical terms, or table-specific abbreviations. Instead of asking users to manually scan the entire document, AQUA presents a structured review surface so users can inspect detected abbreviations and possible candidates per file. Text abbreviations are abbreviations and definitions detected in the main body text. Table abbreviations are abbreviations and definitions detected from table content. Figure abbreviations are abbreviations and definitions associated with figures or figure-related content. Relevant candidates are possible abbreviations, short forms, or terms that may need confirmation by the user.

Question 10

Can users see which search cycle introduced each study?

Accepted Answer

Yes, the Living SLR workflow is intended to preserve search-run and sync-run provenance. Where cycle tracking is enabled, users can see which search run, refresh cycle, or sync introduced a study into the review workflow. This helps teams distinguish original records from later update records. It also supports reporting questions such as which studies came from the baseline search, which came from a scheduled refresh, which were already present, and which were newly synced into screening. Cycle visibility is important for auditability because living reviews evolve over time. Review teams should be able to explain when evidence entered the project and how it was handled after entry.

Question 11

Can Living SLR searches be scheduled automatically?

Accepted Answer

Living SLR is designed for repeated search cycles and ongoing monitoring. Scheduling support depends on the configured workflow and account setup. Where enabled, scheduled runs can help retrieve updated search results and support controlled sync into screening. A responsible living-review workflow should define the database source, search strategy, refresh frequency, sync behaviour, reviewer responsibilities, and how new records are screened. Users should also know whether updates are manual, scheduled, or partially automated.

Question 12

What is AccuScope, and how does it rank articles for a scoping review?

Accepted Answer

AccuScope supports scoping review workflows by helping organise and prioritise articles against the review's objectives. It can rank or score records using configured criteria and available article information so users can focus attention on the most relevant material first. This is useful in scoping reviews because the goal is often to map a broad evidence area rather than answer a narrow intervention-effect question. Ranking can help users understand clusters of relevant literature, identify likely priority records, and manage large result sets.

Question 13

Can AccuScripter generate evidence tables and PRISMA-related report content?

Accepted Answer

Yes. AccuScripter can generate evidence tables and support PRISMA-related reportcontent using information available within the project. It can help create structured evidence tables, study summaries, report sections, and content describing the review methodology and study selection process. AccuScripter can also use screening, extraction, and other project data to develop relevant methods and results content. By bringing project evidence into a structured reporting workflow, AccuScripter helps researchers prepare clear, consistent, and evidence-linked content for systematic reviews and other research reports.

Question 14

Can RoB Master generate downloadable risk-of-bias tables and visualisations?

Accepted Answer

Yes. RoB Master can generate downloadable assessment outputs, including workbook and document files. For supported assessment outputs, the result package can also include judgement summaries and generated visualisations such as summary plots or traffic-light plots. Outputs are packaged for download so teams can review them outside the platform, share them for QA, or use them in reporting workflows. The exact contents can depend on the selected assessment tool and processing result.

Question 15

Which files, file sizes, and multiple-file uploads does RoB Master support?

Accepted Answer

RoB Master supports multi-file upload and background processing. Files should contain readable study content because recommendations and assessment outputs depend on the text that can be extracted from the document. RoB Master supports PDF, TXT, DOC, and DOCX files. A RoB Master project can include up to 30 uploaded files, with a current limit of up to 20 MB per file. After upload, the AI recommendation can be reviewed and changed for each file before processing. Processing runs in the background, with status updates and result downloads available after completion. For best results, users should choose supported file types, keep files within the size limit, and upload readable study documents that contain the information needed for appraisal.

Question 16

Can different studies in one project use different assessment tools?

Accepted Answer

Yes. RoB Master supports per-file assessment selections. This matters because one evidence review can include different study designs that need different appraisal methods. For example, a project may include randomised trials, observational studies, economic evaluations, or systematic reviews. Those evidence types can be assessed with the appropriate tool selected by the user. RoB Master can group files by the user's selected type or scale for processing.

Question 17

How does RoB Master recommend a framework, and can the recommendation be changed?

Accepted Answer

RoB Master reviews uploaded study files and recommends an assessment type or scale for each file. The recommendation is intended to help users identify which risk-of-bias or critical-appraisal framework is likely to fit the study design and evidence type. Users can review the recommendation, change the selected type or scale, and save their selections before processing. This is important because the AI may not know project-specific protocol requirements, sponsor preferences, or methodological decisions made outside the document itself. The recommended workflow is: upload files, review AI recommendations, correct type or scale selections where needed, save selections, then process the assessment outputs.

Question 18

Which risk-of-bias and critical-appraisal frameworks does RoB Master support?

Accepted Answer

RoB Master supports structured risk-of-bias and critical-appraisal workflows with AI-assisted tool recommendations. Configured assessment options include tools such as ROBINS-I, Cochrane-style risk-of-bias options, NICE, Newcastle-Ottawa, JBI, Drummond's scale, Downs and Black scale, and AMSTAR 2.0 where available in the deployment. The exact framework list may depend on account configuration and implementation status. Teams working under a protocol, regulatory submission, HTA process, or sponsor-specific method should confirm the required framework before processing files.

Question 19

Which formats can extraction results be exported to?

Accepted Answer

AccuSynthesis supports structured exports such as Excel workbooks and Word outputs. Excel exports are useful for evidence tables, analysis preparation, QA, and transfer into other review files. Word outputs can support narrative review, client deliverables, or report preparation. Exports can be shaped around the selected sheets, sections, groups, fields, and reviewed results. This lets users export the data they need instead of treating every extraction field as one generic output.

Question 20

Does AccuSynthesis support studies with multiple treatment groups?

Accepted Answer

Yes. AccuSynthesis can represent studies with multiple treatment groups, subgroups, timepoints, outcome domains, and repeated results. It does this through template structure, row identity, dimensions, and measures. For example, a template can distinguish treatment arm, comparator, subgroup, visit, outcome, statistic, and value. This helps prevent values from being flattened into a confusing single row or mixed across arms. The team should design templates carefully for multi-arm studies. Clear row identity and field descriptions reduce ambiguity and make the final export more useful for evidence tables, quantitative synthesis, or narrative reporting.

Question 21

Can users review extracted data before exporting it?

Accepted Answer

Yes. AccuSynthesis is built around researcher review. Users can inspect extracted values, review the source context, identify not-reported or uncertain values, revise extracted data, and decide what is ready to export. This review step is central to the product. AI can help populate a structured template while the research team confirms values, study arms, subgroups, and not-reported values in a consistent evidence workflow. This combination of AI-assisted extraction and researcher review helps teams maintain consistency across complex evidence while keeping researchers in control of the data used for analysis and reporting.

Question 22

Can extraction templates be created, customised, and reused ?

Accepted Answer

Yes. AccuSynthesis supports template creation, customisation, and reuse. Users can build templates manually, import structured templates, convert suitable workbooks into template drafts, and use AI assistance to generate template. Reusable templates are valuable because evidence extraction often repeats similar structures across reviews. A team may use recurring fields for study characteristics, intervention arms, outcomes, timepoints, subgroups, safety events, economic variables, or diagnostic accuracy measures. Templates should still be reviewed before extraction begins. The review team should confirm that each field has the right label, description, value type, allowed options, grouping, and row identity so that exported data matches the intended analysis.

Question 23

What information can AccuSynthesis extract from research articles ?

Accepted Answer

AccuSynthesis extracts the information defined in the project's extraction template. This can include study characteristics, population details, interventions, comparators, treatment groups, outcomes, timepoints, safety results, economic results, diagnostic results, epidemiology values, qualitative findings, and custom fields. You can also upload your own template. The template is the key control layer. It defines sheets, sections, groups, fields, dimensions, measures, value types, allowed options, and instructions. That means extraction is not a generic summary of the article. It is a structured workflow guided by the review team's intended evidence table. AccuSynthesis is designed to make extraction more structured and reviewable, not to remove the need for scientific verification.

Question 24

Can AccuScreener perform summary extraction using Mini-DET?

Accepted Answer

Yes. Mini-DET supports focused summary extraction for configured fields in screening workflows. It can help extract concise information from records or documents so reviewers can see key details while making screening decisions. Mini-DET should be configured around the review's needs. For example, a team might ask for population, intervention, comparator, outcome, design, or other short fields that help screening and triage. The value comes from making extraction focused rather than asking for broad unstructured summaries. Mini-DET outputs are designed to be checked alongside the source material so reviewers can use them confidently during screening and reporting workflows.

Question 25

Which full-text and supplementary-document formats are supported?

Accepted Answer

AccuScreener full-text review is primarily PDF-centred because published full texts are usually handled as PDFs. Citation imports can use supported reference formats, and downstream document modules may support additional file types. For citation import, supported workflows can use Excel, RIS, NBIB, or other supported citation import formats. For full-text screening, the workflow is PDF-focused because published full texts are usually handled as PDFs. For supplementary or downstream document processing, supported formats can include PDF, DOC, DOCX, and TXT where the selected module accepts them. For a smooth upload and review process, users should check file type, file size, document readability, filename, and whether the file belongs to a study in the project.

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