New webinar on 3rd March 2026 | 9:00 am PT
In this webinar, discover how Sopact Sense revolutionizes data collection and analysis.
Transform lengthy PDFs, reports, and transcripts into structured insights with AI document analysis.
Meta Title: AI Document Analysis | Automated Report Review & Rubric Scoring (60 chars)Meta Description: Transform lengthy PDFs, reports, and transcripts into structured insights with AI document analysis. Rubric scoring, thematic extraction, and compliance checks — in minutes, not months. (158 chars)URL: /use-case/ai-document-analysis
AI document analysis is the process of using artificial intelligence to automatically read, interpret, extract, and score information from unstructured documents such as PDFs, reports, interview transcripts, grant applications, and compliance filings. Unlike simple optical character recognition (OCR), modern AI document analysis applies natural language processing, rubric-based evaluation, thematic coding, and sentiment detection to transform qualitative content into structured, decision-ready data.
Organizations across the social impact, education, and advisory sectors generate thousands of pages of qualitative evidence each year — annual reports, evaluation narratives, stakeholder interviews, program applications, and ESG disclosures. Traditionally, teams spend weeks or months reading these documents manually, copying findings into spreadsheets, and attempting to standardize subjective assessments. AI document analysis eliminates this bottleneck by applying consistent analytical frameworks to every document simultaneously.
The urgency is straightforward. Programs are scaling. Funders demand evidence faster. And the volume of qualitative data — PDFs, transcripts, open-ended survey responses, pitch decks — is growing exponentially. Manual review cannot keep pace.
Consider the numbers: a mid-sized foundation reviewing 500 grant applications manually dedicates 12–16 weeks of staff time just to initial screening. An accelerator program analyzing 100 pitch decks with multiple reviewers burns 200+ hours before a single shortlist is finalized. An ESG advisory firm collecting sustainability reports from 50 portfolio companies spends more time on data extraction than on the strategic analysis they were hired to deliver.
AI document analysis changes this equation. What once consumed entire quarters now takes hours — with greater consistency, full audit trails, and the ability to cross-reference findings across hundreds of documents simultaneously.
AI document analysis encompasses several distinct analytical functions that work together to transform raw documents into structured intelligence:
Thematic extraction surfaces recurring patterns, topics, and narratives across documents. Rather than searching for keywords, AI identifies conceptual themes — growth barriers, stakeholder satisfaction drivers, implementation challenges — and tags them consistently across every document in the dataset.
Rubric-based scoring applies predefined evaluation criteria to qualitative content. A scholarship application essay can be scored against leadership, innovation, and community impact dimensions. A sustainability report can be evaluated against ESG framework compliance categories. Each score comes with evidence citations from the source document.
Sentiment and tone analysis detects whether document language conveys confidence, concern, urgency, or ambiguity. This is particularly valuable for interview transcripts, open-ended survey responses, and stakeholder feedback narratives.
Summary generation condenses 5–200 page documents into structured overviews that preserve key findings, evidence, and recommendations without losing critical nuance.
Compliance and completeness checks verify that submitted documents meet defined requirements — missing sections, incomplete disclosures, or contradictory statements are flagged automatically, often before a human reviewer ever opens the file.
Deductive coding applies researcher-defined coding frameworks to qualitative data, enabling systematic analysis that follows established methodologies like grounded theory, framework analysis, or theory-driven evaluation.
When five reviewers read the same 30-page evaluation report, they extract different findings, emphasize different themes, and assign different quality ratings. This inconsistency compounds across large document sets. A foundation reviewing 200 applications with a panel of reviewers will produce materially different shortlists depending on which reviewer handled which application.
The issue is not competence — it is cognitive limitation. Human attention degrades across documents. The 150th application gets less careful review than the 15th. Fatigue, anchoring bias, and varying interpretation of evaluation criteria all introduce noise into what should be a systematic process.
AI document analysis eliminates reviewer drift by applying identical analytical criteria to every document. The rubric that evaluates the first application is exactly the rubric that evaluates the 500th. Scores are calibrated, evidence is cited, and the audit trail is complete.
Manual document review creates devastating time delays. A typical cycle looks like this: documents arrive → staff distributes for review → reviewers read and take notes → notes are compiled into spreadsheets → spreadsheets are cleaned and standardized → analysis begins → findings are synthesized → reports are drafted.
For an organization processing 100 multi-page documents, this pipeline consumes 6–12 weeks before any actionable insight emerges. During that time, programs continue operating without evidence, funders wait for reports, and strategic decisions are delayed.
AI document analysis compresses this timeline from weeks to hours. Documents uploaded in the morning produce structured analytical outputs by the afternoon. Themes are surfaced, scores are assigned, and summary reports are generated — all with evidence links back to the source material.
Perhaps the most costly failure of manual review is what never gets analyzed at all. Organizations routinely collect rich qualitative data — interview transcripts, open-ended survey responses, narrative reports, reflection journals — and never extract systematic insights from them because manual analysis is simply too labor-intensive.
This creates a paradox: the most insightful data sits unanalyzed while organizations make decisions based solely on quantitative metrics that tell them what happened but not why. AI document analysis resolves this by making qualitative analysis economically feasible at scale. Every transcript, every narrative, every open-ended response can be analyzed with the same rigor applied to structured numerical data.
Sopact Sense transforms document review through its Intelligent Suite — a four-layer analytical engine that processes documents at every level of granularity.
At the most granular level, Intelligent Cell analyzes individual data points. Upload a 100-page PDF, and Intelligent Cell extracts summaries, applies rubric scores, performs sentiment analysis, identifies themes, and codes content against your analytical framework — all from a single prompt in plain English.
What Intelligent Cell does with documents:
Example: An accelerator program receives 500 startup pitch decks as PDF uploads. Intelligent Cell scores each deck against a 6-dimension rubric (market opportunity, team strength, traction, social impact alignment, scalability, financial sustainability), extracts key claims, and flags contradictions between stated traction metrics and financial projections. Five hundred deck reviews completed in hours, not months.
Intelligent Row synthesizes all data associated with a single entity — their application form, uploaded documents, interview transcript, and assessment scores — into a unified plain-language summary. This is the "complete picture" for each applicant, grantee, or portfolio company.
Example: A scholarship program evaluates each applicant across their motivation essay (analyzed by Intelligent Cell), teacher recommendation letter (analyzed by Intelligent Cell), academic transcript (structured data), and interview notes. Intelligent Row combines all four into a single applicant summary: "Strong candidate with demonstrated leadership in community health initiatives. Essay scored 4.2/5 on innovation dimension. Teacher recommendation highlights collaborative problem-solving but notes time management concerns. Academic performance above cohort median."
Intelligent Column analyzes patterns across all documents in a specific category. It answers questions like: "What are the most common themes across all 200 narrative reports?" or "How do rubric scores correlate with program outcomes?"
Example: A CSR advisory firm collects sustainability reports from 50 portfolio companies. Intelligent Column surfaces that 72% mention supply chain risk but only 18% provide quantified mitigation strategies. This cross-document insight — invisible in one-at-a-time review — becomes the foundation for strategic advisory recommendations.
Intelligent Grid generates comprehensive analytical reports that combine quantitative metrics with qualitative evidence across entire cohorts or portfolios. These are board-ready briefs that include executive summaries, KPI dashboards, equity breakdowns, representative quotes, and recommended actions.
Example: A foundation's annual report draws from 150 grantee narrative submissions, 30 site visit reports, and quantitative outcome data. Intelligent Grid produces a 12-page impact brief with thematic analysis, outcome correlations, and evidence-linked recommendations — in hours, not the 3 months traditional manual synthesis requires.
The challenge: A foundation receives 800 scholarship applications annually. Each includes a personal essay, recommendation letter, financial documentation, and academic transcript. Manual review requires a panel of 8 reviewers working for 10 weeks.
With Sopact Sense: Applications are collected through a unified intake form with unique applicant IDs. Uploaded documents (essays, recommendation letters, transcripts) are immediately analyzed by Intelligent Cell using the foundation's rubric criteria. Intelligent Row generates a complete applicant summary combining all data sources. Intelligent Column identifies patterns across the applicant pool (common themes, score distributions, equity indicators). Intelligent Grid produces a shortlist report with evidence-linked justifications.
Result: 800 applications reviewed in 2 days. Reviewer panel focuses on the top 100 candidates for nuanced human evaluation, saving 8 weeks of initial screening time.
The challenge: A management consulting firm advises institutional investors on ESG performance across a 50-company portfolio. Each company submits a sustainability report (30–100 pages). The firm must standardize disclosures, identify gaps, and produce a comparative analysis.
With Sopact Sense: Reports are uploaded and analyzed against ESG framework categories (Environmental, Social, Governance). Intelligent Cell extracts disclosure completeness scores, flags missing categories, and identifies greenwashing indicators. Intelligent Column produces cross-portfolio comparisons: which companies lead on environmental disclosure, which lag on governance transparency. Intelligent Grid generates an investor-ready portfolio ESG brief.
Result: Portfolio analysis completed in 3 days instead of 6 weeks. The firm delivers advisory recommendations while competitors are still in data extraction.
The challenge: An evaluation firm conducts 60 stakeholder interviews for a multi-site program evaluation. Transcripts total 1,200 pages. Traditional qualitative coding would require 3 analysts working for 4 weeks.
With Sopact Sense: Interview transcripts are uploaded and analyzed using deductive coding against the program's Theory of Change. Intelligent Cell identifies themes, extracts evidence quotes, and codes each transcript. Intelligent Column surfaces cross-interview patterns — which themes appear at which program sites, which stakeholder groups report different experiences. Intelligent Grid produces the evaluation findings chapter with thematic matrices and representative quotations.
Result: Qualitative analysis completed in 1 day. Analysts focus on interpretation and recommendation development rather than data processing.
The challenge: An impact accelerator receives 1,000 applications per cohort. Each includes a pitch deck, impact thesis statement, and founding team bio. The review committee needs a defensible shortlist of 25.
With Sopact Sense: Applications flow through multi-stage intake. Phase 1 uses Intelligent Cell to score pitch decks against rubric dimensions (market size, team capability, impact alignment, traction, scalability). Phase 2 uses Intelligent Row to synthesize deck analysis, impact statement, and team assessment into candidate profiles. Phase 3 uses Intelligent Grid to produce a comparative matrix ranking all candidates with evidence citations.
Result: 1,000 → 100 shortlist generated in hours. Review committee makes final 100 → 25 selection with full analytical context, cutting pre-review time by 80%.
The challenge: A regulatory affairs team must verify that 200 submitted compliance documents meet specific requirements — correct sections present, required disclosures complete, no contradictory statements.
With Sopact Sense: Intelligent Cell checks each document against a completeness rubric, flags missing required sections, identifies contradictory language, and generates a compliance status summary. Self-correction links allow submitters to fix flagged issues and resubmit. Intelligent Column provides an aggregate compliance status dashboard showing which requirements are most commonly incomplete.
Result: Compliance review completed in real time as documents are submitted. Staff time redirected from checklist verification to substantive regulatory analysis.
Most organizations treat PDFs as filing cabinets — static files stored on shared drives, opened one at a time, read manually, and summarized in separate spreadsheets. AI PDF analysis transforms these passive documents into active sources of structured intelligence.
With Sopact Sense, AI PDF analysis works through a simple three-step loop. First, upload any PDF — grant reports, sustainability disclosures, evaluation narratives, pitch decks, compliance filings — through a structured intake form tied to a unique entity ID. Second, define what you want extracted using plain-English prompts: rubric scores, thematic codes, risk flags, completeness checks, or executive summaries. Third, receive structured analytical outputs that sit alongside all other data collected for that entity, ready for cross-referencing and reporting.
The difference between AI PDF analysis and traditional document handling is not incremental. A program officer who manually reads and summarizes 50 grantee annual reports (averaging 25 pages each) dedicates roughly 125 hours — more than three full work weeks — to extraction alone, before any comparative analysis begins. Sopact Sense's Intelligent Cell processes the same 50 reports in under 4 hours, applying identical analytical criteria to every document and producing structured outputs that feed directly into Intelligent Column and Grid analyses.
What makes this approach particularly powerful is that AI PDF analysis does not require documents to follow a standard template. Reports from different organizations with different formats, structures, and writing styles are all analyzed against the same evaluation framework. Sopact Sense's AI reads context, not just keywords — understanding that "community partnerships" in one report refers to the same theme as "stakeholder collaboration" in another.
For organizations managing portfolios, cohorts, or multi-site programs, AI PDF analysis eliminates the single biggest bottleneck in evidence-based decision-making: the gap between collecting rich qualitative documents and actually extracting usable intelligence from them.
Automated document review eliminates the most labor-intensive phase of any application, compliance, or reporting workflow — the initial read-and-sort cycle that consumes staff weeks before substantive evaluation even begins.
In traditional workflows, documents arrive through email, portals, or shared drives. Staff members open each file individually, verify completeness, flag missing sections, score content against evaluation criteria, compile notes in spreadsheets, and reconcile reviewer differences. For organizations processing hundreds of submissions per cycle, this manual triage represents the largest single time cost in their operations.
Sopact Sense automates every step of this pipeline. When a document is submitted through a structured intake form, automated document review begins immediately — no batching, no manual trigger. Intelligent Cell checks completeness against your requirement template, scores content against your rubric dimensions, extracts key themes and evidence, and flags potential issues. If a submission is missing required sections, a self-correction link is automatically generated, allowing the submitter to fix and resubmit without staff intervention.
This is not a simple keyword scanner. Automated document review with Sopact Sense applies genuine comprehension to qualitative content. An essay scored for "leadership potential" is evaluated on the substance of the applicant's narrative — specific examples, demonstrated impact, articulated vision — not on whether the word "leadership" appears a certain number of times.
The downstream effects compound. When automated document review handles initial triage, human reviewers focus exclusively on the substantive evaluation of pre-qualified submissions. A scholarship program that previously required 8 reviewers working 10 weeks to process 800 applications can now direct its review panel to the top 100 candidates in 2 days, with full analytical context already generated for each applicant.
The fundamental challenge of AI data extraction from PDF documents is converting information that exists in narrative form — paragraphs, tables, charts, freeform text — into structured, queryable data that can be analyzed, compared, and reported on at scale.
Traditional approaches to this problem rely on template-based extraction or optical character recognition (OCR), both of which fail when documents vary in format, structure, or content organization. A sustainability report from Company A has different section headings, different metrics, and different narrative structures than a report from Company B. Template-based extractors break. OCR captures characters but not meaning.
Sopact Sense's approach to AI data extraction from PDF goes beyond character recognition into semantic comprehension. The AI reads documents the way an analyst would — understanding that a section titled "Environmental Commitments" contains the same type of information as one titled "Sustainability Targets," even though the labels differ. It extracts not just data points but relationships between data points, contextual qualifiers, and the evidentiary basis for claims made in the document.
The extracted data does not land in an isolated spreadsheet. Because every document enters the system through structured intake tied to unique entity IDs, extracted data automatically joins with all other information collected for that entity — survey responses, prior submissions, quantitative metrics, interview notes. This creates complete entity profiles where document-extracted insights are immediately available for cross-referencing and pattern analysis.
For practical application, consider an ESG advisory firm collecting sustainability disclosures from 50 portfolio companies. AI data extraction from PDF processes each report to extract standardized data across environmental, social, and governance categories — regardless of how each company has structured its disclosure. The firm receives a normalized dataset with completeness scores, identified gaps, and comparative analysis across the full portfolio.
Qualitative document analysis applies the analytical rigor of formal qualitative research methods — thematic coding, deductive analysis, grounded theory, framework analysis — to document collections at a scale that manual coding cannot achieve.
In evaluation research and program assessment, qualitative document analysis is where the most actionable insights live. Quantitative metrics tell you what happened. Qualitative analysis of stakeholder narratives, interview transcripts, program reports, and open-ended responses tells you why it happened, what barriers exist, and what should change. The problem has always been cost: manually coding 60 interview transcripts using a thematic framework requires 3-4 analysts working for a month.
Sopact Sense transforms qualitative document analysis from a capacity-limited luxury into a standard operating procedure. Upload your document collection — interview transcripts, evaluation narratives, annual reports, reflection journals — and define your analytical framework using plain-English prompts. The AI applies deductive coding against your predefined themes, identifies emergent themes that fall outside your framework, extracts representative quotations with source citations, and produces thematic matrices showing pattern distribution across your dataset.
The critical advantage is not just speed but consistency. When a human analyst codes the 40th transcript, they have already been influenced by patterns observed in the first 39. Cognitive fatigue, confirmation bias, and interpretation drift are well-documented challenges in qualitative research. AI-powered qualitative document analysis applies the same interpretive framework to every document with identical analytical rigor.
Intelligent Column extends single-document analysis into cross-document pattern detection. After individual documents are coded by Intelligent Cell, Column analysis surfaces which themes appear across which subgroups, which program sites report different stakeholder experiences, and which categories show the strongest evidence base. These patterns — invisible when documents are reviewed one at a time — become the foundation for strategic recommendations and program improvement decisions.
AI report analysis addresses a specific high-value use case: the systematic processing of recurring reports from grantees, portfolio companies, program sites, or organizational partners into aggregated intelligence that drives strategic decisions.
Every funder, accelerator, advisory firm, and membership organization receives periodic reports from the entities they support or serve. Quarterly narrative reports from grantees. Annual sustainability disclosures from portfolio companies. Progress updates from accelerator cohorts. Compliance filings from regulated entities. The common thread is volume multiplied by complexity — dozens or hundreds of multi-page reports arriving on a recurring cycle, each requiring extraction, standardization, and comparative analysis.
Manual AI report analysis at this scale produces two predictable outcomes. Either organizations invest enormous staff resources in reading and synthesizing every report — consuming weeks that could be spent on strategic activities — or they resort to surface-level review that misses critical insights buried in narrative text. Both outcomes represent avoidable costs.
Sopact Sense's approach to AI report analysis operates across all four layers of the Intelligent Suite. Intelligent Cell processes each individual report against your analytical framework — extracting performance metrics, scoring narrative quality, identifying risks, and summarizing key findings with evidence citations. Intelligent Row combines report analysis with all other data for that entity, creating a complete profile that shows how narrative claims align with quantitative performance data. Intelligent Column surfaces cross-portfolio patterns: which grantees report similar challenges, which sectors show stronger outcomes, which compliance categories are most commonly incomplete. Intelligent Grid generates the board-ready brief — an aggregated analytical report that combines quantitative dashboards with qualitative evidence, thematic analysis, and recommended actions.
The transformation is measurable. A foundation that previously dedicated 3 months of staff time to annual grantee report synthesis now generates the same analytical output in days, with greater depth, full evidence trails, and the ability to answer ad-hoc questions by querying the underlying data rather than re-reading source documents.
The comparison between AI-powered document analysis and traditional manual review reveals fundamental differences in speed, consistency, and analytical depth.
Speed: Manual review of 100 multi-page documents takes 4–8 weeks with a dedicated team. AI document analysis processes the same volume in hours. The difference is not incremental improvement — it is an order-of-magnitude reduction in time-to-insight.
Consistency: Human reviewers exhibit natural variance in attention, interpretation, and scoring. The 50th document reviewed receives different cognitive resources than the 5th. AI applies identical analytical criteria to every document, producing calibrated, comparable outputs.
Scalability: Manual review scales linearly — twice the documents requires twice the reviewers or twice the time. AI document analysis scales near-instantly. Processing 1,000 documents takes marginally more time than processing 100.
Audit trail: Manual review typically produces notes, spreadsheets, and summaries that are difficult to trace back to source evidence. AI document analysis maintains explicit links between every finding, score, and theme and its source location in the original document.
Qualitative depth: Manual review often treats qualitative data as secondary to quantitative metrics because the analysis cost is prohibitive. AI makes qualitative analysis economically viable at any scale, enabling organizations to extract insights from data they previously ignored.
Cost: A team of 5 analysts reviewing 500 documents for 8 weeks represents $40,000–$80,000 in fully loaded staff costs. AI document analysis reduces this to a fraction while producing more consistent, more thorough, and faster results.
Every document enters the system through structured intake forms tied to unique entity IDs. An applicant's essay, recommendation letter, and transcript are all linked to a single persistent identifier. No orphaned files. No ambiguous attribution. No duplicate submissions.
Documents can be uploaded as PDFs, Word files, or text entries. Each upload is immediately associated with the submitting entity and available for AI analysis.
Configure what you want to extract using plain-English instructions. No coding, no technical configuration. Examples:
Analysis runs automatically as documents are submitted. Results appear as structured columns in your data grid — scores, themes, summaries, and flags alongside the original document and all other entity data. No manual trigger, no batch processing delay.
Intelligent Column and Grid analyses aggregate document-level findings into portfolio, cohort, or program-level insights. Patterns that are invisible in individual documents become clear across the collection.
Board-ready reports are generated directly from the analytical outputs. Evidence is linked, quotes are cited, and findings are structured for stakeholder communication. Reports can be generated in any language supported by the AI model.
AI document analysis handles PDFs, Word documents, scanned forms, interview transcripts, open-ended survey responses, pitch decks, compliance filings, narrative reports, and evaluation documents ranging from 5 to 200+ pages. Sopact Sense's Intelligent Cell processes any text-based document uploaded through its data collection forms.
AI document analysis achieves consistency rates above 95% for structured extraction tasks like rubric scoring and compliance checking. The primary advantage over human review is not raw accuracy but consistency — AI applies identical criteria to every document without fatigue, anchoring bias, or attention degradation. Human reviewers remain essential for nuanced judgment, but AI handles the systematic analytical workload.
Yes. Sopact Sense's Intelligent Cell accepts custom rubric criteria defined in plain English. You specify the dimensions, scoring scales, and evaluation standards. The AI applies them consistently across all documents, providing scores with evidence citations from the source material. Rubrics can be updated and reapplied without reprocessing from scratch.
Sopact Sense provides dedicated database instances per customer with role-based access controls. Data is encrypted at rest and in transit. Documents are not used to train AI models without explicit permission. On-premise deployment is available for organizations with strict data governance requirements.
OCR (optical character recognition) converts scanned images of text into machine-readable characters. AI document analysis goes far beyond OCR — it understands context, extracts meaning, applies evaluation criteria, identifies themes, detects sentiment, and produces structured analytical outputs. OCR is one input step; AI document analysis is the complete analytical pipeline.
Processing time varies by document length and analysis complexity, but typical timelines are dramatically faster than manual review. A set of 500 application essays (2-5 pages each) with rubric scoring completes in 2-4 hours. A portfolio of 50 sustainability reports (30-100 pages each) with thematic extraction completes in 4-8 hours. Manual equivalents require 6-16 weeks.
Yes. Sopact Sense processes documents in any language and can generate analytical outputs and reports in languages supported by the underlying AI models. This enables organizations with international portfolios to analyze documents in their original language while producing standardized English-language reports for stakeholders.
Sopact Sense's document analysis is not a standalone tool — it is embedded within the platform's unified data collection architecture. Documents uploaded through intake forms are automatically linked to unique entity IDs, associated with all other collected data (survey responses, quantitative metrics, prior submissions), and available for cross-referencing in Column and Grid analyses.
AI PDF analysis uses artificial intelligence to read, interpret, and extract structured data from PDF documents. Unlike basic OCR that only recognizes characters, AI PDF analysis understands context, applies evaluation rubrics, identifies themes, and produces analytical outputs such as scores, summaries, and compliance flags. Sopact Sense processes PDFs uploaded through structured intake forms, linking every document to unique entity IDs for cross-referencing.
Automated document review eliminates manual triage by processing submissions instantly as they arrive. Sopact Sense checks completeness against requirement templates, applies rubric scoring, extracts themes, and flags issues automatically. Self-correction links allow submitters to fix problems without staff intervention. Organizations typically reduce document review cycles from 6-12 weeks to 2-4 hours.
Yes. AI data extraction from PDF converts narrative text, tables, and freeform content into structured, queryable datasets. Sopact Sense's Intelligent Cell reads context rather than relying on templates, meaning documents with different formats and structures are all analyzed against the same evaluation framework. Extracted data automatically joins with other entity data through persistent unique IDs.
Qualitative document analysis applies formal research methods — thematic coding, deductive analysis, and framework analysis — to document collections at scale. Sopact Sense codes documents against predefined themes, identifies emergent patterns, extracts representative quotations with source citations, and produces thematic matrices. This makes rigorous qualitative analysis feasible for collections of 50-500+ documents.
AI report analysis systematically processes recurring reports from grantees, portfolio companies, or program partners into aggregated intelligence. Sopact Sense extracts performance metrics, scores narrative quality, identifies cross-portfolio patterns, and generates board-ready briefs combining quantitative dashboards with qualitative evidence. A synthesis that previously took 3 months of staff time completes in days.
OCR converts scanned images of text into machine-readable characters — it captures letters but not meaning. AI document analysis goes far beyond character recognition to understand context, evaluate content quality, apply rubric scoring, identify thematic patterns, detect sentiment, and produce structured analytical outputs. OCR is a single input step; AI document analysis is the complete analytical pipeline from raw document to actionable insight.
Stop spending weeks on manual document review. See how Sopact Sense transforms PDFs, transcripts, and narrative reports into structured intelligence — with rubric scoring, thematic analysis, and board-ready reports generated in hours.
