Build and deliver a rigorous qualitative analysis in days, not months. Learn how Sopact Sense automates open-ended feedback and document analysis with AI-ready data.
Data teams spend the bulk of their day fixing silos, typos, and duplicates instead of generating insights.
Data teams spend the bulk of their day fixing silos, typos, and duplicates instead of generating insights.
Hard to coordinate design, data entry, and stakeholder input across departments, leading to inefficiencies and silos.
Open-ended feedback, documents, images, and video sit unused—impossible to analyze at scale.
From One-Time Reports to Continuous Insight
By Unmesh Sheth, Founder & CEO, Sopact
For years, organizations have treated qualitative data analysis as a task to complete at the end of a project. Surveys are closed, interviews transcribed, and teams spend weeks reading, coding, and summarizing. By the time the report is ready, the decisions that matter have already been made.
That model no longer fits how data moves today.
At Sopact, we see qualitative analysis as a continuous feedback system—not a phase. It starts with clean data collection, keeps stakeholder identity intact, and uses AI to interpret stories the moment they’re shared. The goal isn’t to produce another document; it’s to help teams learn faster and act with clarity.
“The real power of qualitative analysis isn’t in explaining what happened. It’s in giving you the confidence to change what happens next.” — Unmesh Sheth, Founder & CEO, Sopact
Qualitative data analysis (QDA) is how organizations make sense of unstructured information—comments, interviews, narratives, or open-ended survey responses. It reveals patterns that numbers alone can’t show: what people value, where they struggle, and why outcomes differ.
In traditional research, analysts imported transcripts into tools like NVivo or Atlas.ti and coded them line by line. Those platforms were designed for academic rigor, not operational speed. They help you understand, but they don’t help you keep up.
Modern qualitative analysis platforms such as Thematic and Sopact have transformed that process. They use AI to extract patterns automatically, but the philosophies differ. Thematic focuses on analyzing unstructured text once it’s collected; Sopact begins earlier—by collecting clean, identity-linked data from the start. That simple change eliminates hours of cleanup and ensures every insight remains connected to a real person, program, or cohort.
Think of it as shifting from post-mortem analysis to real-time understanding.
Automation means nothing if your data is still fragmented. Clean collection is the foundation of meaningful AI.
Numbers tell you what changed; stories tell you why.
Without qualitative context, teams are left guessing about causation.
Consider a workforce training program. Quantitative data shows that 82 percent of participants improved their technical confidence. That’s good news—but qualitative feedback explains why: participants who had peer mentors progressed faster, while those who lacked reliable internet access fell behind.
When stories and metrics live together, strategy becomes evidence-based instead of assumption-based.
Sopact turns that integration into daily practice. Each response—whether from a form, an interview, or a PDF report—is analyzed instantly and linked back to its owner’s profile. You don’t wait for the next survey cycle to learn what’s working; the insight appears as soon as the feedback arrives.
The result: qualitative analysis stops being a periodic report and becomes a living system of learning.
For decades, qualitative data analysis was a manual craft. Researchers used Excel sheets or CAQDAS tools like NVivo, Atlas.ti, or MAXQDA to highlight text, tag codes, and group themes. The process worked for dissertations and focus groups, but it breaks under today’s data volumes and expectations for speed.
Three recurring issues keep organizations stuck in this outdated cycle.
Surveys live in one platform, interviews in another, and PDFs in cloud folders. Without unique identifiers, linking them is almost impossible. Teams spend most of their time reconciling duplicates or guessing which response belongs to whom. That’s not analysis—it’s archaeology.
Even with CAQDAS tools, human coders must define themes, assign them, and ensure consistency across reviewers. It’s slow, inconsistent, and hard to replicate. Two analysts can read the same paragraph and reach different conclusions. That’s fine for small research, not for managing a live program or portfolio.
By the time the report is polished, the insights are outdated. Feedback loses its edge when it arrives months later. Teams cannot adapt to change if their learning cycle takes an entire quarter.
The faster your organization learns from stakeholder data, the stronger your outcomes become. Speed isn’t a luxury—it’s a feedback ethic.
<blockquote> “Continuous feedback turns reporting into reflection. That’s how organizations build evidence without breaking momentum.” <br>— Unmesh Sheth</blockquote>
Imagine a workforce training program evaluating both skill growth and confidence. In the past, correlating test scores with participant confidence comments would have taken weeks of coding. Now, with Intelligent Columns, the team simply selects the two fields, types an instruction, and receives a correlation analysis in minutes.
Sometimes results are clear—confidence and performance rise together. Sometimes they’re mixed—confidence lags despite higher scores. Either way, leaders now see the full story, instantly, and can adapt programs in real time.
Imagine a foundation funding dozens of workforce programs. Each grantee submits reports filled with participant stories. Traditionally, analysts spend weeks coding and summarizing themes.
With Sopact, responses enter cleanly, themes and sentiments are extracted in seconds, and correlations appear immediately—like “mentor support” aligning with higher retention.
Leaders act faster because evidence is live.
Impact isn’t measured once a year anymore. It’s observed every day through living data.
Qualitative data analysis has evolved from slow, manual interpretation to continuous organizational learning.
Thematic pioneered automation for customer feedback; Sopact extended it to mission-driven ecosystems.
By combining clean-at-source collection, AI-driven analysis, and continuous feedback, Sopact turns scattered stories into strategy—instantly.
Stop chasing data. Start learning from it.
Most organizations collect qualitative data from many different sources—long PDF reports, hundreds of Zoom interviews, or open-ended survey responses. The challenge isn’t gathering the data; it’s turning that mountain of text into reliable, actionable insight.
That’s where Sopact Sense’s Intelligent Suite comes in.
It works like a multi-layered engine that reads, understands, and translates qualitative data into clear patterns—without losing nuance.
Let’s walk through how it handles three real-world data sources and the layers that make it possible.
Source of qualitative data:
Impact reports, compliance reviews, or grantee updates often run hundreds of pages. Reading every sentence manually is impossible.
Goal / Outcome:
You need to extract summaries, key findings, risks, and outcomes—fast.
How Sopact Sense works:
This turns document review from a 3-week manual exercise into a 10-minute automated insight.
Whether it’s a 5-page memo or a 100-page report, Intelligent Cell delivers consistency that manual reading never can.
Source of qualitative data:
Recorded interviews from accelerators, mentorship programs, or user research.
Goal / Outcome:
Understand common barriers, motivations, and growth stories across all participants.
How Sopact Sense works:
This helps analysts move from anecdotal insights to pattern-based learning while preserving individual voice.
Source of qualitative data:
Pre/mid/post program surveys and ongoing forms (open-text fields).
Goal / outcome:
Correlate what people said with what changed in their scores (satisfaction, confidence, completion).
How Sopact Sense works (in plain English):
Example Prompt + Output (visual cards):
Source of qualitative data:
Batches of PDFs (5–100 pages each): partner policies, MOUs, audits.
Goal / outcome:
Flag risk, missing clauses, or non-compliance quickly and route to the right reviewer.
How it works:
Prompt + Output:
Source of qualitative data:
NPS verbatims, app store reviews, support tickets.
Goal / outcome:
Explain why detractors score low and what turns passives into promoters.
How it works:
Prompt + Output:
Source of qualitative data:
Essays, recommendation letters, interview notes (multi-format, multi-rater).
Goal / outcome:
Summarize each applicant consistently, surface readiness signals, and preserve auditability.
How it works:
Prompt + Output:
*this is a footnote example to give a piece of extra information.
View more FAQs
