--- description: Get detailed information about dFL usability, features, price, benefits and disadvantages from verified user experiences. Read reviews and discover similar tools on Capterra Israel. image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: dFL Price, Reviews & Ratings - Capterra Israel 2026 --- Breadcrumb: [Home](/) > [Data Analysis Software](/directory/31077/data-analysis/software) > [dFL](/software/1106416/dFL) # dFL Canonical: https://www.capterra.co.il/software/1106416/dFL > The \#1 labeling platform for sensor AI. Harmonize, label \& export multimodal sensor data for ML. Visual DSP, autolabel SDK, provenance. > > Verdict: Rated **4.2/5** by 5 users. Top-rated for **Likelihood to recommend**. ----- ## Overview ### Who Uses dFL? Research teams building ML models from multimodal sensor data—plasma, robotics, and additive-manufacturing engineers at national labs and universities labeling and exporting sensor time-series. ## Quick Stats & Ratings | Metric | Rating | Detail | | **Overall** | **4.2/5** | 5 Reviews | | Ease of Use | 4.0/5 | Based on overall reviews | | Customer Support Software | 5.0/5 | Based on overall reviews | | Value for Money | 4.6/5 | Based on overall reviews | | Features | 4.8/5 | Based on overall reviews | | Recommendation percentage | 80% | (8/10 Likelihood to recommend) | ## About the vendor - **Company**: Sophelio ## Commercial Context - **Starting Price**: US$0.00 - **Pricing model**: Flat Rate (Free version available) (Free Trial) - **Target Audience**: Self Employed, 2–10, 11–50, 51–200, 201–500, 501–1,000, 1,001–5,000, 5,001–10,000, 10,000+ - **Deployment & Platforms**: Mac (Desktop), Windows (Desktop), Linux (Desktop), Windows (On-Premise), Linux (On-Premise) - **Supported Languages**: English - **Available Countries**: Angola, Argentina, Aruba, Australia, Austria, Bahamas, Bahrain, Belgium, Bermuda, Bosnia & Herzegovina, Botswana, Brazil, Bulgaria, Canada, Cayman Islands, Chile, China, Colombia, Costa Rica, Croatia and 68 more ## Features - Dashboard Software - Data Connectors - Data Discovery Software - Data Visualization Software ## Integrations (4 total) - AWS for Data - Google Cloud - Hugging Face - TensorFlow ## Support Options - Email/Help Desk - FAQs/Forum - Knowledge Base Software - Phone Support - Chat ## Category - [Data Analysis Software](https://www.capterra.co.il/directory/31077/data-analysis/software) ## Alternatives 1. 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The Python Data Provider approach is also a strong point for technical users because it gives flexibility for custom datasets and real engineering workflows. > > **Cons**: Because I tested it during beta, some parts still need polish. The first-run onboarding and custom script setup could be clearer, especially for explaining that a Python Data Provider is required. I also saw some friction around license status clarity, graph UI behavior, autolabeling review, and settings/layout persistence. These issues did not stop me from completing the workflow, but they made the first experience less smooth. > > I tested dFL as part of a beta evaluation using a small wake-word detection style dataset from my own audio/time-series workflow. For each sample, I generated multiple signal views, including a waveform stream and RMS energy features at different sampling rates. This helped me test dFL as a multi-rate signal labeling tool rather than just a basic audio viewer. Overall, the core workflow was promising. I was able to load my dataset through a custom Python Data Provider, inspect signals in different graph views, create manual labels, view and export labels, trim time windows, try autolabeling, and export data for downstream ML use. The signal-first approach is the main value for me, especially compared with more generic labeling tools. The product still feels like a beta in some areas, especially around onboarding, UI state, and persistence, but the direction is strong. I think dFL can be useful for engineers working with sensor, audio, or other time-series data where alignment, labeling, and export matter. ----- ### "Strong provenance and bulk performance, held back by discoverability" — 4.0/5 > **Hanene** | *20 July 2026* | Education Management | Recommendation rating: 8.0/10 > > **Pros**: Real provenance, not just labels. Every export ships a metadata file with the exact parameters used — I verified this myself down to the exact decimal between what dFL displayed and what it exported. Bulk operations are genuinely fast: autolabeling 90 records took about two seconds with clear progress reporting. I also wrote a custom Python normalization filter and it ran inside dFL bit-for-bit identical to my own reference implementation. For anyone doing reproducible ML on sensor or time-series data, that level of traceability is rare. > > **Cons**: Some strong features are hard to find — the native statistical autolabelers, for instance, are hidden behind a graph type rather than listed where you'd expect. There's no live preview when reordering preprocessing steps, so comparing two configurations means reapplying and re-screenshotting each time. A few default parameter values (like smoothing strength) are set to essentially "off," which can make a first attempt look broken when it isn't. > > I used dFL to prepare a 9-to-90-record RF transient dataset for a device classification task, plus a second public dataset with real missing-data gaps, to put the full 8-step workflow through its paces. The strongest part of the product is reproducibility: exports carry rich metadata (exact operation order, exact parameters), and I confirmed values matched on-screen readings to the last digit. Custom Python filters integrate cleanly and execute with no measurable deviation from my own code. Bulk operations scale well — autolabeling and exporting 90 records both completed in seconds with good progress feedback. Where dFL loses points is discoverability and feedback: some of the best features (native autolabelers, certain harmonization options) aren't where you'd look first, and there's no live preview when you change preprocessing order, so iterating on a configuration takes more manual steps than it should. None of this blocked my work, but a newcomer will hit friction in the first session. Overall, a genuinely capable tool for multimodal time-series labeling, let down a little by onboarding and UI discoverability rather than by its core engine. ----- ### "Strong sensor data labeling workflow, with minor usability friction" — 4.0/5 > **Hajar** | *21 July 2026* | Computer Software | Recommendation rating: 8.0/10 > > **Pros**: I liked how the application guides the complete annotation workflow in one place. Data import using a custom Python provider worked smoothly, graph visualization was responsive, trimming updated all graphs consistently, and the built-in autolabeling workflow performed reliably. The harmonization tools were easy to use, and both graph export and bulk export generated well-structured CSV and metadata files that I could successfully verify using Python. Overall, the core workflow felt organized and straightforward. > > **Cons**: The main limitation I observed was in the manual labeling workflow. I encountered inconsistent behavior when working with custom labels, including server-side errors during some save/update operations. I also noticed synchronization issues between graph panels after reloading the workspace. In addition, some interface feedback could be more intuitive, particularly around layout management, where newly created layouts sometimes required a manual refresh before becoming selectable. > > I used dFL Labeler to test the complete workflow with the UCI Human Activity Recognition dataset. Overall, my experience was positive because most core features worked as expected. Data ingestion, graph visualization, trimming, autolabeling, harmonization, and export all completed successfully without stability or performance issues during my testing. The documentation also matched the implemented functionality, making it easy to follow the workflow. While I encountered a functional issue in manual labeling and a few usability issues related to layout management, these did not prevent me from completing the evaluation. Overall, I found dFL to be a promising annotation platform with a solid core workflow that would benefit from improvements to manual labeling reliability and a few user interface details. ----- ### "Strong multi-rate sensor labeling and harmonization workflow" — 4.0/5 > **Andrés Felipe** | *14 July 2026* | Information Technology & Services | Recommendation rating: 9.0/10 > > **Pros**: The strongest part of dFL is its end-to-end workflow for multi-rate sensor data. I was able to load pressure, flow, temperature, and vibration signals sampled at 100, 10, and 1 Hz, visualize them together, trim the data, create manual labels, run an automatic zero-crossing labeler, and export the results. The harmonization pipeline is flexible, and the JSON provenance metadata makes the processing steps reproducible. Local desktop processing is also valuable for sensitive or large datasets. > > **Cons**: Some of the searchable transformation dropdowns were not immediately intuitive and could provide clearer guidance. Bulk Export completed successfully, but it remained on “Updating” for approximately 90 seconds without showing a progress percentage or estimated completion time. A newly created Project Layout also did not appear immediately in the layout selector. None of these issues blocked the workflow. > > I evaluated dFL using a hydraulic condition-monitoring dataset with sensors operating at different sampling rates. The complete workflow—from ingestion and visualization to labeling, harmonization, and export—worked successfully. I especially appreciated being able to preview preprocessing changes and then verify the exact settings in the exported metadata. The processed output opened correctly in Python and Pandas with aligned timestamps and no missing values. Overall, dFL is a promising and effective platform for preparing complex sensor time-series data, although a few interface and progress-feedback improvements would make the initial experience smoother. ----- ### "Strong multi-rate sensor harmonization, rough onboarding" — 5.0/5 > **CHENXI** | *15 July 2026* | Hospital & Health Care | Recommendation rating: 8.0/10 > > **Pros**: The harmonization fill is excellent — one step took a 90.9%-missing channel to 0.004%, which is otherwise fiddly to script by hand. Provenance metadata on every export is a genuine plus for reproducible pipelines. Responsive graphing, working themes/multi-graph, and trimming that keeps large data snappy. > > **Cons**: Getting data in is the hard part — no direct CSV loader, so you must hand-write a Python "Data Provider" script (documented only for Parquet). A couple of controls need discovery rather than being obvious: autolabeling produces nothing until you raise smoothing above zero, and the label datetime fields take a strict format with no hint. > > I tested dFL on a public multimodal sensor dataset (100 Hz IMUs plus a \~9 Hz heart-rate channel that was 90% missing) to evaluate how it harmonizes signals sampled at different rates. Its core strength is real: a single interpolation step took the heart-rate channel from 90.9% missing to essentially zero, and every export ships a provenance-metadata file recording the exact fill/resample/smooth options and order — great for reproducibility. Graphing, themes, multi-graph layouts and trimming all work well. The rough edge is onboarding: ingestion needs a hand-written Python "Data Provider" script rather than a direct CSV load, and a few controls need discovery. Strong core, rough first run. ## Links - [View on Capterra](https://www.capterra.co.il/software/1106416/dFL) ## This page is available in the following languages | Locale | URL | | en | | | en-AE | | | en-AU | | | en-CA | | | en-GB | | | en-IE | | | en-IL | | | en-IN | | | en-NZ | | | en-SG | | | en-ZA | | ----- ## Structured Data