DigniFeed: accountable operations for food distribution

Prepared by Education Angel Group (EAG), with AI-assisted research and drafting. Research date: September 19, 2026. EAG is developing DigniFeed and has an interest in its success. This is a source-led examination of its proposed role, not an independent evaluation or evidence that the product is operating successfully.

1. Proposition, audience and present limits

DigniFeed is proposed as an AI-assisted operating system for fairer food drives. Its intended work connects intake, inventory, participant preferences, explained allocation options, queues, fulfillment and correction. Authorized people retain distribution and exception authority. The product does not determine eligibility, replace food-safety judgment or rank recipients by deservingness. These are EAG's governing boundaries, not demonstrated performance. EAG product definition, A0

The immediate audiences are the person recording deliveries, the person reconciling stock, the person distributing goods and the supervisor responsible for resolving discrepancies. Recipients would need understandable choices, accurate information, privacy and a way to obtain help. A direct recipient interface remains a design decision. Anonymous information and receiving service need not require an online account. Operator identity and permission to change a site's records are separate matters. A participant-chosen name or symbol is only a later candidate, not an established feature.

Overview. The evidence supports investigating whether food distribution can better accommodate household preferences and make operational decisions easier to understand and correct. It does not establish that AI allocation is necessary or that new software improves fairness, food security, nutrition, waiting time or waste. Existing software already describes appointment, inventory-reservation and participant-choice functions. The relevant question is whether DigniFeed can solve a specific unresolved operational problem with less burden and adequate accountability, compared with those alternatives.

This report treats fairness as a question requiring an explicit local definition and participant input. Equal quantities, equal waiting time, preference satisfaction and serving people with different access constraints can point to different decisions. A product should expose those tensions rather than hide them behind one score. The locked tagline expresses an ambition; it is not a comparative finding.

2. Questions and evidence standards

The central question is: which intake, choice, inventory and allocation practices could help food-drive operators serve people fairly and respectfully? Six narrower questions make that investigation practical.

Question | What the available evidence contributes | What remains unknown
What need exists? | Official household food-security estimates establish context. | Demand, unmet requests and operational constraints at a particular drive.
What do recipients value? | Local interviews and larger pantry surveys identify preferences and experience priorities. | Preferences among people not using those pantries and people avoiding service.
Does a better pantry environment improve outcomes? | A randomized study supplies important null overall findings. | Effects of DigniFeed or a comparable inventory-and-correction tool.
What alternatives already exist? | First-party documentation describes operational capabilities. | Comparative cost, accessibility, burden and reliability in the intended setting.
What could software reasonably change? | A coherent record could make discrepancies and decisions reviewable. | Whether that change reduces errors without slowing or restricting service.
What would justify continuing? | Direct operational and participant measures can test a bounded hypothesis. | Baseline rates and meaningful local thresholds, which have not been collected.

Official descriptive data, qualitative accounts, randomized evaluations and vendor documentation answer different questions. None substitutes for another. A household survey cannot diagnose a site's inventory problem. Interviews cannot estimate an intervention effect. A randomized pantry-environment intervention does not directly test software. A vendor's help article does not establish usability or comparative effectiveness.

The report therefore separates source findings from EAG's inferences and proposed tests. No interviews, pantry observations, recipient records, product trial or economic evaluation were conducted for this report. Its most consequential uncertainty is whether the actual problem is a recordkeeping failure that software can address, or a shortage of food, space, transport, staffing or appropriate supply.

3. Need and context: household conditions are not a software market estimate

USDA reports that 13.7% of U.S. households experienced food insecurity at some point in 2024; 5.4% experienced very low food security. The 13.7% estimate was not statistically significantly different from 13.5% in 2023. The page was updated in March 2026, but the observation year remains 2024. These measures concern household resources for food, not the number of people seeking pantry service on a particular day. USDA ERS, D1 (https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us/key-statistics-graphics)

The matching state comparison uses pooled 2022-2024 data. Arkansas's food-insecurity estimate is 19.4%, compared with 13.3% nationally. Their published 90% margins of error are 1.67 and 0.40 percentage points. The corresponding very-low-food-security estimates are 7.6% and 5.2%, with margins of 1.52 and 0.21 points. Very low food security is a subset of food insecurity, not an additional category to add. Rabbitt and colleagues, D2, Table 4, printed p.29 (https://ers.usda.gov/sites/default/files/_laserfiche/publications/113623/ERR-358.pdf)

Household food security: Arkansas and U.S.
Household percentages, average 2022-2024. Point-estimate bars; 90% margins in report table and data alternative. Very low food security is a subset, not additive. No site demand or product effect.
These equivalents accompany the reader figures. Source IDs refer to the report references. A0 identifies EAG's product definition reviewed for the original report, not an external study or proof of availability. Other source IDs resolve to this report's reference and access record.

Figure 1. Household food security: Arkansas and U.S.

Household percentages, average 2022-2024. Point-estimate bars; 90% margins in report table and data alternative. Very low food security is a subset, not additive. No site demand or product effect.

Measure | Arkansas | United States
Food insecurity | 19.4% | 13.3%
Very low food security | 7.6% | 5.2%

Households in each geography; unknown food-security status excluded

90% margins in percentage points: food insecurity AR 1.67, US 0.40; very low AR 1.52, US 0.21. Bars show point estimates without error bars.

Published percentages unchanged; zero-based common axis; no difference or ratio calculation.

Sources: D2.
D2: https://ers.usda.gov/sites/default/files/_laserfiche/publications/113623/ERR-358.pdf

Figure 1. Published household percentages, average 2022-2024. Bars show point estimates, not uncertainty intervals; the table below supplies the 90% margins. No product effect or current site demand is inferred. Text and data alternative (#figure-text-and-data-alternatives).

Household measure | Arkansas estimate | Arkansas margin | U.S. estimate | U.S. margin
Food insecurity | 19.4% | ±1.67 points | 13.3% | ±0.40 points
Very low food security | 7.6% | ±1.52 points | 5.2% | ±0.21 points

The statistical denominator is households in each geography, excluding unknown food-security status. Three-year pooling improves state precision. It does not produce a 2024-only Arkansas estimate or identify particular households.

For DigniFeed, these data justify taking access seriously, not promising that an operational tool resolves the underlying shortage. Before choosing an intervention, a site would need to distinguish unavailable goods from unrecorded goods, an inconvenient service window from a poorly managed queue, and an unsuitable offering from an inaccurate inventory count. Those failures may look similar in a summary dashboard but require different remedies.

Regular pantries and occasional drives may also differ materially. A pantry may retain staff, storage and recurring visitors; an occasional drive may have temporary volunteers and uncertain incoming donations. This is a proposed distinction to investigate locally, not an established comparison from the retained studies. Transfer requires evidence about the actual setting.

4. Alternatives and the gap DigniFeed would need to demonstrate

PantrySoft's current help documentation describes a participant portal for registration, appointments and item selection. It describes item or category limits, inventory reservation during shopping and release after an unfinished selection times out. This makes a broad novelty claim about online pantry choice or reservations untenable. Documentation establishes described functionality only; this review did not create an account, test the software or verify performance. PantrySoft, D8 (https://support.pantrysoft.com/client-portal-quickstart-registration-scheduling-and-inventory)

A separate PantrySoft guide distinguishes an appointment from a recorded visit and explains that its open-scheduling approach does not manage pantry or staff capacity. This is useful counterevidence to treating a scheduling screen as capacity control. It also illustrates that an intended visit and completed service are different records. PantrySoft, D9 (https://support.pantrysoft.com/scheduling-client-appointments-open-scheduling)

A fair comparison must include ways of improving the existing operation without purchasing new software. EAG proposes evaluating staff-assisted choice, a locally maintained stock sheet, clear item-unit labels, an exception notebook and an accessible appointment or walk-in process. These are comparator concepts, not validated interventions or assertions that paper is always cheaper or safer. Their value depends on volume, turnover, error risk and the operator's ability to maintain them.

Compare the operational alternatives
Documented capabilities and proposed comparators are distinct. No cost ranking or comparative effectiveness established.
Figure 2. Compare the operational alternatives

Documented capabilities and proposed comparators are distinct. No cost ranking or comparative effectiveness established.

Manual process | proposed comparator: Clarify roles, stock units and corrections. Test whether an improved staff-led process meets the need. Not an evaluated intervention.

Existing software | documented: Registration, appointments and item selection. PantrySoft describes these functions; documentation is not a usability or impact trial.

DigniFeed | proposed: Connect stock, human decisions and corrections. Must demonstrate an unresolved benefit after total work and access costs are included.

Sources: A0, D8, D9.
A0. EAG product definition: /projects/dignifeed
D8: https://support.pantrysoft.com/client-portal-quickstart-registration-scheduling-and-inventory
D9: https://support.pantrysoft.com/scheduling-client-appointments-open-scheduling

Figure 2. Comparative decision frame. Documented vendor functions and proposed manual/DigniFeed options are labeled separately. No comparative trial, cost ranking or effectiveness advantage has been established. Text and data alternative (#figure-text-and-data-alternatives).

Alternative | Reason to consider it | Question before choosing
Improve the existing staff-led process | May address unclear roles or item units without a new system. | Does the process remain accurate during busy periods and shift changes?
Evaluate established operational software | Relevant functions are already documented. | Does the actual configuration support the site's service, access and correction requirements?
Pilot a narrow DigniFeed record-and-correction workflow | Could test an unresolved accountability problem directly. | Is its added value observable after including training, reconciliation and support work?
Postpone software and address capacity | Appropriate if the binding constraint is goods, storage, transport or staff. | Would buying or maintaining software divert resources from that constraint?

The most defensible potential gap is not a missing dashboard. It is a site-specific failure to connect what was physically received, what was available, what a person decided, what was actually distributed and what was subsequently corrected. That gap remains unmeasured. If an existing tool or clearer manual practice closes it satisfactorily, this research provides no reason to prefer custom development.

5. Evidence, counterevidence and transfer limits

Long and colleagues interviewed 50 adults at six Northwest Arkansas pantries in July-August 2018, using English, Spanish or Marshallese. Half came from choice pantries and half from minimal-choice pantries. Participants described wanting adequate quantity, better quality and food relevant to their households. This provides locally relevant qualitative evidence for asking what people can use, rather than treating every package as equivalent. It is a selected sample of existing clients, not random assignment or a statewide prevalence estimate. It does not measure a software effect. Long et al., D3 (https://uconnruddcenter.org/wp-content/uploads/sites/2909/2022/04/Food-Pantry-Clients-Qualitative-Study.pdf)

Caspi and colleagues' Minnesota surveys analyzed 4,321 clients from 188 pantries in the first wave and 5,529 from 220 in the second. Collection occurred in 2017-2018 and 2019-2020. Selecting one's own food was the leading stated experience priority; welcome, ease and waiting also mattered. Convenience sampling, unknown refusal and possible overlap limit representativeness. The study supports examining the whole encounter, but does not show that appointment software shortens waits. Caspi et al., D4 (https://www.cdc.gov/pcd/issues/2021/20_0531.htm)

The strongest counterweight to a simple improvement narrative is the SuperShelf cluster-randomized study. Sixteen Minnesota pantries were randomized; COVID-19 interrupted complete observations at five sites. The baseline paired sample comprised 317 clients. Adjusted overall diet-quality and cardiovascular-health outcomes did not differ significantly between conditions. Existing choice pantries received an environmental intervention, so this is neither a choice-versus-no-choice experiment nor a test of DigniFeed. Implementation success did not establish those client benefits. Selected component changes do not overturn the overall finding. Caspi et al., D5 (https://uconnruddcenter.org/wp-content/uploads/sites/2909/2023/10/Caitlin-kaad060.pdf)

Different studies answer different questions
Qualitative evidence comparison. Populations, designs and outcomes differ; no pooled effect or software benefit is estimated.
Figure 3. Different studies answer different questions

Qualitative evidence comparison. Populations, designs and outcomes differ; no pooled effect or software benefit is estimated.

Arkansas interviews | D3: Quantity, quality and household relevance. Historical accounts from existing clients; not a causal intervention estimate.

Minnesota surveys | D4: Choice and service experience. Stated priorities inform questions; convenience samples do not prove queue effects.

SuperShelf trial | D5: Null overall health outcomes. Environmental intervention in existing choice pantries; not a DigniFeed or choice-versus-no-choice trial.

Sources: D3, D4, D5.
D3: https://uconnruddcenter.org/wp-content/uploads/sites/2909/2022/04/Food-Pantry-Clients-Qualitative-Study.pdf
D4: https://www.cdc.gov/pcd/issues/2021/20_0531.htm
D5: https://uconnruddcenter.org/wp-content/uploads/sites/2909/2023/10/Caitlin-kaad060.pdf

Figure 3. Qualitative comparison of study purposes. Preferences inform questions; the intervention supplies null overall health findings; neither establishes software efficacy. Study populations and designs differ, so no combined effect is calculated. Text and data alternative (#figure-text-and-data-alternatives).

EAG's inference is deliberately narrower than a health or food-security promise. An understandable choice and correction process is worth examining as a service outcome in its own right. It might also fail: staff could spend more time entering data, recipients could encounter new forms, or apparent accuracy could depend on work performed outside the system. Those possibilities need direct measurement.

The evidence set is also asymmetric. Existing users are better represented than people who cannot attend, decline participation or avoid service. A later evaluation that surveys only people who successfully complete the new process could miss the very exclusion it should detect. No retained source establishes which queue arrangement is best across all settings, nor that AI produces fairer allocations than ordinary rules and a trained operator.

6. Proposed mechanism: make a decision traceable and correctable

Proposed decision and correction paths
EAG conceptual mechanism, not an evaluated causal chain or approved implementation. People retain distribution and safety authority.
Figure 4. Proposed decision and correction paths

EAG conceptual mechanism, not an evaluated causal chain or approved implementation. People retain distribution and safety authority.

Stock basis. Record intake and unit → Reconcile physical count → Expose uncertain or held stock. An entry alone does not establish usable availability.

Human decision. Inspect source and constraints → Review options and exceptions → Confirm actual distribution. A suggestion or reservation is not fulfillment or authority.

Correction. Identify discrepancy → Record attributable correction → Resolve through named owner. Hypothesis: better reviewability; benefit unmeasured.

Sources: A0.
A0. EAG product definition: /projects/dignifeed

Figure 4. EAG conceptual mechanism. A suggestion does not authorize distribution, a reservation is not fulfillment, and an entry does not establish that stock is physically usable. Benefit remains untested. Text and data alternative (#figure-text-and-data-alternatives).

The following mechanism is an EAG hypothesis. It describes an operational relationship to test, not an approved implementation or proven causal chain.

A recorded intake would identify the site, item, unit, quantity, time, source and responsible role. The operator would distinguish checked stock from unresolved discrepancies and items held for an authorized decision. An allocation option would refer to that record and to the site's explicit constraints. A person would then confirm what actually happened. Correction would preserve an understandable account of the change and route unresolved issues to a named owner.

Consider a hypothetical mismatch between a received carton and its recorded unit. A count entered as individual packages could be mistaken for cartons, creating apparent availability that does not exist. The useful response would be to identify the uncertain unit, reconcile the physical count and make the correction attributable. An allocation model applied before that correction would make the wrong input more consequential. This example is illustrative, not a reported DigniFeed incident.

Likewise, a provisional reservation should not be counted as a completed distribution. A recipient may change a selection, a held item may remain unavailable, or service may be interrupted. The interface would need to say which event is known and which is merely intended. It should not display a successful distribution because someone pressed a button that failed to save.

AI would earn a role only if it helped with an identified task beyond an ordinary form or rule. Possible research candidates include summarizing unresolved discrepancies or presenting clearly labeled alternatives from approved inputs. The comparison should include a non-AI baseline. If suggestions conceal assumptions, generate unsupported stock facts or encourage staff to accept decisions without examining them, the proposed mechanism has failed even if it saves clicks.

7. Design concepts to investigate

These concepts are research proposals, not approved screens, runtime features or food-handling instructions.

A stock record with a visible basis. The operator should be able to tell when and by whom a quantity was checked, which unit it uses and whether it is held, reserved or available under the site's rules. An attractive total is insufficient if it conceals uncertainty. A missing count should remain missing until reconciled, rather than being inferred from a forecast.

Preference handling without a personal dossier. A person might indicate an item they do not want, a choice they prefer or a need for assistance without explaining a private hardship. Whether even that information needs retention is a local design question. The system should not infer a diagnosis or promise that a food meets a medical need. Any safety-relevant decision remains with the authorized people and applicable procedures.

An explanation before an allocation action. An option should state the constraint it is applying, the stock record it relies on and what remains uncertain. Where objectives conflict, the display could show the tradeoff plainly rather than attach a fairness badge. The operator needs a usable way to change or reject the suggestion and obtain help.

Correction that does not punish the recipient. The proposed process should allow a mistaken record to be challenged without requiring the recipient to construct a persuasive story. Staff should be able to distinguish a data error from a policy exception. A correction history is useful only if someone can read it, act on it and protect it from inappropriate disclosure.

More than one way to participate. A staff-assisted path and an understandable fallback are candidates for testing alongside any direct digital surface. Digital completion should not become an implicit test of deservingness. A later design exercise must examine language, keyboard and assistive-technology access, small screens, interruptions and the effect of shared devices. This report has not performed that usability work.

The first useful prototype would therefore emphasize a complete correction path over broad automation. With invented records only, an operator could receive an item, find a discrepancy, review its implications and record a corrected decision. Success in that exercise would support a usability claim about that scenario, not operational readiness or field benefit.

8. Risks, equity and burden

The primary risks are not limited to technical errors. An accurate system can still encode an inappropriate policy. A fast process can still discourage people who cannot use it. A detailed profile can expose more than the site needs to know. EAG's proposed safeguards below require local review and testing; none is a compliance certification.

Risk | How it could appear | Evidence needed before accepting the design
False availability | A reservation, stale count or inferred quantity appears as usable stock. | Reconciliation and interrupted-action exercises with clear uncertain states.
Hidden exclusion | People without a suitable device cannot obtain the same service. | Observation of staff-assisted and non-digital paths, including unsuccessful attempts.
Burden displaced to volunteers | A shorter recipient interaction creates longer setup and cleanup. | Total work across roles and shifts, including corrections and duplicate records.
Unreviewable allocation | Staff accept an unexplained suggestion as policy. | Demonstrated understanding, rejection and repair of deliberately wrong suggestions.
Unnecessary profiling | Preferences become persistent sensitive attributes or eligibility signals. | A documented need for each field, role-specific access and retention decisions.
Misleading outcome claims | Pounds, transactions or completed forms are described as dignity or health. | Distinct outcome definitions and claims restricted to what was measured.

Queue measures deserve particular care. A shorter wait after arrival could conceal earlier online booking effort, travel at a less suitable time or people leaving before check-in. The report proposes observing the whole service path and recording abandonment where ethical and feasible. It does not establish an appointment-only or first-come approach as the equitable default.

Food-safety and eligibility rules depend on the real program and responsible authorities. A historical regulation was located during scouting but was not used to state current law. This report gives no household eligibility advice and authorizes no handling practice. Before an operational specification, the actual site must establish the rules it is permitted and required to apply. The product should display those rules accurately without treating the software as their source.

9. Evaluation and conditions for changing direction

The first evaluation question should be narrow: can authorized operators maintain a more understandable and correctable stock-to-distribution record without increasing access barriers or total work? This is different from asking whether DigniFeed reduces food insecurity. The latter would require a separate design, a credible comparison and much broader evidence.

Begin with a description of the existing process, collected only with appropriate authorization. Observe where uncertainty arises and how it is currently resolved. Select a comparator that represents a serious alternative: the improved existing workflow or an appropriately configured established tool. Comparing new software with a deliberately neglected baseline would not answer the procurement question.

A synthetic exercise can first test comprehension and recovery without real recipient information. Use mismatched units, duplicate entries, an interrupted action and a disputed selection. Observe whether operators can identify the uncertain record, preserve a human decision and correct it. Failed exercises should change the design before any live use is considered.

Proposed outcome | Measurement question | Interpretation limit
Record accuracy | Do recorded quantities and states match authorized physical checks? | A clean record does not establish safe handling or adequate supply.
Correction quality | Can the responsible person find and resolve an error with its reason preserved? | More reported errors might reflect better detection.
Recipient experience | Can people understand choices, obtain help and decline unnecessary disclosure? | Responses from successful users alone can conceal exclusion.
Total operational effort | What time is spent before, during and after service across all roles? | A faster screen may merely move work elsewhere.
Access | Can intended users complete the service through available paths? | Aggregate success can conceal language or assistance barriers.
Decision comprehension | Can operators explain and reject an inappropriate option? | A recorded override button is not evidence of meaningful oversight.

Before a live pilot, define the observation window, denominators, missing-data handling and locally meaningful change criteria with the participating site. Do not invent a percentage target merely because a dashboard needs one. Where feasible, compare similar service periods while accounting for differences in arrivals, donations and staffing; simple before-and-after change cannot by itself establish causation.

Change direction if the tool repeatedly misrepresents availability, creates pressure to disclose, prevents service through a reasonable fallback or leaves operators unable to explain decisions. Pause allocation assistance if the underlying stock record remains unreliable. Remove AI if a simpler process performs as well with less burden. Prefer an established alternative if its actual configuration meets the need more effectively. A technically functioning prototype is not a reason to continue an unsuccessful service experiment.

10. Methods, limitations, references and corrections

This is a bounded narrative review, not a systematic review or meta-analysis. Research and retrieval took place on September 19, 2026. Source discovery targeted USDA household data, original pantry preference and intervention studies, and first-party operational alternatives. Retained empirical sources were checked at their original institutional or author-institution locations. Relevant methods, results and limitations were inspected; this does not imply that every cited reference or supplement was reviewed.

The largest gaps are the absence of DigniFeed field evidence, contemporary site-specific Arkansas operational observations, direct evidence from nonusers, a comparative queue study suited to the intended setting and a total-cost comparison. Vendor documents are living pages, dated here by retrieval. The interview and survey observations are historical. Their publication dates must not be read as present-day measurements.

An allocation-model preprint was excluded from the substantive synthesis because version and author metadata needed reconciliation and its agency-network problem differed from household service. A practitioner checklist had inconsistent retrieval and was not made necessary to a conclusion. Inaccessible studies were not treated as read evidence. Current legal, eligibility and safety requirements remain outside the report's verified findings.

No correction notice was visible in the retained materials. A bounded scouting search found no notice for the two Caspi studies; that is not a comprehensive correction or retraction clearance. A later correction to a source, changed vendor capability or new local evidence should update the associated claim, figure and interpretation together. The report should be reconsidered if a rigorous comparable software evaluation appears, if participant research contradicts its assumptions or if the intended operating setting changes.

References

- A0. Education Angel Group, DigniFeed product definition, reviewed September 19, 2026 for this report. Defines intended scope, not implementation or effectiveness.
- D1. USDA Economic Research Service. Food Security in the U.S.: Key Statistics & Graphics (https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us/key-statistics-graphics). Updated March 30, 2026; retained national estimates concern 2024.
- D2. Rabbitt, M. P., Reed-Jones, M., Hales, L. J., Suttles, S., and Burke, M. P. Household Food Security in the United States in 2024, ERR-358 (https://ers.usda.gov/sites/default/files/_laserfiche/publications/113623/ERR-358.pdf). December 2025. Table 4 and accompanying methods, printed pp.28-29.
- D3. Long, C. R., and colleagues. Food Pantry Clients' Needs, Preferences, and Recommendations for Food Pantries: A Qualitative Study (https://uconnruddcenter.org/wp-content/uploads/sites/2909/2022/04/Food-Pantry-Clients-Qualitative-Study.pdf). Online April 6, 2022. DOI: 10.1080/19320248.2022.2058334. Methods, themes and limitations.
- D4. Caspi, C. E., and colleagues. Needs and Preferences Among Food Pantry Clients (https://www.cdc.gov/pcd/issues/2021/20_0531.htm). Preventing Chronic Disease, April 1, 2021. DOI: 10.5888/pcd18.200531. Methods, Results, Figure 2 and limitations.
- D5. Caspi, C. E., and colleagues. A Cluster-Randomized Evaluation of the SuperShelf Intervention in Choice-Based Food Pantries (https://uconnruddcenter.org/wp-content/uploads/sites/2909/2023/10/Caitlin-kaad060.pdf). 2023 online version. DOI: 10.1093/abm/kaad060. Methods, Table 2 and Discussion.
- D8. PantrySoft. Client Portal QuickStart: Registration, Scheduling, and Inventory (https://support.pantrysoft.com/client-portal-quickstart-registration-scheduling-and-inventory). Undated living documentation, retrieved September 19, 2026. Definition, Scheduling and Shopping.
- D9. PantrySoft. Scheduling Client Appointments: Open Scheduling (https://support.pantrysoft.com/scheduling-client-appointments-open-scheduling). Undated living documentation, retrieved September 19, 2026. Definition and appointments-versus-visits explanation.

Figure text and data alternatives

Complete semantic alternatives accompany each figure in this reader and its text download. Source IDs refer to the references in this report. A0 identifies EAG's product definition reviewed for the original report, not an external study or proof of availability. Other source IDs resolve to this report's reference and access record.

Corrections and entity context

Readers identifying an error can contact EAG (/contact) with the section, disputed statement and supporting source. DigniFeed (/projects/dignifeed) provides the public entity context, not an external citation for EAG's product claims.