Bonterra Que: The First Agentic AI Platform Built for the Social Good Sector
Bonterra Que represents a significant shift in how nonprofits can leverage artificial intelligence. Rather than simply offering suggestions or generating text on demand, Que acts autonomously on behalf of nonprofit teams, handling complex tasks like donor segmentation, grant matching, and campaign creation while keeping humans firmly in control. Here is what nonprofit leaders need to know about this platform, what it can do today, and whether it is the right fit for your organization.
When Bonterra launched Que (pronounced "Q") on October 1, 2025, it marked the arrival of the first fully agentic AI platform designed specifically for the social good sector. Unlike the general-purpose AI tools that nonprofits have been experimenting with over the past few years, Que is embedded directly into the software that thousands of organizations already use every day, including Network for Good, EveryAction, and CyberGrants. This integration means that Que does not require nonprofits to learn a new tool or build custom workflows. It works within the systems teams already know.
The timing is significant. According to Bonterra's own Funder AI Survey, 91% of funders see AI as a force that will transform philanthropy, yet 92% express concern about data use and ethics. That tension between excitement and caution defines where the sector stands right now. Nonprofit leaders want the efficiency and insight that AI promises, but they need platforms that respect the unique ethical obligations of mission-driven work. Bonterra is making a direct bet that its decades of sector experience and the depth of its data network give it a competitive advantage that general-purpose AI companies cannot match.
For organizations that are still in the early stages of their AI journey, it is worth reading our nonprofit leaders guide to AI to build foundational understanding before evaluating a platform like Que. And for those already developing internal AI capabilities, this article will help you understand how Que fits into the broader landscape of nonprofit technology and where it might complement or replace your current tools.
In this article, we will break down what "agentic AI" actually means in practice, walk through Que's current capabilities, examine the data advantage Bonterra brings to the table, explore the ethical framework underpinning the platform, and compare it to alternatives in the market. Whether you are considering Bonterra Que for your organization or simply trying to understand the direction nonprofit technology is heading, this guide will give you the context you need to make informed decisions.
What Makes Que "Agentic" and Why It Matters
The term "agentic AI" has become one of the most discussed concepts in technology over the past year, but it is often used loosely. To understand what Bonterra Que actually does differently, it helps to distinguish between three levels of AI capability that nonprofits are encountering today.
The first level is the basic chatbot or question-and-answer interface. Tools like ChatGPT, Google Gemini, and Microsoft Copilot fall into this category when used in their simplest form. You ask a question, you get an answer. You provide a prompt, you get a draft. The AI responds to each request independently and does not take action on your behalf. It is reactive, not proactive.
The second level is the copilot model, where AI assists you within a specific workflow. Microsoft Copilot embedded in Word or Excel is a good example. The AI understands the context of what you are working on and can make suggestions, generate content, or analyze data within that specific application. It is more helpful than a standalone chatbot, but it still waits for your instructions before doing anything.
The third level is agentic AI, and this is where Que operates. An agentic AI system can take autonomous action toward a goal. Instead of waiting for you to ask it to draft a donor thank-you email, Que can identify which donors need follow-up, draft personalized messages based on their giving history and engagement patterns, and queue those messages for your review. Instead of requiring you to manually build a donor segment, Que can analyze your data and create segments based on patterns it identifies, recommending which groups to target for a specific campaign. For a deeper exploration of how AI agents are changing nonprofit operations, see our article on AI agents for nonprofits.
The key distinction is that agentic AI acts, not just suggests. It can chain multiple steps together to accomplish a complex task. However, and this is critical for the nonprofit context, Que is designed to operate under human oversight. It does not send emails or finalize campaigns without approval. It does the heavy lifting of analysis, drafting, and preparation, then presents its work for a human to review, modify, and approve. This "human-in-the-loop" approach is essential for organizations where every communication carries the weight of their mission and donor trust.
Core Capabilities: What Que Can Do Today
Bonterra Que launched with a focused set of skills designed to address the most time-consuming and high-impact areas of nonprofit operations. Each skill is embedded directly into the relevant Bonterra product, so teams interact with Que in the context of their existing workflows rather than switching to a separate AI tool. Here is a detailed look at each capability area.
Fundraising Coaching
Personalized guidance to improve fundraising performance
Que's fundraising coaching skill analyzes your organization's giving data, campaign performance, and donor behavior to provide actionable recommendations. Rather than generic best practices, these recommendations are grounded in your specific data and benchmarked against patterns from the broader Bonterra Network. This means the advice you receive is contextual, not theoretical. If your year-end campaign is underperforming compared to similar organizations, Que will identify specific areas where adjustments could improve results.
- Analyzes campaign performance against sector benchmarks from 180,000 nonprofits
- Identifies underperforming areas and recommends specific adjustments
- Provides timing recommendations based on historical donor engagement patterns
- Suggests ask amounts calibrated to each donor's capacity and history
Donor Stewardship
Automated, personalized donor communications and relationship management
Donor stewardship is one of the areas where nonprofits struggle most with capacity. Organizations know that timely, personalized follow-up increases retention, but when development teams are stretched thin, stewardship often falls to the bottom of the priority list. Que addresses this by proactively identifying donors who need attention, drafting personalized communications based on their giving history and engagement, and queuing those communications for staff review. This is particularly valuable for donor analysis and engagement, where understanding individual supporter patterns can significantly improve retention rates.
- Drafts personalized thank-you messages, impact updates, and re-engagement emails
- Identifies at-risk donors based on declining engagement signals
- Recommends optimal timing and channel for each outreach
- Maintains communication history to ensure consistency across touchpoints
Smart Segmentation
AI-driven donor segmentation that goes beyond basic filters
Traditional donor segmentation relies on manual filters: giving level, last gift date, location, and similar demographic criteria. Que's smart segmentation takes a fundamentally different approach. It analyzes behavioral patterns, engagement signals, and giving trajectories to create segments that reflect how donors actually interact with your organization. This means you can target donors who are likely to increase their giving, donors who show signs of lapsing, or donors who have the capacity for major gifts but have not yet been cultivated.
- Creates behavioral segments based on engagement patterns, not just demographics
- Identifies upgrade candidates and major gift prospects automatically
- Recommends segment-specific messaging strategies and ask amounts
- Continuously refines segments as new data comes in
Campaign Creation
End-to-end campaign building with AI-generated content and strategy
Building a fundraising campaign from scratch involves dozens of decisions: audience selection, messaging, channel strategy, timing, form design, and follow-up sequences. Que can handle much of this groundwork autonomously. Given a campaign goal, Que will recommend target segments, draft email content, build donation forms, and suggest a communication timeline. All of this is presented for review and approval before anything goes live, but the hours of setup work are dramatically reduced.
- Generates complete campaign plans including audience, messaging, and timeline
- Drafts email sequences, social content, and donation page copy
- Builds and configures donation forms with optimized layouts
- Recommends A/B testing strategies based on sector performance data
Grant Matching and Scoring
Intelligent grant discovery and application support for both grantseekers and grantmakers
For grantseeking organizations, Que scans available funding opportunities and matches them to your organization's mission, programs, and eligibility criteria. Rather than spending hours searching databases and reading guidelines, development teams receive a curated list of relevant grants with compatibility scores and key deadline information. On the grantmaker side, Que can score incoming applications against defined criteria, helping program officers prioritize their review queue and identify the strongest candidates more efficiently.
- Matches organizations to relevant funding opportunities based on mission alignment
- Scores grant applications against defined criteria for grantmakers
- Tracks deadlines and provides timeline management for grant cycles
- Leverages data from 25% of all corporate giving processed through CyberGrants
The Data Advantage: Why Scale Matters for Nonprofit AI
Every AI system is only as good as the data it is trained on, and this is where Bonterra makes its strongest argument. The Bonterra Network encompasses data from 180,000 nonprofits, 20 million individual supporters, 25% of all corporate giving in the United States, and approximately $28 billion in annual giving flowing through its platforms. This is not generic internet data or publicly available information. It is real transactional, behavioral, and engagement data from the social good sector.
This data advantage shows up in practical ways. When Que recommends an ask amount for a donor, that recommendation is informed by patterns across millions of similar giving transactions. When it identifies a donor as an upgrade candidate, it is drawing on behavioral signals that have been validated across thousands of organizations. When it suggests the optimal time to send a fundraising email, it is working with engagement data at a scale that no single organization could generate on its own.
CEO Scott Brighton has emphasized this point directly, stating that "agentic AI may grab attention today, but the true differentiator over time will be the credibility of our models, the depth of our data, and the trust we've earned through decades of partnership with the sector." This is a strategic bet that sector-specific data will matter more than general AI capability. A large language model from OpenAI or Google may be more capable in general terms, but it does not have access to the transactional patterns of $28 billion in nonprofit giving.
For nonprofit leaders evaluating AI tools, the data question is critical. General-purpose AI tools can generate text, answer questions, and assist with common tasks. But when it comes to sector-specific decisions like donor segmentation, ask amount calibration, and grant matching, the quality of the underlying data directly determines the quality of the output. Organizations that want to develop a thoughtful approach to AI adoption should consider building a strategic plan for AI that accounts for data quality and platform capabilities.
It is worth noting that Bonterra claims organizations using Que are seeing 20-40% lifts in fundraising results. While these numbers are compelling, they should be evaluated with appropriate caution. Early adopters tend to be more sophisticated organizations that may see outsized results. The real test will be whether these gains hold across a broader range of organizations as adoption grows. Still, even modest improvements in fundraising efficiency represent significant value for resource-constrained nonprofits.
2026 Roadmap: Where Que Is Heading
Bonterra has been transparent about the fact that Que's current capabilities represent a starting point, not a finished product. The 2026 roadmap includes several significant expansions that will broaden Que's utility beyond fundraising and grantmaking into operational areas that affect every nonprofit.
Case Management
AI-assisted case management will help human services organizations manage client interactions, track outcomes, and identify individuals who need additional support. This could be particularly impactful for organizations serving large populations where individual attention is limited by staff capacity. Que will be able to flag cases that need intervention, suggest next steps based on similar client outcomes, and help caseworkers prioritize their workload.
Impact Management
Impact measurement is one of the most challenging areas for nonprofits. Que's planned impact management capabilities will help organizations track, analyze, and report on their outcomes more effectively. This includes automated data collection, trend analysis, and the ability to generate impact reports that connect program activities to measurable outcomes. For organizations that struggle with proving their effectiveness to funders, this could be transformative.
Workflow Automation
Beyond specific skill areas, Bonterra plans to expand Que's ability to automate multi-step workflows that span different parts of the platform. Imagine a system that automatically triggers a stewardship sequence when a major gift is received, updates the donor's profile, notifies the development director, and schedules a personal thank-you call. This kind of cross-functional automation is where agentic AI delivers its greatest value.
These roadmap items are worth watching, but nonprofit leaders should make purchasing decisions based on current capabilities, not future promises. Technology roadmaps shift, and features that are planned for 2026 may arrive later than expected or look different from what was initially described. The current fundraising and grantmaking capabilities are real and available today, and that is where the evaluation should focus.
Ethical AI Framework: Three Pillars of Responsible Use
Given that 92% of funders express concern about data use and ethics in AI, Bonterra has built its approach around three ethical pillars. For nonprofits that handle sensitive donor, client, and beneficiary data, these commitments matter as much as the technical capabilities. Organizations that are building AI champions within their teams should evaluate any AI platform against its ethical framework.
Human-Led
AI assists, humans decide
Que is designed to operate under human oversight at every stage. While it can autonomously analyze data, draft communications, and build campaigns, it does not take final action without human approval. This means that a donor email drafted by Que does not get sent until a staff member reviews and approves it. A campaign built by Que does not go live until someone confirms the strategy. This approach respects the fact that nonprofit communications carry unique weight and responsibility. A poorly targeted fundraising appeal does not just miss a revenue target; it can damage relationships that took years to build.
- All AI-generated outputs require human review and approval before execution
- Staff maintain full control over final decisions and communications
- Clear approval workflows prevent unintended actions
Transparent by Design
Understanding how and why AI makes recommendations
One of the biggest challenges with AI systems is the "black box" problem, where users cannot understand why the AI made a particular recommendation. Bonterra has committed to making Que's reasoning transparent. When Que recommends a donor segment or suggests an ask amount, it provides the rationale behind that recommendation. This transparency is important not just for user confidence, but for organizational accountability. When a board member asks why a particular fundraising strategy was chosen, staff should be able to explain the reasoning, even when AI was involved in developing it.
- Provides explanations for recommendations and suggestions
- Shows the data points and patterns that informed each decision
- Enables staff to explain AI-assisted decisions to stakeholders and boards
Built for Trust
Data security and privacy as foundational principles
Nonprofits handle sensitive data, including donor financial information, client records, and beneficiary data. Bonterra has committed to ensuring that Que's AI models do not use individual organization data to train models that benefit other organizations. Your donor data stays yours. The aggregated insights from the Bonterra Network are anonymized and used in aggregate to improve the platform's models, but individual records are not shared or exposed. This is a critical distinction that nonprofit leaders should verify with any AI vendor they are considering.
- Individual organization data is not used to train models for other organizations
- Network-level insights are anonymized and aggregated
- Enterprise-grade security standards for data storage and processing
How Que Compares to Other Nonprofit Platforms
Bonterra Que does not exist in a vacuum. Nonprofit leaders evaluating AI-powered platforms have several options, each with different strengths and trade-offs. Understanding where Que fits in the competitive landscape helps clarify whether it is the right choice for your organization.
Bonterra Que vs. Salesforce Nonprofit Cloud
Salesforce Nonprofit Cloud with Einstein AI is the most direct competitor to Bonterra Que in terms of scale and capability. Salesforce offers more customization and flexibility, allowing organizations to build highly tailored CRM configurations, automate complex workflows, and integrate with a vast ecosystem of third-party applications. Einstein AI provides predictive analytics, lead scoring, and generative AI features within the Salesforce ecosystem.
However, Salesforce's power comes with significant technical overhead. Organizations typically need dedicated administrators, and often external consultants, to implement and maintain their Salesforce instance. The learning curve is steep, and the total cost of ownership can be substantially higher than the licensing fees suggest. Que's advantage here is simplicity. Because it is embedded into products that are already configured for nonprofit use, the barrier to adoption is much lower. Organizations that do not have technical staff or Salesforce expertise may find Que more practical, even if Salesforce is more powerful in theory.
Bonterra Que vs. Bloomerang
Bloomerang has built a strong reputation for simplicity and donor retention focus. Its interface is clean, its reporting is intuitive, and it does an excellent job of helping small to mid-size nonprofits track donor relationships. Bloomerang has introduced some AI-assisted features, including generative AI for drafting communications and predictive analytics for donor retention.
Where Bloomerang falls short compared to Que is in the depth of AI capability. Bloomerang's AI features are assistive rather than agentic. They help users complete tasks more quickly, but they do not autonomously take multi-step actions the way Que does. For small organizations that value simplicity above all else, Bloomerang may still be the better choice. But for organizations that want AI to take on a more substantial role in their operations, Que offers significantly more capability.
Bonterra Que vs. Givebutter
Givebutter has gained popularity for its modern interface, free fundraising tools, and social fundraising capabilities. It is particularly popular with smaller organizations and grassroots campaigns. However, Givebutter's AI capabilities are limited compared to Que. While Givebutter offers some AI-assisted content generation, it does not provide the agentic capabilities, sector-specific data models, or enterprise-scale features that Que delivers. Givebutter is a strong choice for organizations focused on peer-to-peer fundraising and events, but it is not positioned to compete with Que on AI sophistication.
Bonterra Que vs. General-Purpose AI Tools
Some nonprofits are building their AI capabilities using general-purpose tools like ChatGPT, Google Gemini, or Microsoft Copilot. These tools are powerful, flexible, and increasingly affordable. They can assist with content creation, research, data analysis, and many other tasks. The limitation is that they are not connected to your fundraising data, donor records, or grant management systems. Using ChatGPT to draft a donor email requires you to provide the context manually. Using Que, the context is already there because it is embedded in your CRM. For organizations exploring this approach, our guide on getting started with AI as a nonprofit provides a practical framework for evaluating your options.
Pricing and Access: What to Expect
As of March 2026, Bonterra has not published public pricing for Que. The platform is available across three tiers, Essentials, Pro, and Enterprise, with pricing customized based on organization size, the specific Bonterra products you use, and the Que capabilities you need. This is common in enterprise software but can be frustrating for nonprofit leaders who want to understand costs upfront before engaging with a sales process.
What we know is that Que is embedded into existing Bonterra products, which means you likely need to be a Bonterra customer (or become one) to access it. If your organization already uses Network for Good, EveryAction, or CyberGrants, Que may be available as an add-on or included at certain tiers. If you are not currently a Bonterra customer, adopting Que means committing to the Bonterra ecosystem.
When evaluating pricing, nonprofit leaders should consider the total cost of ownership. This includes not just the subscription fee, but the staff time required for implementation, training, and ongoing management. Que's embedded approach should reduce implementation costs compared to platforms that require significant configuration, but the transition from an existing CRM to Bonterra's ecosystem is still a substantial undertaking. Organizations should request detailed implementation timelines, data migration support details, and references from organizations of similar size and complexity.
We recommend that organizations interested in Que request a demo that focuses specifically on the AI capabilities rather than the broader CRM features. Ask to see Que perform tasks relevant to your specific use case, whether that is donor segmentation, grant matching, or campaign creation. Pay attention to the quality of the AI output and whether the recommendations feel genuinely useful or generic.
What This Means for the Nonprofit Sector
Bonterra Que's launch signals a broader shift in how technology companies are approaching the nonprofit market. For years, the social good sector has been an afterthought for enterprise software companies, receiving watered-down versions of commercial tools or being asked to adapt business-focused platforms to mission-driven work. The arrival of a purpose-built agentic AI platform designed exclusively for nonprofits suggests that the market is maturing and that technology providers see enough demand and willingness to invest in sector-specific solutions.
This matters because AI adoption in the nonprofit sector has been uneven. Larger organizations with technology budgets and dedicated staff have been experimenting with AI for the past two years. Smaller organizations have largely been watching from the sidelines, uncertain about how to start, what tools to use, and whether they can afford it. Platforms like Que that embed AI into existing workflows lower the barrier to entry. A development associate at a mid-size nonprofit does not need to understand prompt engineering or AI architecture. They need a tool that helps them identify which donors to contact and what to say, and Que is designed to deliver exactly that.
However, the concentration of AI capability in a small number of platforms raises important questions about vendor dependency. If your organization becomes reliant on Que for donor segmentation, campaign strategy, and grant matching, you are deeply tied to Bonterra's ecosystem. Switching costs increase, and your AI strategy becomes inseparable from your vendor relationship. This is not necessarily a reason to avoid Que, but it is a factor that should be part of any strategic AI planning process.
The competitive response from other platforms will also be worth watching. Salesforce, Bloomerang, and other nonprofit technology providers are all investing in AI capabilities. The next 12 to 18 months will likely see rapid development across the sector, with each vendor racing to match or exceed Que's agentic capabilities. For nonprofit leaders, this means the landscape is changing quickly, and decisions made today should account for how rapidly the market is evolving. Building internal AI champions who can evaluate and adapt to these changes will be critical regardless of which platform you choose.
Conclusion
Bonterra Que represents the most ambitious attempt to date to bring agentic AI to the social good sector. Its integration into existing Bonterra products, its access to data from 180,000 nonprofits and $28 billion in annual giving, and its ethical framework designed for the unique requirements of mission-driven organizations set it apart from both general-purpose AI tools and competing nonprofit platforms. The current capabilities in fundraising coaching, donor stewardship, smart segmentation, campaign creation, and grant matching address real pain points that nonprofit teams face every day.
That said, nonprofit leaders should approach Que with both enthusiasm and pragmatism. The platform is still early in its development, with significant capabilities on the 2026 roadmap that have not yet been delivered. Public pricing is unavailable, making it difficult to assess value without engaging directly with Bonterra's sales team. And the 20-40% fundraising lift claims, while encouraging, need to be validated across a broader range of organizations.
For organizations already in the Bonterra ecosystem, exploring Que is a natural next step. For organizations considering a platform switch, Que adds a compelling AI dimension to the evaluation. And for all nonprofit leaders, Que's launch is a signal that agentic AI is arriving in the social good sector, and the organizations that prepare for this shift now will be best positioned to benefit from it. Start by developing your team's AI literacy, build a strategic plan for AI adoption, and evaluate platforms like Que based on your specific needs, not just the excitement of the technology.
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