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DevOps for GenAI Toronto Edition Hackathon: Unlocking New AI Market Opportunities
Toronto’s vibrant tech community is abuzz as the DevOps for GenAI Hackathon returns with John Willis, this time with ambitious new themes that reflect both global trends and uniquely Canadian innovation. Building on the foundational insights from the first Hackathon held in Ottawa organizers are expanding the event to spotlight end-to-end lifecycle automation for generative AI, advanced observability, agent scalability, and the critical dimensions of AI security and governance.

This progression to “DevOps for GenAI Hackathon Days” signifies a bold step forward in purpose and scope. The hackathon not only honors the collaborative foundation of DevOps but accelerates it toward a future of trustworthy, ethical, and scalable artificial intelligence in the software lifecycle, uniting communities globally around common challenges and innovation goals.

Expanding the Hackathon Theme

The Toronto edition introduces targeted tracks that tackle the most pressing issues for GenAI in production:
- MLOps Pipelines: Teams will design fully automated pipelines for LLMs and diffusion models, focusing on robust data management, fine-tuning, CI/CD, and drift detection using battle-tested tools (MLflow, Kubeflow, Argo Workflows).
- AI Observability & Monitoring: The hackathon emphasizes building centralized platforms for measuring latency, token usage, and output quality, as well as integrating safety detectors for hallucinations and toxic content.
- OpenTelemetry, Prometheus, and Grafana play leading roles, building on winning projects like InnerAI and InsightAI_Minions from the Ottawa edition.
- Agent Deployment at Scale: Participants will architect systems to containerize, orchestrate, and scale multiple AI agents in Kubernetes clusters—applying DevOps best practices for reliability, scalability, and observability.
Innovation Meets Real-World DevOps

What sets the Toronto hackathon apart is its strategic alignment with enterprise challenges. Unlike many theoretical or demo-driven AI events, the Toronto edition emphasizes hands-on solutions to core business obstacles faced when deploying GenAI systems at scale.

By structuring challenges and judging through an enterprise lens, Toronto’s hackathon delivers practical, production-ready GenAI approaches poised for immediate adoption as organizations behind them scale AI across their operations.

Security and governance are central to the Toronto DevOps for GenAI hackathon, recognizing today’s enterprise-scale risks around generative AI adoption. The event calls upon security firms and open-source security researchers to rigorously review, test, and contribute to the official repository at https://github.com/CanadaDevOpsCommunity2025

Security companies, open-source defenders, and researchers are encouraged to:
- Fork, audit, and contribute to the repository,
- Submit issues or pull requests for any discovered vulnerabilities,
- Help harden IaC, agentic workflows, and GenAI application layers by integrating advanced governance tools, such as policy-as-code engines and explainable AI dashboards.
This approach not only facilitates continuous improvement and defense-in-depth for community-driven projects but also sets an example for responsible and transparent AI innovation helping protect users, enterprises, and the broader tech ecosystem.

Accelerating Community and Collaboration

Toronto’s edition continues the hackathon’s tradition of uniting developers, data scientists, and platform engineers, but extends collaboration across industry, academia, and open-source communities. Global tech leaders such as John Willis (co-author of The DevOps Handbook) advocate for solution designs that not only prototype novel ideas but actively bridge theory with scalable practice, helping participants prepare for the “complexities of modern professional environments”.

Addressing Strategic Gaps

While past hackathons delivered creative dashboards, automated benchmarking, and middleware solutions, Toronto organizers have identified strategic gaps such as:
- Incomplete CI/CD integration and lack of deep automation.
- Insufficient multi-cloud scalability.
- Unaddressed data privacy and compliance requirements.
Teams are now challenged to harden their prototypes, embrace production-ready architectures, and pioneer standardized approaches to observability and evaluation, setting the stage for broader enterprise adoption.

The Path Forward

In summary, the Toronto edition positions itself as a launchpad for next-generation GenAI and DevOps convergence, pledging to mature open-source solutions into production-grade platforms. By blending technical rigor with collaborative energy, this hackathon not only energizes the local tech community but promises to shape the future direction of AI development in enterprise and beyond.

Hackathon participants are encouraged to build, test, and iterate on ideas with the knowledge that their innovations today will be the foundations of tomorrow’s trustworthy, scalable, and ethical GenAI platforms.

The Toronto DevOps for GenAI Hackathon is setting the pace for AI innovation, and its path forward will see its unique blend of DevOps and generative AI scaling across new geographies and themes in 2026–27. Plans are already in motion to expand to key innovation hubs in North America, EMEA, and APAC, each bringing new production-grade challenges, vertical themes like AI security, sustainability, and agentic orchestration, and expanded community participation.

By keeping sponsorship, contribution, and mentorship onboarding adaptable and inclusive, the GenAI hackathon ecosystem will foster sustainable growth, deeper industry ties, and richer participant experiences as it scales geographically and thematically.

Join us at the Hackathon

https://www.eventbrite.com/e/devops-for-genai-hackathon-tickets1407877793379?aff=oddtdtcreator
November 17, 2025
Accelerating GenAI Innovation with DevOps Hackathons—Hackathon Insights, Strategic Gaps and Future Directions
Importance of Hackathon

Hackathons have become a vital platform for driving innovation, collaboration, and skill development across diverse industries. These intense, time-bound events bring together programmers, designers, and problem solvers to rapidly prototype new ideas and tackle real-world challenges. Beyond fostering creativity, hackathons enable participants to expand their technical skills, network with like-minded professionals, and gain industry recognition.

The “DevOps for GenAI” hackathon, recently held in Ottawa, showcased 15 competitive projects at the intersection of DevOps and Generative AI. The event highlighted the growing convergence of AI observability and prompt engineering, with projects split between these two domains. This white paper outlines the intent, technical gaps, strengths, future perspectives, and collaboration requirements of the presented projects, drawing from the available repositories and event summaries.
- AI Observability: Roughly half the projects focused on monitoring, debugging, and optimizing AI systems. These solutions aimed to provide real-time insights into model performance, resource utilization, and anomaly detection, reflecting the industry’s need for robust AI system management.
- Prompt Evaluation: The remaining teams targeted prompt engineering and evaluation tools for GenAI systems, aiming to improve prompt quality, automate testing, and provide feedback loops for continuous improvement.
The rapid adoption of large language models (LLMs) and generative AI (GenAI) systems in enterprise and research settings has elevated the need for advanced observability, monitoring, and accessibility solutions. This white paper explores the combined strengths and future potential of open-source projects produced during the DevOps for GenAI Hackathon, Ottawa edition. It particularly explores three projects in the AI Observability space —InsightAI_Minions, AI Observability Monitoring InnerAI, and GenA11yHelper—to deliver a unified, extensible, and accessible framework for GenAI deployment and operation.

AI Observability & Monitoring (Inner AI)

Presented by James Drayton Beninger, Amogh Thoutu, Farhan Mahamud, Shubhangi Singh, Bardia Azami, Inner AI Group, Canada DevOps Community 2025

AI Observability & Monitoring (Inner AI) middleware solution, designed to enhance transparency, reliability, and accountability in text-based generative AI (GenAI) applications. The solution integrates seamlessly into developer workflows, providing real-time monitoring, metrics, and testing capabilities for large language model (LLM) interactions. We discuss the system’s core strengths, outline future directions for research and development, and identify opportunities for collaboration within the broader AI and DevOps communities. The goal is to foster robust, trustworthy AI deployments across industries by advancing observability standards and practices. The Inner AI solution comprises four main modules:

• API Wrapper: Intercepts and augments LLM prompt/response cycles.
• Metrics: Collects detailed telemetry on LLM performance and behavior.
• Dashboard: Visualizes real-time and historical metrics for developers.
• Testing Module: Simulates end-user interactions to validate system robustness.

Key Strengths

• Seamless Integration: Designed as middleware, the system requires minimal changes to existing application code, supporting rapid adoption.
• Comprehensive Monitoring: Captures both quantitative (latency, error rates) and qualitative (response quality, prompt drift) metrics.
• Automated Testing: Simulates diverse user scenarios, enabling proactive detection of issues such as hallucinations or degraded model performance.
• Extensibility: Built using widely adopted frameworks (e.g., FastAPI, Prometheus, LangChain), facilitating customization and scalability.
• Developer-Centric Design: Provides actionable insights through an intuitive dashboard, accelerating debugging and model iteration cycles.

InsightAI_Minions: Enabling Observability and KPI Monitoring for LLM Deployment

By Denis Shleifman, Morteza Mirzaei, Dorian Gerdes, Manav Isrrani, Sachin Kumar, Canada DevOps Community 2025

InsightAI_Minions, an open-source framework for deploying, monitoring, and benchmarking large language models (LLMs) using DevOps best practices. The system provides automated KPI measurement, advanced alerting, and extensible observability for LLM deployments. We discuss the strengths of the platform, future development directions, and the need for collaborative efforts across the AI and DevOps communities to advance the state of LLM observability and reliability.

The rapid adoption of LLMs in production environments requires robust tools for deployment, monitoring, and performance evaluation. InsightAI_Minions addresses this gap by providing a comprehensive solution for automated LLM deployment and observability, leveraging modern DevOps pipelines and open-source monitoring stacks.

Strengths of InsightAI_Minions

• Automated Deployment: Utilizes GitHub Actions and Docker Compose for seamless LLM deployment, supporting continuous integration and delivery workflows.
• Comprehensive KPI Measurement: Simulates diverse test prompts (from a set of 50,000) to measure latency, token throughput, and success rates, providing actionable insights into LLM performance.
• Advanced Observability: Integrates OpenTelemetry (OTEL) for metrics collection and VictoriaMetrics for time-series storage, with visualization via Grafana dashboards.
• Proactive Alerting: Implements advanced alerting (warning and critical) with configurable Slack notifications, enabling rapid incident response.
• User-Defined KPIs: Allows users to define custom metrics and dashboards, enhancing flexibility for varied deployment scenarios.

InsightAI_Minions represents a significant step forward in operationalizing LLM deployments with robust observability, benchmarking, and alerting capabilities. Continued collaboration across industry, academia, and standards organizations is essential to address evolving challenges and ensure reliable, scalable, and transparent AI systems.

Future Perspectives

• Expanded Integration: Support for additional deployment platforms (e.g., Kubernetes, cloud-native environments) and CI/CD tools beyond GitHub Actions.
• Enhanced Metric Coverage: Inclusion of more granular performance and reliability metrics, such as GPU utilization, memory footprint, and prompt-specific analytics.
• AI-Driven Anomaly Detection: Incorporation of machine learning models for predictive analytics and automated anomaly detection in LLM behavior.
• Scalability Improvements: Optimization for large-scale, multi-model benchmarking and distributed deployments.

GenA11yHelper: DevOps-Powered Prompt Testing for Smarter GenAI Applications

By Yassine Sami, Mihir Soni, Dondre Samuels, Vaishali Jaiswal, Prompt Pilot Group, Canada DevOps Community 2025

GenA11yHelper is a lightweight, DevOps-driven tool designed to streamline prompt experimentation and optimization for large language models (LLMs). By integrating user feedback, automated prompt promotion, and robust versioning within a cloud-native workflow, GenA11yHelper empowers teams to iteratively enhance GenAI applications. This white paper highlights the project’s core strengths, outlines future development directions, and identifies key areas for community and industry collaboration to maximize the impact of accessible, effective GenAI systems.

• Integrated DevOps Workflow: Utilizes Terraform, Docker, and GitHub Actions to automate deployment, prompt versioning, and CI/CD, ensuring repeatability and scalability.
• User-Centric Feedback Loop: Incorporates real-time user ratings to identify and promote the most effective prompts, directly aligning model outputs with end-user needs.
• Cloud-Native and Lightweight: Deployed on AWS using EC2 and S3, with a minimal resource footprint, making it accessible for a wide range of teams and organizations.
• Open and Modular Architecture: Built with Streamlit, LangChain, and OpenAI API, enabling rapid prototyping and easy integration with other GenAI tools and workflows.

Future Perspectives

• Enhanced Feedback Analytics: Planned integration with Weights & Biases will provide advanced visualization and tracking of prompt performance over time, enabling data-driven optimization.
• Expanded Accessibility Features: Future releases aim to incorporate accessibility testing for prompts and outputs, ensuring GenAI solutions are inclusive for users with diverse needs.
• Support for Multiple LLM Providers: Extending compatibility beyond OpenAI APIs to include other LLM backends, fostering broader experimentation and resilience.
• Community-Driven Prompt Libraries: Establishing shared repositories of high-quality, versioned prompts to accelerate GenAI adoption and best practices across domains.

GenA11yHelper demonstrates a practical, DevOps-powered approach to prompt engineering for GenAI applications, with strong potential for future growth and impact. Continued collaboration across open source, industry, and academia will be essential to realize its vision of accessible, effective, and user-driven GenAI systems.

Strengths of Combined Approach

The integration of InsightAI_Minions, AIObservability-Monitoring_InnerAI, and GenA11yHelper represents a significant advancement in the operationalization of GenAI systems. By uniting performance monitoring, real-time observability, and accessibility, this approach lays the foundation for robust, transparent, and inclusive AI deployments—meeting the demands of modern enterprises and diverse user communities.

Combining InsightAI_Minions’ KPI benchmarking with InnerAI’s real-time middleware monitoring provides both macro (system-level) and micro (transaction-level) insights into LLM performance. Integration enables unified dashboards (Grafana, Prometheus) and customizable alerts, facilitating rapid incident response and proactive maintenance.

Unified AI Operations Platform

• Single pane of glass: The integration can evolve into a centralized AI operations platform, offering unified monitoring, alerting, and accessibility management for all GenAI assets.
• Self-healing AI systems: With comprehensive observability and alerting, automated remediation (e.g., rollback, scaling) becomes feasible, reducing downtime and manual intervention.

Overall DevOps for GenAI Hackathon Strengths

• Innovation: Teams demonstrated creative approaches to AI observability and prompt evaluation, including novel dashboards, feedback tools, and automated benchmarking.
• Community Collaboration: The hackathon fostered cross-disciplinary teamwork, with developers, data scientists, and DevOps engineers collaborating closely.
• Rapid Prototyping: Projects showcased the ability to rapidly prototype and iterate on ideas, leveraging open-source tools and cloud-native technologies.

Strategic Gaps in Coding and Further Considerations

Despite promising prototypes, several strategic coding gaps were observed:

• Integration Depth: Many solutions demonstrated basic integrations between DevOps pipelines and AI models but lacked end-to-end automation or seamless CI/CD integration.
• Scalability: Most projects were at the proof-of-concept stage, with limited attention to scalability, multi-cloud deployment, or enterprise-grade robustness.
• Security & Compliance: Few projects addressed data privacy, secure model deployment, or compliance with industry standards—critical for production AI systems.
• Testing Coverage: Automated testing, especially for AI-specific edge cases and prompt variability, was often incomplete or absent.

Future Perspective

• Production-Ready Solutions: Moving forward, projects should focus on hardening prototypes for real-world deployment, emphasizing scalability, security, and maintainability.
• Standardization: There is a need for standardized interfaces and APIs for AI observability and prompt evaluation tools, facilitating broader adoption within enterprise DevOps workflows.
• Continuous Learning: Integrating feedback loops to enable models and prompts to evolve based on real-world usage and monitoring data will be crucial.
• AI Ethics & Trust: As GenAI systems become more pervasive, embedding ethical guidelines and transparency into observability and evaluation tools will be essential.

Collaboration Required

• Industry Partnerships: Collaboration with cloud providers, AI platform vendors, and enterprises will help transition prototypes to production environments.
• Open-Source Community: Continued engagement with the open-source community can accelerate development, testing, and adoption of these tools.
• Academic Involvement: Partnerships with universities can drive research into advanced observability metrics, prompt evaluation methodologies, and AI system reliability.

Importance of Community Building

Beyond enhancing individual skills, hackathons foster strong community building by bringing together diverse talents and perspectives, which boosts networking, teamwork, and knowledge sharing. This sense of community not only accelerates professional development but also inspires collective innovation, creating environments where participants support each other and co-create impactful solutions that might not emerge from solitary efforts.

Hackathons as a Way to Bridge Academia and Students

Hackathons act as a key educational strategy linking academic theory with practical, industry-relevant experience. They cultivate a culture of creativity, collaboration, and innovation that benefits students by preparing them for the complexities of modern professional environments while also enriching academic programs through close ties with industry.

Conclusion

The DevOps for GenAI hackathon has energized the local tech community and highlighted both the promise and challenges of integrating GenAI into DevOps workflows. Addressing the identified coding gaps and fostering broader collaboration will be essential for maturing these innovations into impactful, production-ready solutions.
July 28, 2025
How to Host a Successful Hackathon: Encourage Innovation and Deliver Value

In the tech world, hackathons are not just events; they’re incubators for real-world innovation and collaboration. They bring together not just coders, but visionaries eager to push the boundaries of what’s possible with technology. As these events become mainstream, organizations from tech giants to startups are leveraging hackathons not just to unearth coding prowess, but to kindle a culture of continuous innovation and ethical AI development.

If it sounds like a hackathon would benefit your organization, you’re likely correct – but a challenging, fun and well-organized hackathon doesn’t happen on a whim. First, you need to nail down ground rules, guardrails and goals to ensure the best return from the event.

Setting Goals and Determining Desired Outcomes

Before determining the objective or challenge of the hackathon, it’s important to consider your organization’s priorities and what you hope to achieve by hosting it. This can – and should – vary widely depending on the scope of your business.

If you’re a vendor of proprietary software, you might consider using the hackathon to gauge the accessibility and user experience offered through your project. It could also be an opportunity to encourage customers and partners to use your solutions to solve business problems in ways they hadn’t thought of before. If you’re questioning current cybersecurity measures, it could be beneficial to format the event around auditing and recording security discrepancies or vulnerabilities.

Hackathons also don’t need to be an externally facing event – if you have in-house talent, hosting an internal hackathon could make the most of your resources while bolstering camaraderie and chemistry among employees. Internal hackathons can enable goals in sensitive areas of your business that couldn’t be exposed to external developers.

Once you’ve determined the primary objective and attendee profile for your hackathon, you can determine the challenge you’ll pose to participants that will ultimately yield the desired outcome.

Creating the Perfect Challenge

The crux of a successful hackathon is a challenge that mirrors real business problems, engaging minds to develop solutions that are not only innovative but also practical and ethical. It’s about responsibly applying AI to do good in ways that resonate with your core values. At ABBYY, for instance, we’ve witnessed teams of professionals harness the low-code capabilities of our intelligent document processing platform to solve complex data puzzles within hours — not just as a testament to the power of AI but to the creativity and collaborative spirit of the participants.

These teams were comprised of professionals from Deloitte, PWC and Unilever, indicating a strong knowledge base of document-centric challenges. They worked to solve advanced scenarios involving handwritten forms with identity documents, claims processing, bank checks with signature extraction, unstructured data with named entity recognition, combined PDF, and bank statements.

With the vast experience of the competing teams and the accessible low-code/no-code interface of Vantage, the winners of the hackathon were able to develop and present robust solutions within a four-hour period.

Note the designation of “winners,” as hackathons are traditionally a competitive activity. Therefore, there needs to be guidelines for rules and adjudication while still ensuring creative freedom.

Crowning Winners and Encouraging Creativity

Competition, while central to the hackathon’s vigor, is curated to fuel creativity and not just to crown victors. It’s about recognizing those who think differently and championing the potential of AI to enhance — not replace — the human element in the workplace.

Hackathons often entail both development and presentation skills, as teams will pitch their completed solution. Judging criteria should consider all steps of this process and account for the competitors’ ability to illustrate the full breadth of their product. This ensures that all team members’ efforts are considered, encouraging deeper collaboration.

An example of comprehensive judging criteria could be:
• Completeness of solutions
• Functionality of solutions
• Uniqueness of solutions
• Clarity of presentations
• Teamwork and collaboration

Not every team will walk away a winner, but with a sufficiently engaging objective, each participant can leave the hackathon having learned something new, made a new connection, or otherwise developed themselves and their professional skills.

Finding Opportunities Through Feedback

Feedback from these events is gold dust. It’s not just about what went right; it’s about learning, adapting and constantly improving. It’s a dialogue that continues long after the hackathon ends, ensuring that each subsequent event is more aligned with your vision for AI’s role in society.

Were there opportunities for logistical and organizational improvement that could have limited the event’s potential? Were the assigned scenarios excessively challenging, or perhaps not challenging enough? Did participants expect more opportunities for networking? Ask your attendees for their thoughts on every facet of the event that could have impacted the value brought to them and your organization.

Participants in ABBYY’s recent hackathon found value across the spectrum of development and professional growth. “I feel like we learned many things, like executing new strategies and how we can interact with other technologies,” said Radhakrishnan R, associate software engineer at PWC. “The other great thing is we connected with many people. This was a good opportunity to build a great network – we saw how many people are working with this technology, and we got to meet them in person.”

Mutual and Multifaceted Benefit

For companies that sit at the forefront of AI technology, hackathons are an opportunity to test the waters of user experience and accessibility and to push platforms to solve business problems creatively and ethically. They are a testament to a commitment to compliant and explainable AI, aligning with the ethos that technology must be as responsible as it is revolutionary.

In essence, a hackathon, when done right, reflects a belief in the symbiotic relationship between humans and AI. It’s a platform where innovation meets practicality, where ethical AI development is not just a buzzword but a practice, and where every participant, regardless of the outcome, leaves with a richer understanding of AI’s transformative power.

December 11, 2023
Appian World Recap: Low Code, BPI, Hackathon & The Woz

I had a chance to attend my first Appian World a few weeks ago. It was right in my backyard, at the iconic Fountainbleau in Miami Beach. I brought a video crew with me and had a chance to do some deep dive interviews with customers, partners and Appian execs. I will highlight those further down. The real story of Appian World, though, was about low code, business process management, hackathons and the Woz. I wrote a recap of the CEO’s keynote from the show, but wanted to follow up with more details.

Appian had announced a hackathon with the winners to be announced at Appian World after the judges—John Rymer, Steve Wozniak and myself—selected the winner from five or six finalists. The winner received a $5,000 cash prize. Contestants came from all over the world; some were teams from Appian partners and customers, while others were individuals.

(Before I tell you more about the winner, let me say that judging this hackathon alongside Steve Wozniak was a personal bucket list moment for me. I did get a little giddy standing next to the Woz and talking with him about the hackathon contestants. But enough about me.)

Back to the hackathon. After evaluating the finalists, we (the judges) decided that the winner best exemplified the spirit of a hackathon. Brad Cauzza created FRED, a facilities record database. Basically, using Appian low code app building and integrations, Brad built a system that allowed visitors to sign in when visiting a facility. It also tracked incoming packages and more. It was a really nice use of the Appian platform.

There were some finalists who had grander applications, but we thought FRED was a great example of what you do at a hackathon. Below is my interview of Brad and Malcom Ross of Appian.

While we are looking at interviews, I also had a chance to sit down for a deep dive with Appian founder and CEO Matt Calkins. Matt has guided Appian from a small group of he and his three co-founders all the way through to public entity. Appian is not an overnight wonder; it has been a 19-year trip for Matt and his team. He shared a lot of his views on this journey, as well as where he sees Appian going in the future.

As I mentioned, there were actually four founders of Appian and they are all still with the company 19 years later. That is quite unusual, to say the least. I also had a chance to sit and chat at length with another Appian founder, Michael Beckley, who is CTO-founder of Appian, like Matt from day 1 all of those years ago. Here is my discussion with Michael:

Over the course of the event I spoke with several Appian folks, as well as partners and customers. You can see the whole YouTube playlist of Appian World 2018 interviews on our DevOps TV YouTube channel.

— Alan Shimel

May 29, 2018
Hackathon: DevOps Q&A with Appian VP Malcom Ross

Hackathons are a great way to demonstrate the value of a platform. I had the chance to speak with Malcolm Ross, vice president, Product at Appian to share a bit more about Appian’s virtual hackathon to correspond with its upcoming user conference.

Full disclosure: I’ll be one of the judges attending Appian World to judge the finalists, so hope to see you all there!

Alan Shimel: Can you share a high-level overview of the Hackathon?

Malcolm Ross: Appian is holding its first online hackathon in conjunction with DevPost to correspond with our upcoming Appian World conference in late April. We’re looking forward to seeing how participants are able to build and deploy powerful enterprise apps with Appian’s low-code cloud development platform.

Shimel: Who could apply for the Hackathon?

Ross: Whether they’re an existing Appian customer or not, we challenged anyone to build a new app on the Appian platform that enables their business to do more. There’s no specific theme—just show us something that improves their team’s performance and drives enterprise innovation! We asked all participants to submit their applications by March 27 along with a demo video (hosted on YouTube, Vimeo or Youku). This should incorporate a step-by-step visual demo, and additionally, a way to access the working application for judging and testing. Anyone interested in participating received a free 60-day instance of the full Appian Cloud platform to support their development efforts.

Shimel: Who judges the winners?

Ross: A panel from Appian will score all eligible submissions to determine the top three finalists. Finalists will be notified by April 13 and given the opportunity to demo in-person or remotely to an all-star panel of judges, including Apple co-founder Steve Wozniak, Forrester analyst John Rymer and yourself, Alan! We’ll also have some Appian executives involved in the process including ‎Senior Vice President for Strategy Lang Ly, Chief Technology Officer Michael Beckley, Director of Product Strategy Jorge Sanchez and myself.

Shimel: What are the evaluation criteria for the selection of the winning team?

Ross: It’s pretty open-ended for participants to come up with their own innovations. The main requirement was that the users must build a functioning app on the platform by March 27. More about the criteria can be found here.

Shimel: What are the awards?

Ross: There are three cash prizes ($5,000, $3,000 and $2,000) along with admission to Appian World 2019 and promotional of the submissions by Appian on the Appian AppMarket.

First place will receive $5,000 cash and four free passes to Appian World 2019, and the Appian Deluxe Promotion, which includes a featured promo on Appian.com, Appian AppMarket (if the app is in the market) and Appian Community, joint webinar and collateral, a blog post on Appian.com and a press release.

Second place will be awarded $3,000 cash and two free passes to Appian World 2019, and the Appian Promotion, which includes a featured promo on Appian AppMarket (if app is in the market) and Appian Community and a blog post on Appian.com.

The third-place prize is $2,000 cash and one free pass to Appian World 2019, and the Appian Promotion, which includes a featured promo on Appian AppMarket (if app is in the market) and a blog post on Appian.com.

Shimel: What is the timeline for the Hackathon?

Ross: As mentioned, the deadline for entry was March 27. We’ll make sure to notify finalists by April 13 so they have some time to book travel if they would like to meet our all-star panel of judges for the conference. Final judging will be held on April 25 at the Fontainebleau Hotel in Miami Beach, Florida.

Shimel: Where can people find more information about the Hackathon?

Ross: Anyone who’s interested can find more information about Hackathon at Appian.devpost.com or learn more about our Appian World conference at appianworld.com.

— Alan Shimel

March 28, 2018