Start building sooner.

Let our ✨Backlogue generator deliver a complete Jira backlog including Epics & Stories with implementation details and UAT. Stay in flow with the tools you know and love. No training required.

Ready to turn 30 hours of backlog prep into 30 minutes?

Actual Jira project generated by AgilientAI

✨BACKLOGUE

Upload your features. Once uploaded, it takes about 2-3 minutes to graduate from a backslog to your ✨Backlogue.

Files accepted: csv, txt, xls, doc, pdf Upload & Generate Jira Backlog

Limit 5MB. Free version generates a max of 8 Epics, 40 Stories, 120 Subtasks.


FAQs

I already do these with my LLM. Why use ✨Backlogue?

Sure, but this inefficient and risky.

Due to the inefficiency of prompting, you will only reduce your backlog efforts by 30%, and pay a hefty price. You'll pay in varying token costs, frustruation in trying to get a consistent output and yiled no repeatable methods and processes. The most dangerous aspect is the potential for data breeches and unintended injections that compromise systems.

Why is ✨Backlogue a better option?

We believe that the prompt box should be abstracted away to reduce surface attack areas. In a move towards simplicity. AI solutions should be simple and familiar but never make you and your customers less safe.

We are a direct investment in your most valuable asset: your highest-skilled human capital. Our platform embodies the "Amplify, Don't Replace" principle, ensuring that our technology is not just another tool but a force multiplier for your development teams. By eliminating the friction of prompt engineering, we unlock their creative potential and allow them to deliver more value, faster.

Which LLMs does ✨Backlogue use?

We are model agnostic.

AgilientAI is the technology behind ✨Backlogue. It implements enterprise level secret handling, tokenization and secure Google Cloud Storage. Enterprise accounts can "Bring Your Own Key" and still leverage the proprietary algorithms.

Is ✨Backlogue safe, reliable and scalable?

How does ✨Backlogue mitigate the security vulnerabilities inherent in open-ended LLMs?

Our core architecture is built around the "Don't Make Me Less Safe" tenet. Backlogue's "magic boxes" operate in a highly controlled environment, providing a secure, sandboxed layer between your sensitive data and the underlying models. This structured approach eliminates the risks of data breeches and prompt injection that are common in open-ended, public-facing AI.

How does this solution fit into our long-term AI strategy and what is its scalability across our enterprise?

Our platform is a direct investment in the "Prioritize Speed and Efficiency" tenet. By automating the most time-consuming aspects of backlog generation, Backlogue allows your team to reallocate hundreds of hours from low-value, repetitive tasks to high-impact strategic work. This fundamental shift in resource utilization directly translates to a measurable reduction in project overhead and a faster time-to-market.

How does AgilientAI address technical debt often created by the unpredictable outputs of large language models?

We subscribe to the "Abstract Complexity, Preserve the Familiar" principle. ✨Backlogue is designed as a foundational, "abstraction-as-a-service" layer that is intended to be integrated into your existing enterprise processess. By providing a clean, simple interface, we can scale our solution without requiring extensive training, ensuring a high adoption rate and long-term viability.

How is the project data used?

What are the data use policies?

We abstract data during processesing to train models on best-in-class prompt optimizations, better cost management and detect bad actors in the system.

How does AgilientAI use my data to train your models?

AgilientAI models are always reasoning to optimize for more relevant epics, stories and subtasks. When you generate a project, our AI models analyze best-in-class prompts to lock in the stellar examples of output. Our agents learn industry jargon, constraints, dependencies, etc., in order to deliver a better product with every run. The models do not track personal data, however if you have sensitive customer data in your upload sheet, as with any AI product, it is imperative to remove it before processing.

Can we audit the decision-making process within Backlogue's "magic boxes" to ensure regulatory compliance and accountability?

Our platform is a testament to our "Be Transparent, Build Trust" tenet. While ✨Backlogue abstracts complexity, the output remains fully auditable. We provide a transparent log of the inputs and generated outputs, ensuring your organization can maintain a clear and defensible chain of custody for all data and decisions. This is not a black box; it is a meticulously engineered system designed for full accountability.