rhino.ai brings genAI and BRDs to automated software development

In large enterprises, software engineering teams are burdened by the amount of software needed to deliver value to their business. The complexity of legacy systems hamstrings them, the rush to refactor for new platforms, and, recently, the need to infuse artificial intelligence into their products add to the strain. Too often, the result is an increase in effort and decreased output, leading to further disenfranchising of the technology team’s value contribution.

The challenges are simple to quantify. For instance, HFS research documented 60% of firms with more than $1 billion in annual revenue manage between 150 and 400 applications. Yet, technology and applications development teams deliver less than two dozen yearly release cycles. Speed, innovation, and adaptability are critical if a business is to remain competitive. Traditional ways of modernizing, refactoring, and even creating applications are no longer feasible.

rhino.ai’s vision of “reimagining software development and modernization with AI” is at the forefront of its core platform. The company’s founders told us their reason for creating this platform stemmed from the fact that after running a considerable consulting company for a few years, they wanted to build software that could automate everything their teams did and hence, rhino.ai was born. Unlike other AI agents in the market, rhino.ai has been designing, patenting, building, and testing its core platform for several years and has impressive results and endorsements of measured outcome to share with the market.

rhino.ai’s Universal Application Notation* (UAN) makes software development composable, reusable, and deployable across the user’s choice of digital platforms

Let’s think about the software development lifecycle (SDLC) for a moment. Whether following an Agile or Waterfall methodology, software engineering requires teams to follow a vicious cycle that can result in a software engineering team never getting off the development cycle. To succeed, these teams must understand workflows, data pipelines, legacy applications, data dependencies, business requirements, governance, and more. As illustrated in Exhibit 1, this SLDC cycle looks way too much like a flywheel only a hamster would enjoy.

Exhibit 1: Today’s SDLC is a vicious software engineering cycle that encumbers innovation.

Source: HFS Research, 2024

And this is where rhino.ai’s solution changes things.

rhino.ai’s solution approaches software engineers’ challenges: the need for speed to delivery, an ability to refactor legacy to modern platforms (including but not limited to cloud-based architectures), and compliance with corporate governance, security, and best practices. Basically, rhino.ai is helping clients reimagine the SDLC as a composable, collaborative, and consumable model that brings people, automation, and AI together to craft code with immediate relevance and impact. This may be the biggest change in the SLDC since Agile and it answers a question that CIOs have been wrestling with as traditional software development has struggled to keep up with business transformation needs.

Bring in enterprise context and best practices from universal inputs with rhino.ai’s AI extractors and mining capabilities

rhino.ai calls its secret sauce Universal Application Notation (UAN). This differs from using low-code or no-code as a solution for visually adapting one code base into new workflows or applying artificial intelligence to assess and convert code to a best-effort target state. Rather, their patented UAN solution approaches refactoring, rearchitecting, and recoding by ingesting code logic, data schemas, and business rules from all critical sources. These include log files, workflow diagrams, source code repositories, business, and application documentation (via LLMs), and application logic from custom off-the-shelf (COTS) and SaaS applications.

Exhibit 2: rhino.ai UAN brings technical functions and requirements into enterprise contexts and solution delivery.

Source: rhino.ai, 2024

An app is an app is an app — in a format and structure where every application across the enterprise and technology department is represented the same way… by decoupling the software syntax

The key is that UAN creates composable libraries of application requirements with enterprise context. These UAN modules map schemas, workflows, logic, and connections and enable developers, AI agents, and architects to model, assemble, refine, and produce solutions quickly as deployable applications to a defined target software architecture.

Merging AI and application development is all the rage, but CIOs may be settling on short-term value creation

Companies across advisory, IT services, software engineering, and software development tool creation are rushing to infuse GenAI into their solutions. The promise of GenAI is that customers will achieve a boost in production speed resulting in the firm’s software developers being more productive in meeting the business needs of enabling its workforce to out-perform competitors.

Creating a universal application notation language to make software composable and contextual speeds development. However, for rhino.ai UAN is only a beginning. As the usage of GenAI tools to boost software development is growing. Exhibit 3 illustrates the use of GenAI in coding; firms are focusing on productivity and are thus likely to leave money on the table.

Exhibit 3. Generative AI is seen as a game changer in software development

Source: HFS Research, 2024

This is because productivity measurements are a lagging indicator and often subjective. Rather, to be successful the benefits of adding GenAI into the SLDC will only result from tools that allow future-state advantages. We encourage CIOs to think about what the intent of the new features is—functions, integrations, or applications. Understanding the leading indicators ‘what do we want our teams to be able to do?’ is essential to revolutionizing how we currently build, deploy, and optimize our business with software technologies.rhino.ai believes AI and autonomous agents must do more than document and convert code, AI must improve software development and modernization for dynamic business outcomes

The game changer is rhino.ai UAN allows these outputs to be deployed across a number of target deployment architectures, including ServiceNow, OutSystems, Appian, Unqork, AWS, and Microsoft. Moreover, defining business functions in UAN creates a built-in best-practice playbook, meaning teams can deploy solutions based on operational, financial, or technical models. Because UAN is target and tech agnostic, teams also can test for functionality, quality, and business requirements across multiple target states, mitigating lock in and expanding choice.

UAN then allows for the low-code tools available on these application platforms to assemble these composable applications into the workflows of the business. This is where the rubber hits the road, composable applications, with universal integration potential, that can leverage flow centric workflow automation of low-code tools to expedite the creation, customization, and curation of use-centric tools.

Input from HFS OneCouncil about the challenges many are facing with refactoring and transforming software to modern digital architectures show that CIOs seek to address three major challenges:

• Convolution: The challenges in implementing new software or refactoring applications and workflows—while navigating a broad range of application languages, data schema, and workflow dependencies.
• Confusion: The ability to enable their teams, business partners, and IT services partners to use technologies and business requirements to create safe, sustainable, compliant solutions.
• Complexity: Reducing the linear model of working and dependency on stage gates to make IT more responsive to business needs.

Through its architecture optimization, rhino.ai’s solution can improve how the business runs by modernizing the right software (automated functional, policy, and code analysis) in the right places (based on security, policy, user needs, and posture). By converting legacy code and developing new code based on known inputs and aligning with clear business intent, a firm can identify a future state where it is optimized from a software catalog.

rhino.ai is pushing the discussion forward on how autonomous agents can be used as change agents across the software development lifecycle and toward sustained alignment with business purpose

Their solution, called AVA —an autonomous virtual assistant for software development—breaks away from being an AI-based productivity tool. AVA facilitates a ground-up reimagination of software development, not just incremental improvements to SDLC phases.

The vision and design of AVA makes it experience-focused, intent-driven, context-powered, and future-proof. AVA works in concert with solutions developed on traditional coding languages and low-code and no-code solutions. Moreover, AVA incorporates security, private LLM, and the orchestration of inputs from models, humans, and agents to go beyond code refactoring to purposeful code generation.

AVA can orchestrate various AI models and agents, humans, enterprise systems, and software vendor tooling while focusing on the future of software development to be true AI-led software configurations based on human intents. rhino.ai is continuously adding support for more software vendors with its open approach to bring the best of ecosystem tools and AI agents to enterprises.

By connecting UAN to generative AI, rhino.ai provides a roadmap that helps turn the intent of the user into a robust, configurable software solution that augments a human’s capabilities, regardless of their knowledge of source code.

By making business requirements and best practices part of software development, companies can finally automate the Center of Excellence—making it a real-time contributor to the SDLC, not a roadblock at the end of an expensive and demanding project

It has been said that nothing kills innovation like a CoE. The typical journey of a business-led software development lifecycle is the requesting of new features, functions, or solutions. Working collaboratively, software engineering teams will apply the firm’s best practices to develop a solution, test the solution, and then submit for validation. However, the software team may not have insights into new policies, regulations, or authorized best practices. And the CoE, the governing body and last hurdle in many cases, may then put the brakes on days, weeks, or even months of hard work.

rhino.ai is committed to changing this drag on productivity. By going beyond just policy-as-code, they are using AI and LLM to enable enterprises to infuse business requirements early in the SLDC thus making them available for the early stages of software development. Using rhino.ai’s playbooks feature, companies can take business best practices, business logic, governance, regulatory, and security requirements and insert them into the live development of code, automated refactoring efforts, and CI/CD pipelines. Organizations can edit their playbooks in real time and rhino.ai will automatically adjust its advice and suggested changes. It can do this because it developed its UAN and AVA tools to offer real-time developer feedback on functionality, scalability, deployability, and optimization. By baking the CoEs’ documents, policies, and playbooks into the way software is refactored or designed with rhino.ai’s UAN, the traditional SDLC is now able to evolve into the AI-based software configuration lifecycle (AISCLC), see Exhibit 4.

Exhibit 4. Evolving from a traditional SDLC to an AI-Software configuration lifecycle is the future of software development

Source: HFS Research, 2024

For rhino.ai customers, these results aren’t demos. They are based on real-world deployments with customers currently both proof of concept and going live. With rhino.ai, enterprise customers can finally emerge from technology-led transformation to business-driven transformation. And software finally has its renaissance from a service catalog of individual tools into a collection of enterprise-aware capabilities. HFS calls this the Generative Enterprise.

The Bottom Line: Approaching software development with fresh eyes and the latest technology, rhino.ai is helping CIOs, developers, architects, and consultants collaborate, create, test, govern, and run applications that deliver transformative business value.

rhino.ai customers can build once and deploy anywhere. Its UAN is designed to avoid platform or vendor lock-in and adapt as business needs change, not as technology limitations dictate. With AI-powered modeling of code, logs, business requirements, and application workflows and enabling natural language-based prompting for intent, technology and business teams can spin up solutions that meet their technology needs in the platforms they already use while conforming to business requirements.

*Note: UAN patent pending