Insight

The Executive Guide to AI-Assisted Software Development

Imagine saving 4,500 years of developer work in just one year—an achievement so astonishing it sounds improbable. Yet, in a LinkedIn article from Amazon’s CEO, that’s exactly what the company claims to have achieved by integrating AI into their code modernization efforts. This isn’t a distant dream; it’s a strong example of AI's impact on software production.

Currently, American enterprises are pouring nearly half of their IT budgets into application development and support, as highlighted in a recent study on IT spending intentions. These applications are more than just operational efficiency enablers—they’re often the main way businesses connect with customers and drive differentiation. The ability to consistently deliver the right digital products, quickly and with high quality, is a significant competitive weapon. Despite Marc Andreessen's 2011 prediction that "software is eating the world," many enterprises are still grappling with the gap between their digital transformation ambitions and the harsh realities of a compressed macroeconomic environment. This tension has left executives in a difficult position.

AI-enabled software development could finally bridge this gap, offering a pathway to both reduce decades of accumulated technical debt and spark a wave of digital innovation—all without the need for increased budgets. Importantly, software development doesn’t just encompass coding and technical tasks, it covers product engineering and digital business transformation as a whole. As enterprise leaders explore the potential of artificial intelligence (AI) in the software development lifecycle (SDLC), including the full scope of activities involved in crafting digital solutions that deliver tangible value, they're faced with a landscape rich in opportunity but also rife with complex questions:

We've invested $325 million in a platform we call “CoreAI” to transform our parent company, Publicis Groupe. These efforts have equipped us with unique insights and practical strategies to help enterprises navigate the AI revolution in software development.

1. Understanding AI-assisted software development

• Definition and explanation of AI-assisted software development
• Differentiation between AI-assisted and traditional development methods

2. Capturing value from AI-assisted software development

• The key benefits of AI-assisted software development
• What are the top AI use cases in the software development lifecycle?
• Using lean value streams for optimization
• Measuring and managing value, speed and quality improvements
• Cross-discipline collaboration and alignment
• Real-time feedback and mentoring
• Application modernization

3. The need for specialized tools and platforms for AI-assisted software development

• The “jagged frontier” of AI capabilities
• The power of fine-tuned AI
• Why prompt engineering is not enough
• How specialized tools and platforms help
• Examples of fine-tuned AI in action
• Adaptability is the key to success

4. Issues, risks and mitigations with AI-assisted software development

• Are AI outputs explainable?
• Security and disclosure
• How to pick use cases that minimize risk
• How AI errors can be mitigated
• The risk of infringing on others’ intellectual property
• The AI skills needed to mitigate new AI risks

5. Preparing for what’s next: future trends in AI-assisted software development

• Inventing the future and Jevons paradox
• Transforming IT capabilities
• Moving from general contractors to inventors
• Exploiting proprietary assets
• Conclusion: The new AI frontier in software development

6. How Publicis Sapient can help

Claude 3.5 Sonnet and OpenAI o1—leading the way in coding

Consider the recent release of Anthropic’s latest large language model (LLM), Claude 3.5 Sonnet, on June 20, 2024. Claude 3.5 Sonnet made headlines for its industry-leading performance across a range of benchmarks, particularly in its ability to write software code.

One standout evaluation involved the model’s ability to understand an open-source codebase and implement a pull request for a bug fix or new feature based solely on a natural language description. The model was then tested on whether all the codebase tests—kept hidden from the model—would pass for the completed submission. This wasn’t just a trivial exercise; the problems were based on real pull requests, requiring the model to edit multiple files, write, run and iteratively correct code in a secure, sandboxed environment without internet access. Achieving a new high-water score on this benchmark is a remarkable feat. It demonstrates AI’s growing ability to autonomously manage complex, multistep software development tasks, from understanding requirements to coding and testing, all without human guidance.

The buzz surrounding Claude’s release was immediate. Social media lit up with excited posts from developers and enthusiasts alike. A technical founder on Reddit claimed a 10x productivity boost using the model compared to pre-LLM days.

Another individual shared their experience in a post on X of deploying a web app using Claude, Replit and Google Firebase after just six days of learning to code.

Or, consider the more recent release of OpenAI’s “o1-preview” enhanced reasoning system, formerly known as “Project Strawberry/Q,” on September 12, 2024. This new model approaches problem-solving with a more deliberate, reflective process. Early tests show that o1-preview can analyze, backtrack and weigh different options before arriving at a solution, significantly reducing the likelihood of errors or inaccurate responses. This makes it well suited for complex tasks that require planning and iteration—such as enterprise-level code development or large-scale program planning. OpenAI reports that o1 has dramatically improved its Codeforces Elo benchmark performance, jumping from the 11th percentile, “Newbie,” to the 89th percentile, or “Expert” level, compared to human developers.

These stories are thrilling, and it’s easy to get swept up in the idea of a future where the SDLC is fully automated by AI. However, as exciting as these developments are, in an enterprise context it’s crucial to separate the reality from the hype. Understanding where AI is genuinely transforming the SDLC—and where it falls short—requires a balanced perspective.

Definition and explanation of AI-assisted software development

AI-assisted software development refers to the integration of AI technologies, particularly LLMs, into the software development process to enhance and accelerate various aspects of business and systems analysis, design, coding, testing, deployment and maintenance. This approach leverages AI to augment labor-intensive tasks, such as analyzing existing artifacts, ideating design alternatives, identifying potential bugs, optimizing performance and generating code, test cases and documentation based on natural language descriptions and existing work products.

Differentiation between AI-assisted and traditional development methods

Traditional software engineering relies heavily on human developers. It’s true that any modern SDLC already uses automation extensively: DevOps tooling automates repetitive tasks like code integration, regression testing and deployment, while static code analyzers use predetermined rulesets to analyze and grade code for compliance. Yet, even with these tools, traditional development demands deep domain and technical expertise and involves a significant amount of manual effort throughout the lifecycle.

AI-assisted software development, on the other hand, integrates AI, especially LLMs, to more efficiently and accurately perform or assist with tasks that were formerly resistant to automation. LLMs are particularly powerful tools for software tasks because they have been pretrained on a vast corpus of preexisting software engineering data. Leveraging their trained models, AI tools like GitHub Copilot can suggest lines of code or even entire functions as developers type, acting as an AI-powered pair programmer. More advanced AI systems, such as Claude Sonnet 3.5 and Cognition’s Devin, go a step further by understanding complex codebases and making sophisticated changes. These systems can implement new features or fix bugs based on high-level instructions by iteratively writing, modifying and testing the code.

The main difference: Traditional DevOps and other SDLC tools, like code analyzers, are rule-based and deterministic, meaning they always produce the same outputs for a given set of inputs. AI tools are probabilistic, with outputs that can vary depending on the data they were trained on and the specific inputs, such as the prompts and context they receive.

The implication: While AI-generated code can speed up development, it may not always meet the cleanliness or standards expected in an enterprise environment due to its training or the quality of inputs.

What does this mean for IT leaders? A skilled developer with a deep understanding of the business domain and development frameworks can use AI as a true force multiplier in improving efficiency and quality. Without this expert human steering the AI and critically examining its outputs, the gains are quickly lost, and either productivity or quality, or both, are compromised.

Therefore, integrating AI into the SDLC must be done with careful consideration and continuous skilled human oversight.

The top AI use cases for developers

According to a recent Stack Overflow survey, the top three AI use cases for developers are:

• Writing code
• Searching for answers
• Debugging

In fact, while AI tools are often used by software developers for quick answers and assistance with small code sections, the same survey also reported that 38 percent of developers report code assistants provide inaccurate information half of the time or more. There are new code assistant tools being released every day, though ChatGPT and GitHub Copilot dominate the space, with the survey showing GitHub Copilot and Anthropic’s Claude rapidly growing in usage. 

However, as we will discuss next, the potential for AI-assisted software development is much larger than developer assistants and code completion.

What are the top AI use cases in the software development lifecycle?

By focusing on the full SDLC from concept to production, we’re not just speeding up code development; we’re accelerating the entire process of delivering customer and business value, adapting swiftly to feedback and maximizing impact by trimming low-value features.

This holistic approach is crucial for truly agile and effective software development:

Strategy and planning: Competitive research, web and behavioral analytics
Gen AI productivity gains: 20%–40% reduction in time for identifying actionable trends

Design: Designs, reverse engineered code plans using repositories, backlog creation/management, story sizing, sprint planning
Gen AI productivity gains: 20%–40% reduction in drawing IAs and designs

Coding: Generated code, integrations and data models
Gen AI productivity gains: 30%–50% reduction in net engineering time

Building, testing, verifying and integrating: Self-testing code, exhaustive scenarios and end-to-end testing
Gen AI productivity gains: 50%–60% reduction in code defects, overall time to test

Release and maintenance: Automated dashboards, insights, traffic and monitoring
Gen AI productivity gains: 20%–30% reduction in mean time to resolve and operational readiness

These gains present an opportunity to nearly double the rate of digital innovation while keeping budgets flat.

Using lean value streams for optimization

To capture economic value, we apply lean thinking across the complete SDLC, combining value stream mapping and a catalog of targeted AI-enabled interventions. These are the same techniques that made the Toyota Production System famous—applied to the SDLC process.

When prioritizing where to start introducing AI into the workflows, it’s essential to prioritize, plan and measure the impact of the interventions.

For example, in one set of measurements with GitHub Copilot, we see the highest gains with AI assistance during code creation in Java and React, with approximately 50 percent improvements, while with code optimization and defect fixing, gains are more modest, varying between 15 to 25 percent.

Finally, it is important to plan for some activities that take more time. For example, generating code might be a lot faster but we might spend a bit more time on ensuring explainability, tests and verification.

Measuring and managing value, speed and quality improvements

In our experience, diligent measurement of speed and quality is essential for optimizing AI-enabled workflows. Relying solely on AI tool analytics is insufficient; it’s important to track detailed metrics across the entire SDLC to understand where productivity and quality improvements are truly occurring.

For example, at this time, the REST API for GitHub Copilot usage metrics provides only aggregated data, such as accepted code suggestions, which is insufficient to examine individual trends or those for each scrum team. It provides insight only into tool usage and not the workflow in which it is being used.

PS KnowHOW is our open-source metrics platform used to diligently measure throughout the SDLC. It gives us real-time visibility into cycle time, quality and value metrics, including the ability to measure the impact of AI interventions.

Cross-discipline collaboration and alignment

AI benefits more than just developers. A transformative benefit of AI is its capability to connect, cross-analyze and generate artifacts throughout the SDLC for different roles, including strategists, product managers, agile program managers, experience designers and data scientists, which can further strengthen collaboration and innovation.

AI can help maintain alignment between strategy, design and implementation, ensuring that the final product adheres closely to original business and architectural intentions—a critical advantage in complex systems where manual oversight often falls short.

Real-time feedback and mentoring

AI can also offer real-time feedback and mentoring, help practitioners refine their skills and avoid common mistakes, create first drafts and review work. For example, solution product managers can leverage AI to generate or critique backlogs and test cases, while developers can utilize AI as a peer programmer, enhancing both individual and team productivity.

Application Modernization

It's not just new application development and routine maintenance that stand to gain. Many large enterprises, particularly those with decades of history, struggle with outdated legacy systems. Simply lifting and shifting to the cloud is not a viable option for many of these systems. Modernizing them to cloud-native technology stacks has, until now, been anything but trivial.

We've conducted over 100 end-to-end legacy transformation experiments using our PS Monarch AI-powered modernization accelerator, achieving:

• Greater than a 50 percent reduction in modernization costs
• 50 percent fewer defects compared to traditional methods
• Up to a 70 percent reduction in cycle times

Picture this:

• A fortress wall with towers and battlements of varying heights, representing AI’s uneven proficiency across different tasks
• Within the fortress walls, AI excels, leading to significant productivity and quality gains for the BCG consultants who were studied
• Beyond those walls, however, AI struggles and can provide incorrect or misleading information, leading to the BCG consultants being 19 percent less likely to produce correct solutions compared to those not using AI

This jagged frontier serves as a cautionary tale for those relying on out-of-the-box LLM chatbots or code completion tools. The metaphor reminds us that while AI can greatly assist with tasks it’s well suited for, it can also be harmful when applied to tasks beyond its current capabilities.

The power of fine-tuned AI

Unfortunately, for the highest-value software development use cases, general-purpose LLMs lack the specific domain and enterprise context required to be genuinely effective in enterprise environments. In effect, many valuable SDLC use cases are outside the castle walls—beyond the jagged frontier. In software development, the real power of AI emerges when LLMs are fine-tuned with unique enterprise knowledge, use case-specific context and by chaining custom task-specific agents and guardrails. That fine-tuning brings the high-value use cases within the castle walls.

Why prompt engineering is not enough

Fine-tuning LLMs with solution patterns, enterprise context and guardrails isn’t just a nice-to-have—it’s crucial for generating enterprise-ready AI outputs. This is simply not possible today with general-purpose LLMs. Some may argue that well-crafted AI prompts can contain all the enterprise context needed to allow the AI to make a useful response. Our experience has shown that while large context windows in modern LLMs allow for generously long prompts, attempting to include all the context in the prompt alone can lead to surprisingly inconsistent results, more frequent hallucinations and diluted relevance. Simply put, relying solely on prompt engineering or vanilla code completion without deeper customization will not, as of this writing, yield the best AI outputs for enterprise software development.

How specialized tools and platforms help

To address this gap, we’ve adopted a new approach: creating task-specific, workflow-enhanced acceleration products that integrate fine-tuned models enriched with domain, use case, and enterprise context, alongside curated prompt libraries and pre-built guardrails.

Enterprise software development challenges demand more than generic, off-the-shelf solutions. While general-purpose LLMs are powerful, they lack the necessary task-specific and workflow context, which can’t be addressed through prompt engineering alone. AI-assisted software development requires tailored acceleration products, much like how Special Forces customize gear for specific missions. These products leverage fine-tuned AI models enriched with relevant context and include built-in safeguards. This ensures teams can confidently produce outputs that align with best practices for their enterprise solutions.

Examples of fine-tuned AI in action

For example, an AI trained on your visual design system can be a game changer for experience designers and front-end developers, ensuring that AI outputs align perfectly with your brand and front-end code standards. Similarly, an AI model familiar with your enterprise architecture and APIs can ensure that its outputs make correct use of your IT foundation, driving efficiency and consistency as it produces and inspects code and test cases. An AI model trained on the nuances of moving an Adobe Experience Manager implementation to Adobe’s cloud service can now assist with such migrations repeatedly and accurately, ensuring its outputs correctly use the architectural patterns consistent with enterprise standards.

Case in point: Amazon used a tuned version of its AI product called Q to upgrade their foundation software applications to Java 17. They observed that by curating the migration pattern and tools with Q, the average time to upgrade an application plummeted from what’s typically 50 developer days to just a few hours.

Adaptability is the key to success

In both the battlefield and the business world, adaptability to the environment and mission context is crucial. The key to building the right AI tools for enterprise software development is identifying the different archetypes or solution patterns where the AI-powered toolset must be precision-built to ensure maximum productivity and quality. It’s crucial to invest in expert AI toolmakers to build the tooling specific for each archetype. The toolmakers ensure that the domain expertise and nuances of the archetype and technology stack are built directly into the AI toolset, thus ensuring the relevance and safety of AI outputs. Each archetype has its own AI toolset, which is managed as a product, to maximize the productivity and quality of the software development teams building solutions of that type.

Are AI outputs explainable?

Explainable AI is critical because the less we make generative AI a black box and the more we understand not just the output but why it chose that output, the better. This is especially true when we use AI for critical enterprise applications.

For example, AI may produce code that works, but developers can't fully understand how or why it was generated that way. AI tools might flag potential bugs without clearly explaining the reasoning behind the detection. AI-assisted design tools could make choices that designers can't easily justify or explain to stakeholders. Similarly, AI-generated requirements may not provide clear rationales to product managers for why certain scenarios were chosen.

This risk can be mitigated by various techniques, such as asking the AI to explain different sections of its outputs, comparing the output of one AI model with another and asking for explanations of key differences or using chain-of-thought prompting. Chain-of-thought prompting encourages AI models to break down complex problems into a series of intermediate steps, mimicking human-like reasoning. This approach involves providing     exemplars that demonstrate step-by-step reasoning, thus guiding the model to generate similar reasoning chains for new tasks. This enables the model to articulate its thought process, leading to more transparent and interpretable outputs.

Advancing AI explainability: How OpenAI's o1-preview model enhances reasoning and accuracy

OpenAI's latest o1-preview model achieves superior reasoning through a combination of reinforcement learning and chain-of-thought reasoning. Reinforcement learning enables the model to continuously refine its problem-solving strategies as it explores constraints, learning from mistakes and adjusting its approach to deliver more accurate and logical outcomes.  By explicitly mapping out its reasoning process, o1 not only explains its output but also identifies its own potential errors and addresses them, significantly improving the likelihood of arriving at the correct solution—much like how humans reduce mistakes when we carefully outline our thinking. We believe o1 is a significant step forward in improving AI explainability, particularly for our SDLC use cases.

Security and disclosure

All enterprises want their secrets to be, well, secret. For many enterprises, it is sufficient to ensure that the enterprise legal agreements with the LLM API provider, like Microsoft, Google, OpenAI or Anthropic, have the right covenants in place to ensure confidentiality and prohibit data leakage, particularly to address the concern that the enterprise prompts or context data will be used to train the external base models. These agreements, in concert with a gateway in the enterprise that scans for and masks sensitive data, like in our own PSChat, might be sufficient.

With some of our clients who work with classified material or high-stakes intellectual secrets, we go the extra mile to ensure that sensitive work products never leave the enterprise by hosting LLMs, such as Meta’s CodeLlama or Mistral, in their own environment. For these clients and in regulated industries like financial services, healthcare and sensitive government sectors, establishing common enterprise foundations that control and monitor access to, enforce security policies for and provide usage visibility of generative AI resources is key. 

Concerns about disclosure are not always applicable. For example, if you're trying to create a web reporting application, you can instruct an AI with a prompt like, "Generate a web app that has tables and checkboxes, and format the data as follows." This allows you to avoid disclosing any proprietary information while still saving a lot of development effort, as neither the prompt nor the output contains sensitive secrets. However, executives must train employees to discern what is confidential information and establish suitable compliance guardrails.

How to pick use cases that minimize risk

With rapid advancements in AI, there’s a temptation to apply it everywhere all at once. IT leaders must evaluate risks by considering the potential impact of AI errors and the ease of detecting and fixing the errors, should they be present.

Just as a risk manager evaluates the likelihood, impact and mitigations for each risk, leaders must evaluate each use case by asking two questions:

a. For this use case, if the AI were to make an error, how large would the impact of the error be?

b. For this use case, if the AI were to make an error, how easy would it be for my software development team to find the error?

Using these questions, it would be apparent that LLMs today are less useful in high-impact, harder-to-inspect tasks like verifying system architectures compared to labor-intensive but lower-consequence, easier-to-inspect tasks like documentation or test case generation.

How AI errors can be mitigated

We must design our AI-assisted workflows with the new checks in the process, commensurate with the use case. For example, with code generation, the benchmark is perfection—no IT leader would knowingly release buggy code into production.

Human developers make mistakes. Therefore, we invented test-driven development and static analysis to assist human developers, and we integrated these tools into our workflows and automation to reduce human error.

Today’s LLMs are incredibly capable and sophisticated token-guessing machines. They too will make mistakes, even if those mistakes become increasingly rare as the LLMs improve. Therefore, we must take the same approach and design our workflows to nudge the AI in the right direction and verify its output using a combination of human review, adversarial AI (where one AI is used to inspect and challenge the generating AI) and other verification techniques. Trust relies on empirical data and ensuring we have rigorous testing processes and human oversight.

The risk of infringing on others’ intellectual property

Choose AI tools from established model providers that have trained their models on code they have rights to use and offer indemnification or legal protection for AI outputs. Keep abreast of ongoing lawsuits and legal decisions regarding LLMs and copyright, as this area of law is still evolving.

The AI skills needed to mitigate new AI risks

For every AI-augmented task in the SDLC, a human employee must be in the driver’s seat. It is a person who points a generative AI tool at the specific kind of problem that they want to solve, provides assistance to generative AI in terms of structuring the problem into smaller, solvable pieces and, importantly, audits, reviews and sanity-checks the results. When employing specialized tools and platforms, the product team that builds the AI toolset builds in guardrails to increase safety, but the software development team must ensure that the AI outputs are fit for purpose.

The biggest risk with the use of AI is inadequate human skills. For example, in human pair programming when an experienced developer is paired with a junior one, there is a risk of the "Watch the Master" phenomenon, where the junior developer becomes too passive.

This is especially applicable to AI-assisted software development. The humans who are guiding and inspecting the AI work require more expertise, not less, whether those humans are business strategists, product managers, experience designers, software engineers or data professionals. They must be curious, skilled at problem decomposition and experts at double-checking the AI so they can take responsibility for the output of the AI and the outcomes of the solution.

Proactive training must equip the team to rigorously scrutinize and verify AI outputs, regardless of how credible and complete they may appear to be. There are also use cases where generative AI, due to biases in the training data, presents some subtle issues. It’s important to know where to apply extra diligence in both application and verification.

Inventing the future and Jevons paradox

 In the future, we can assume that humans will build more software. This article on Jevons paradox and its implications for software development explains why this is inevitable. It describes the counterintuitive situation where technological advancements that make a resource more efficient actually lead to greater overall consumption of that resource, driven by increased demand, rather than a reduction in its use.

For example, improvements in fuel efficiency led to more driving and thus higher overall fuel consumption. Advances in storage technology lower the cost per gigabyte, but the ease of storing large amounts of data encourages more data creation and retention, leading to increased storage infrastructure and resources to power and manage it.

If Jevons paradox holds for AI-enabled software development, as it has with countless other technologies, those businesses that redouble their investments in digital business transformation will have far better odds of maintaining and growing their competitive edge.

Source: Matt Rickard via Substack

This brings us full circle to Marc Andreessen’s famous observation that “software is eating the world.” While his idea captures the imperative for digital business transformation, it raises a crucial question: If software is indeed taking over, why does it seem to take so long to reach its full potential? More importantly, if software were truly to dominate, what would drive this transformation, and what would our world look like? The opportunity to answer these questions is finally available to every business executive, not just those with the resources of digital natives.

1) Transforming IT capabilities

The greatest lift in creating differentiated transformation capabilities lies in skills, talent and organizational transformation. The professionals in our organizations who conceive and build software across Strategy, Product, Experience, Engineering and Data & AI (SPEED) must embrace AI as part of how they work—transforming into cyborgs that seamlessly blend their own intelligence with AI to multiply their efficiency and effectiveness.

This is not just an individual endeavor, but rather one that leaders thoughtfully design, incorporating new roles to support evolving workflows. For example, the myriad technical specialist skill sets in IT evolve into a pool of versatile AI engineers who seamlessly use AI to build solutions across multiple technologies. These AI engineers are supported by a much smaller cohort of deep AI software development product specialists who design workflows and build the tools that the multiskilled AI engineers use.

IT leadership must reimagine and redesign their capabilities, starting with workflow, organization and technology enablers to build this future.

2) Moving from general contractors to inventors

IT needs to focus on more than just creating, testing and launching software, which is often referred to as moving from “backlog to live.” With the speed at which software can now be developed, the real challenge in creating unique value will shift. Instead of just focusing on writing and deploying code, companies will need to pay more attention to how they come up with new ideas, create strategies and design experiences.

Just as a chain is only as strong as its weakest link, the software development process is only as fast and efficient as its slowest parts. IT leaders must rethink and improve the entire process, from coming up with ideas to managing them and finally getting them into production. In essence, this is about realizing the full promise of digital business transformation through product engineering, of which software development is a part.

3) Exploiting proprietary assets

In May 2023, an internal document from a Google employee was leaked and published online in a memo discussing the lack of competitive advantage in LLMs. The memo argued that neither Google nor OpenAI had a sustainable competitive advantage (or “moat”) in LLMs compared to open-source efforts using internet datasets. It was controversial when published but has been seen by some as prescient given subsequent developments in open-source AI models. We can already see the lack of sustained differentiation play out in the rapid race for model supremacy amongst OpenAI, Google, Meta, Anthropic and others.In contrast, large enterprises have a significant moat—proprietary corporate data, knowledge assets and expertise. These can be used to build specific tools fine-tuned for creating accretive enterprise value. Investing in data curation and model fine-tuning will yield significant acceleration compared to using public models alone. Training employees on prompting for the unique capabilities of these fine-tuned models will yield further differentiation.

To maximize the potential of AI, businesses first need to embrace digital business transformation (DBT). At Publicis Sapient, we specialize in guiding companies through DBT, so that they can apply AI across all areas of their business.  

Recognized as a 2023 Market Leader in Generative Enterprise Services by HFS Research, our teams use AI to transform business models, accelerate time to market and reimagine customer and employee experiences, among many other objectives of our clients’ DBT journeys.

Sapient Slingshot

Sapient Slingshot is our proprietary AI platform designed to accelerate software development across all stages of the software development lifecycle (SDLC). The platform supports code generation, testing, deployment and more, powered by agentic AI and an enterprise code library with industry context. When paired with our expert engineers, the platform delivers faster, higher-quality solutions.

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