As image processing becomes central to industries from manufacturing to healthcare, organizations face a crucial question: should visual data be processed in the cloud or at the edge?

The explosion of high-resolution video from smartphones, IoT devices, and industrial cameras is driving demand for fast, efficient processing. In latency-sensitive scenarios like autonomous vehicles or real-time surveillance, delays can be costly — even dangerous. At the same time, privacy concerns and rising cloud costs make data locality and bandwidth efficiency top priorities.

• Cloud or SaaS computing offers scalable power and easy access to advanced AI models — ideal for heavy processing and large-scale tasks.
• Edge or Embedded computing, on the other hand, brings speed and control, enabling real-time analysis and better data privacy by processing directly on the device.

The choice between cloud and edge isn’t just technical — it’s strategic. In many cases, hybrid approaches that combine both will define the future of AI-powered vision systems.

​Why SaaS-Based Computer Vision Is Gaining Traction

As AI adoption expands across industries, many organizations are turning to SaaS-based computer vision platforms to unlock the power of visual intelligence — without the overhead of managing infrastructure or models. While edge AI remains critical for real-time and mission-critical systems, Software-as-a-Service models offer unique advantages, particularly for web-centric and analytics-heavy workflows.

Efficiency Through the Cloud

SaaS computer vision removes the complexity of deploying and managing AI systems. Instead of setting up GPUs, storage, or embedded devices, companies can access ready-to-use tools that process images, videos, and streams via simple APIs or dashboards. This makes rapid prototyping and scalable automation accessible to startups and enterprises alike.

Businesses can go from raw data to insight in minutes — without touching a single server.

Benefits of the SaaS Model

• Speed to deployment: No hardware, no setup. Users simply connect their cameras or upload data and begin analysis.
• Elastic scalability: Cloud platforms handle millions of frames or photos daily, scaling automatically with demand.
• Automatic updates: AI models are continuously improved by the provider, ensuring access to the latest features without manual upgrades.
• Ease of integration: REST APIs, SDKs, and low-code options make it easy to connect SaaS vision tools to existing systems — from CRMs to warehouse software.
• Cost efficiency: For post-processing, non-real-time use cases (e.g., defect classification, visual search, document recognition), SaaS platforms are typically more economical than edge or hybrid solutions.

But Not Always the Right Fit

Despite its strengths, SaaS computer vision has limitations — particularly in applications where latency, connectivity, or data sovereignty are critical:

• In autonomous vehicles, milliseconds matter — SaaS can’t compete with on-device inference.
• In remote environments (e.g., mining, agriculture), network access is inconsistent or unavailable.
• In high-security domains (e.g., defense, healthcare), sending data to the cloud may violate regulations.

In these cases, embedded vision or hybrid edge-cloud architectures remain essential.

The Right Tool for the Right Job

SaaS computer vision is not a replacement for embedded AI — it’s a complement. It shines in centralized, connected, and analytic-driven contexts, where the ability to iterate fast and scale instantly delivers real business value.

As the ecosystem matures, expect to see growing adoption in fields like:

• E-commerce: product search and tagging
• Retail: shopper behavior analysis
• Insurance: automated claims from photo uploads
• Healthcare: cloud-based diagnostics and triage support

Why Embedded Vision Matters

Traditional AI relies heavily on cloud infrastructure — powerful, yes, but also vulnerable to latency, bandwidth constraints, and security risks. In mission-critical environments, this is a limitation that can’t be tolerated.

Embedded vision addresses this by putting intelligence directly on the device. The result? Immediate insights, reduced data transmission, enhanced privacy, and full operational autonomy.

“Imagine detecting a drone threat or an equipment failure in milliseconds — not after a server roundtrip. That’s the edge difference.”

Applications in Focus – Some of the most demanding fields:

• Defense: AI vision systems integrated in anti-drone units and mobile defense platforms for instant threat detection.
• Industrial inspection: Offline-capable anomaly detection for factories, remote rail networks, and energy sites.
• Autonomy: Navigation, obstacle avoidance, and object detection for mobile robots and off-road vehicles.
• Smart infrastructure: Real-time analytics for traffic flow, urban pollution, and smart surveillance with full edge privacy.

Modern defense systems require instantaneous threat identification and response. Embedded vision technologies empower autonomous mobile defense units with real-time obstacle detection and rapid decision-making capabilities.​

In industrial settings, unplanned downtime can be costly. Embedded vision systems facilitate continuous monitoring, early anomaly detection, and predictive maintenance, ensuring uninterrupted operations even in harsh environments.​

Aerospace applications demand compact, efficient, and reliable systems. Embedded AI meets these requirements, providing real-time obstacle detection and navigation capabilities essential for autonomous vehicles and microsatellites.​

Embedded vision technologies are at the forefront of smart city initiatives, enabling real-time traffic analysis, urban surveillance, and efficient public transportation systems. By processing data locally, these systems enhance privacy and reduce latency.​

Precision agriculture benefits immensely from real-time data. Embedded vision systems enable farmers to monitor crop health, detect pests, and optimize irrigation without relying on cloud connectivity, promoting sustainable farming practices.​

In retail, understanding customer behavior is key. Embedded vision solutions analyze foot traffic and shopper interactions in real-time, helping retailers optimize store layouts and improve customer experiences. Similarly, in logistics, automated visual inspections streamline package sorting, reducing errors and accelerating delivery times.

Timely diagnostics can save lives. Embedded vision technology assists in medical imaging analysis, providing immediate insights and supporting healthcare professionals in making informed decisions. In emergency scenarios, real-time visual data aids first responders in efficient and effective interventions.​

In this deep dive, we explore how edge vision transforms key industries and use cases — blending performance, resilience, and real-time insight. Here’s how.

Cloud and Edge: Better Together, Not in Competition

Instead of treating cloud and edge computing as opposing choices, organizations can combine both to create smarter, more efficient AI workflows.

• Synergistic Design: Edge devices can handle real-time detection and filtering, while the cloud manages intensive analytics, model updates, and long-term storage.
• Flexible, Optimized Performance: This hybrid model balances low-latency response at the edge with the scale and power of the cloud — ideal for applications that demand both speed and depth.

By leveraging the unique strengths of each, businesses can build image processing systems that are agile today and adaptable for the future.

Looking Ahead: The Future of Embedded Vision at the Edge

At the core of these applications lies a simple truth: AI is only as good as its ability to act — immediately, and reliably. That’s why we believe intelligence must live at the edge, not onliy in the cloud.

As artificial intelligence continues its migration from centralized data centers to decentralized environments, embedded vision is poised to become the cornerstone of real-time perception in critical systems. What was once a domain of niche aerospace and defense applications is now entering mass adoption across agriculture, logistics, automotive, and infrastructure.

According to MarketsandMarkets, the global market for embedded AI is projected to grow from $8.2 billion in 2023 to over $18.6 billion by 2028, driven by demand for on-device analytics, reduced latency, and operational resilience. This acceleration is further fueled by advancements in edge computing, energy-efficient neural processors, and sensor fusion technologies.

The next frontier? Collaborative swarms of autonomous drones, vision-enabled edge robotics, and self-healing smart grids. Future embedded vision systems will not only detect objects but understand context, perform multi-modal fusion with radar or LiDAR, and learn continuously on the edge. Integration with neuromorphic chips, event-based vision, and federated AI training will push the boundaries of what’s possible — securely, in real time, and without reliance on external networks.

From disaster zones to planetary exploration, embedded vision will be the silent enabler of machines that must think independently, act instantly, and operate autonomously — regardless of bandwidth, cloud access, or terrain.

The coming decade won’t just be about intelligent software. It will be about intelligent systems, on-site, on time, and always aware.

—

About Athis Technologies
Athis Technologies Ltd is a European company building real-time, offline-capable embedded vision solutions for critical systems. We deliver edge AI that empowers autonomy, safety, and operational excellence — without compromise.

Athis technologies : A Unified Vision for a Smarter, Safer World

The future of intelligent monitoring doesn’t belong to a single product or architecture — it belongs to ecosystems that are agile, interoperable, and built for the real world.

With its integrated suite of embedded hardware and cloud-native platforms, Athis Technologies delivers more than just point solutions. Whether it’s Sentinel guarding sensitive perimeters, EdgeVision powering industrial reliability, or FleetIQ monitoring vehicles across vast networks, each module is strengthened by its connection to VisionCloud — the centralized AI dashboard that brings everything together.

At the core lies Synergy — the principle that when devices, data, and decisions are unified, the result is greater than the sum of its parts. It’s this synergy that enables Athis to support real-time performance at the edge, centralized insight in the cloud, and seamless orchestration across both.

In an age where milliseconds, security, and scalability all matter, Athis Technologies is building the kind of unified intelligence the world needs — now and into the future.

Visit Athis Technologies

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These concurrent shifts promise to drive economic growth through productivity, employment, and investment. In terms of investment volumes looking ahead until 2030, the energy transition will probably be larger by a factor of ten. Over that period and beyond, these shifts will intersect in ways that could amplify their benefits—or challenges. Both will happen in similar geographies, notably China, North America, the European Union and India. They will also access similar pools of global capital.

AI will be an enabler for cleaner energy deployment

The energy transition needs to be managed in a way that it does not impose high costs on the consumers, while ensuring reliable energy supplies and AI will act as an enabler to achieve both. At ReNew, leveraging AI has not only improved our electricity output by up to 1.5% from existing solar and wind installations but also streamlined maintenance, demonstrating AI’s potential to enhance efficiency and reduce costs.

Big data, and innovation in analytics, enable us to measure inputs from satellites, sensors and weather monitoring stations to predict solar radiation and wind speed, accurately forecasting the supply of renewable energy generation. On the side of the equation, AI is accumulating terabytes of historic consumer data to forecast consumer demand for electricity. Balancing supply and demand is critical in preventing supply disruptions and blackouts.

Globally, almost $3 trillion worth investment is being allocated between now and 2030 to lay the wires and infrastructure to transport clean energy from points of generation to the consumers. Several companies are already leveraging AI for strategic decision making in terms of planning which type of grid is suitable to which location, all the way down to the size of the wires. With several of these wires running thousands of miles, it is difficult to inspect and maintain them. New machine learning software predicts anomalies in wiring and failures of transformers, saving time and money. While actual numbers are larger, even 5% savings on capital expenditure for installation and replacement, will result in reduced expenditure of $150 billion in the next 7 years.

However, AI is an enabler for more efficient and sustained fossil fuel driven activities too

Like the internet, AI is a tool that is useful for everyone, including the fossil fuel sector. It is an equalizer. Companies like BP, Shell, Exxon are already using AI to lower the cost of extracting oil and gas. Autonomous vehicles, based largely on AI, are increasing in numbers – most of which run on gasoline. By making travel cheaper and more convenient, autonomy could increase the number of vehicle miles travelled. If media reports are to be believed, General Motors Co.’s Cruise and Alphabet Inc.’s Waymo are likely to begin offering self-driving taxis in San Francisco shortly. The merger between electrification and autonomy of vehicles is likely to take some time, based on improvements in batteries, sensors, and computation capabilities.

AI will also be a huge energy guzzler

Training AI models (meaning setting up AI models to spot patterns in datasets) and delivering inferences (meaning numbers, text, videos, imagery based on the patterns) require huge amounts of computing power and data storage. The energy demands of AI, particularly for data centers, are soaring, potentially rivalling the consumption of entire countries like Brazil, South Korea or Germany. According to the IEA, data centre energy usage stood at around 460 terawatt hours in 2022.

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There are lessons from the energy transition revolution that are relevant for the AI revolution

To be candid, I am cautiously optimistic about deploying Artificial Intelligence. Drawing lessons from the energy transition journey, there are three areas where we must collectively pay particular attention, to ensure that AI makes a strong positive contribution to humanity.

Diversification of solution providers: A single US based firm holds around 80% of the high-end AI chip market. The few big tech firms hold most of the computational capabilities, datasets and servers that will enable AI to even function. Much like energy transition, capabilities are concentrated in one or two nations, posing risks of disruptions and trade controls. A wider set of nations, including from the global south, need far more active domestic policies to develop their own AI technologies, solutions, and business models.

Governance to ensure reliability, accountability, and dispute resolution: As the use of AI grows, there will be ever more capturing of data. This makes us prone to errors (due to use of poor-quality data), cyber-attacks and data-theft. These will need appropriate legal provisions, that ensure access by clients to datasets used by service providers and allocation of responsibilities for safety and privacy of the data.

Making it low carbon, before we are locked-in: AI being a sunrise sector, presents an opportunity for being lower carbon right from its early stages. We have the technological solutions to do so and a number of the top 10 companies globally that run data centres have adopted bold targets for achieving net zero emissions. Accountability towards meeting these targets will need to be ensured over the next few years. Large investors, that have many of these companies in their portfolios, currently seem to be focused on understanding and minimizing the social risks of AI. There must also be a focus on the environmental implications. Equally, the biggest clients must start accounting for emissions due to AI services received by them in their Scope 3 emissions, and take steps to reduce them significantly.

As we stand at the crossroads of these technological revolutions, our choices today will determine whether AI becomes a pillar for a sustainable future or a missed opportunity. Embracing these lessons with caution and optimism is not just advisable; it’s imperative.

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Amid geopolitical tension, economic uncertainty and extreme weather events, the resilience of supply chains around the world is being put to the test on a daily basis.

Meanwhile, industry leaders are tasked with managing complex supplier networks, enhancing transparency to showcase sustainability credentials and developing data-driven strategies to quickly meet customer demands. 

In a bid to understand the biggest challenges facing today’s supply chain executives, as well as their key motivations and goals, CGI commissioned Supply Chain Digital and sister title Manufacturing Digital to gather the thoughts of dozens of experienced professionals. 

Perhaps unsurprisingly, the results reveal that artificial intelligence (AI) is already a key area of focus for leaders as they look to successfully navigate this ongoing period of uncertainty. 

Striving for resilience

Resilience and risk management have always been supply chain staples, but their perceived importance has been amplified following a series of major events with far-reaching impacts – beginning with the COVID-19 pandemic. 

Leaders said being data-driven – encompassing visibility, transparency and traceability – was the top attribute (87.4%) of a resilient supply chain, followed by agility and flexibility (78.2%) and predictive/real-time scenario planning (55.2%). 

Interestingly, local production, ESG and security were considered to be less pivotal factors when it comes to resilience. 

Scoring their own supply chains out of 10 in relation to these same factors, executives were relatively confident in their security and data-driven pedigree, scoring them an average of 6.7 and 6.57 respectively. 

However, they were less sure of their carbon neutrality and ESG credentials, settling on an average score of 5.13. 

Operational efficiency key to achieving KPIs

Elsewhere, leaders were asked for their top three KPIs for their companies’ supply chains. 

Researchers discovered that three indicators emerged as clear frontrunners:

• Delivery time/cash-to-cash time cycle: 83.9%
• Customer satisfaction: 77%
• Inventory turnover and velocity: 60.9%

Gross ROI in relation to supply chain technology, carbon footprint and claims were regarded by executives as being less reflective of overall company performance. 

Almost three-quarters (73.6%) of respondents highlighted operational efficiency as the biggest obstacle to achieving their KPIs and goals. This was closely followed by organisation (71.3%), inclusive of business and IT alignment, a lack of skilled employees and internal culture. 

At the other end of the scale, less than a quarter (23%) said regulations were proving problematic to meeting objectives. 

AI a key focus for supply chain leaders

Looking to the future, it’s evident emerging technologies like AI are set to become a primary focus for supply chain leaders – if they haven’t already.

Asked which technologies will support their supply chain initiatives over the next couple of years, more than two-thirds (69%) of executives opted for AI, advanced analytics and digital twins. 

This was followed by:

• Advanced planning system including integrated business planning: 65.5%
• ERP/digital manufacturing cloud: 56.3%
• Intelligent automation: 54%

This serves as clear evidence that AI is perceived by those in positions of authority as a key enabler of innovation and growth. 

Moreover, intelligent scenario planning incorporating AI and real-time decision making was chosen as a top focus area (67.8%) for the next five years, as was L&D and attracting talent (60.9%).

Despite giving their own supply chains relatively low scores for their carbon neutrality and ESG credentials, more than half (55.2%) of the survey respondents said sustainability and reducing carbon emissions would be a top priority moving forward. 

What’s telling, however – especially given the last few years of disruption and uncertainty – is the level of confidence among supply chain leaders in relation to their future preparedness.

A significant majority (82.8%) of professionals claimed they were either ‘ready’ or ‘somewhat ready’ for the future, with an additional 5.7% going as far as to say they are ‘leading the way’.

The post CGI Survey Shows Industry Looks to AI as Supply Chain Future appeared first on AthisNews.

Start Date: December 11-12, 2024
Location: Javits Center, NYC, USA

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While Meta has not released the models yet, their reported experiments show that Chameleon achieves state-of-the-art performance in various tasks, including image captioning and visual question answering (VQA), while remaining competitive in text-only tasks.

How AI is advancing geospatial intelligence

The architecture of Chameleon can unlock new AI applications that require a deep understanding of both visual and textual information.

Early-fusion multimodal models

The popular way to create multimodal foundation models is to patch together models that have been trained for different modalities. This approach is called “late fusion,” in which the AI system receives different modalities, encodes them with separate models and then fuses the encodings for inference. While late fusion works well, it limits the ability of the models to integrate information across modalities and generate sequences of interleaved images and text. 

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Chameleon uses an “early-fusion token-based mixed-modal” architecture, which means it has been designed from the ground up to learn from an interleaved mixture of images, text, code and other modalities. Chameleon transforms images into discrete tokens, as language models do with words. It also uses a unified vocabulary that consists of text, code and image tokens. This makes it possible to apply the same transformer architecture to sequences that contain both image and text tokens. 

According to the researchers, the most similar model to Chameleon is Google Gemini, which also uses an early-fusion token-based approach. However, Gemini uses separate image decoders in the generation phase, whereas Chameleon is an end-to-end model that both processes and generates tokens.

“Chameleon’s unified token space allows it to seamlessly reason over and generate interleaved image and text sequences, without the need for modality-specific components,” the researchers write.

While early fusion is very appealing, it presents significant challenges when training and scaling the model. To overcome these challenges, the researchers employed a series of architectural modifications and training techniques. In their paper, they share the details about the different experiments and their effects on the model.

The training of Chameleon takes place in two stages, with a dataset containing 4.4 trillion tokens of text, image-text pairs, and sequences of text and images interleaved. The researchers trained a 7-billion- and 34-billion-parameter version of Chameleon on more than 5 million hours of Nvidia A100 80GB GPUs. 

Chameleon in action

According to the experiments reported in the paper, Chameleon can perform a diverse set of text-only and multimodal tasks. On visual question answering (VQA) and image captioning benchmarks, Chameleon-34B achieves state-of-the-art performance, outperforming models like Flamingo, IDEFICS and Llava-1.5.

According to the researchers, Chameleon matches the performance of other models with “much fewer in-context training examples and with smaller model sizes, in both pre-trained and fine-tuned model evaluations.”

One of the tradeoffs of multimodality is a performance drop in single-modality requests. For example, vision-language models tend to have lower performance on text-only prompts. But Chameleon remains competitive on text-only benchmarks, matching models like Mixtral 8x7B and Gemini-Pro on commonsense reasoning and reading comprehension tasks.

Interestingly, Chameleon can unlock new capabilities for mixed-modal reasoning and generation, especially when the prompts expect mixed-modal responses with text and images interleaved. Experiments with human-evaluated responses show that overall, users preferred the multimodal documents generated by Chameleon.

In the past week, both OpenAI and Google revealed new models that provide rich multimodal experiences. However, they have not released much detail on the models. If Meta continues to follow its playbook and release the weights for Chameleon, it could become an open alternative to private models.

Early fusion can also inspire new directions for research on more advanced models, especially as more modalities are added to the mix. For example, robotics startups are already experimenting with the integration of language models into robotics control systems. It will be interesting to see how early fusion can also improve robotics foundation models.

“Chameleon represents a significant step towards realizing the vision of unified foundation models capable of flexibly reasoning over and generating multimodal content,” the researchers write.

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Where is it?

Taking place on 9-10 October in Amsterdam, the event aims to gather a large global ecosystem of enterprise, big tech, start-ups, investors and academics to set the global AI agenda.

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The researchers tested the performance of GPT-4, a state-of-the-art LLM developed by OpenAI, on the task of analyzing corporate financial statements to predict future earnings growth. Remarkably, even when provided only with standardized, anonymized balance sheets, and income statements devoid of any textual context, GPT-4 was able to outperform human analysts.UnmuteAdvanced SettingsFullscreenPauseRewind 10 SecondsUp NextNewslettersSubscribeHow AI is advancing geospatial intelligence

“We find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model,” the authors write. “LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company’s future performance.”

Chain-of-thought prompts emulate human analyst reasoning

A key innovation was the use of “chain-of-thought” prompts that guided GPT-4 to emulate the analytical process of a financial analyst, identifying trends, computing ratios, and synthesizing the information to form a prediction. This enhanced version of GPT-4 achieved a 60% accuracy in predicting the direction of future earnings, notably higher than the 53-57% range of human analyst forecasts.

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“Taken together, our results suggest that LLMs may take a central role in decision-making,” the researchers conclude. They note that the LLM’s advantage likely stems from its vast knowledge base and ability to recognize patterns and business concepts, allowing it to perform intuitive reasoning even with incomplete information.

LLMs poised to transform financial analysis despite challenges

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The findings are all the more remarkable given that numerical analysis has traditionally been a challenge for language models. “One of the most challenging domains for a language model is the numerical domain, where the model needs to carry out computations, perform human-like interpretations, and make complex judgments,” said Alex Kim, one of the study’s co-authors. “While LLMs are effective at textual tasks, their understanding of numbers typically comes from the narrative context and they lack deep numerical reasoning or the flexibility of a human mind.”

Some experts caution that the “ANN” model used as a benchmark in the study may not represent the state-of-the-art in quantitative finance. “That ANN benchmark is nowhere near state of the art,” commented one practitioner on the Hacker News forum. “People didn’t stop working on this in 1989 — they realized they can make lots of money doing it and do it privately.”

Nevertheless, the ability of a general-purpose language model to match the performance of specialized ML models and exceed human experts points to the disruptive potential of LLMs in the financial domain. The authors have also created an interactive web application to showcase GPT-4’s capabilities for curious readers, though they caution that its accuracy should be independently verified.

As AI continues its rapid advance, the role of the financial analyst may be the next to be transformed. While human expertise and judgment are unlikely to be fully replaced anytime soon, powerful tools like GPT-4 could greatly augment and streamline the work of analysts, potentially reshaping the field of financial statement analysis in the years to come.

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Start Date: September 8, 9, & 13, 2024
Location: MGM Grand, Las Vegas, NV

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Topics will be discussed via roundtables, keynote speakers, presentations, exhibits and networking.

When AND Where is it?

12 – 14 August 2024 | Las Vegas, USA

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Businesses today are under constant pressure to develop high-quality software and applications rapidly, while keeping costs under control. However, per a recent column in Forbes, 33% of developer time is spent on tech debt-related tasks versus working on new products and features. These tasks are usually mundane and time-consuming for the developers assigned to them, and may cause low motivation and add to high churn rates. 

This is where technologies like Generative AI (Gen AI) can make a difference, opening up new possibilities for automating tedious coding tasks and adding capacity to development teams.

Gen AI coding led to a seismic shift in software development. It has propelled the emergence of coding assistance tools, some enhancing developer productivity with intelligent assistance, while others pioneer autonomous coding technologies. This points to a future where AI handles the bulk of coding, transitioning developers from coding tasks to strategic project oversight.

To capitalise on these advancements, Stride has introduced Stride Conductor, a novel solution that uses natural language to direct a team of LLM agents. These agents collaborate to develop, enhance, and test software in the user’s native development environment, essentially augmenting their team. Stride opted for a multi-agent approach because it allows for self-improvement through critique, better decomposition of large tasks, and the creation of inspectable, readable, and explainable code.

With Stride Conductor’s multi-agent approach, technical debt no longer becomes an expensive inevitability in most IT projects

Technical debt mitigation

With Stride Conductor’s multi-agent approach, technical debt no longer becomes an expensive inevitability in most IT projects, nor does it impede progress. Stride Conductor tackles tech debt in the background by taking over tasks such as:

Static Analysis: Stride Conductor facilitates the high volume, low complexity changes related to linting and validation, security audits, accessibility checks, and web performance optimisation.

Automated Code Testing: Stride Conductor enhances test coverage, fixes failing tests, and adds tests to pull/merge requests to ensure that new or changed code performs as expected.

Replatforming: Stride Conductor can also manage much larger tasks like upgrading language versions, migrating to new stacks, or repatterning existing code.

Case study

Stride Conductor completely changes the calculus around technical debt remediation – enabling projects delayed because of high costs and/or high risk to be delivered with confidence. In a recent project involving a public e-commerce company, Stride was tasked with fixing tens of thousands of static analysis errors in test code, which were preventing the team from upgrading their CI/CD test infrastructure.

These types of errors are generally simple to fix, but the sheer volume was overwhelming, and the project had been deprioritised accordingly.  This kind of “dirty job” is perfect for semi-autonomous AI, which can be taught about codebase rules and patterns and then apply them to novel situations.  Best of all, it’s straightforward to tell when the work is done – when the code patterns look good and the failing tests pass, businesses can move forward with confidence.   

Using Stride Conductor, the company was able to create positive ROI for fixing these static analysis errors, bringing a task estimated at two person-years down to a matter of weeks, including human approvals and oversight. Conductor also offers these advantages over other tools which claim to fix technical debt:

Force Multiplication: Unlike tools that require active operation, Stride Conductor functions like an auxiliary development team, executing tasks semi-autonomously. Human developers need only provide high-level guidance and oversight.

Seamless Interoperability: Stride Conductor integrates into a company’s existing environment, minimising process disruption and maintaining compatibility with existing platforms, stacks and best practices.

Traceability and Transparency: Stride Conductor generates code and a comprehensive chain of thought that can be inspected and reviewed. This end-to-end traceability ensures organisations maintain control and accountability in their AI-augmented development processes.

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