Custom AI Software Development

AI can unlock new possibilities you might not be able to achieve with traditional software alone, such as:

• Process automation: Freeing up time and reducing costs by simplifying repetitive knowledge work or complex analysis through AI.
• Personalization at scale: Tailoring user experiences or product recommendations in real time.
• Interpreting unstructured data: Summarizing, categorizing, or extracting insights from large text or image sets.

While AI introduces opportunities for innovation, it brings new challenges as well. As experts, we can help address your concerns.

Whether AI is the heart of your product or a feature within a broader system, we integrate robust, proven software engineering from degreed professionals who are based in the United States with advanced AI capabilities to maximize your ROI.

Our projects start with consulting work from a business, user, and technology perspective. We anchor every AI solution in your business objectives, setting measurable success criteria and building with flexible, modular architectures. This way, when a new model or technique emerges, you can integrate it without a full rewrite. Our iterative approach and continuous testing help you adapt to rapid AI advancements while keeping your core system stable.



We ensure AI solutions are right for you by:

• Identifying the business drivers for new value creation and/or cost savings
• Defining risks, constraints, and technical requirements for the system architecture and details including data management, model development/training, MLOps, processing, and results
• Driving alignment through design concepts
• De-risking implementation through proving out and tuning AI technology components early in the project

Working with customers throughout the development process and into production monitoring to ensure AI components are working as expected or even improving over time

Atomic developers have Computer Science degrees and can responsibly develop and integrate AI into production systems.

Data security is foundational. We thoroughly vet hosting providers and models to ensure they meet robust data protection standards and respect your organization’s policies. If needed, we can deploy self-hosted or on-prem solutions to keep your data entirely under your organization’s control.

Throughout our process, we partner with you to ensure every architectural choice aligns with your security goals.We also stay up-to-speed with the fast-moving AI landscape to help you make informed decisions based on your privacy and security needs.

Common considerations include:

• Data privacy
• Models privacy / exclusivity
• RAG
• Cloud vs. on-prem hosting

We set clear quality and performance criteria—like accuracy, cost, and response times—and maintain curated test sets to evaluate new or updated models. Because large-language models (LLMs) can shift behavior from one release to the next, we continuously measure improvements (and watch for regressions) in line with your objectives. This allows us to adapt quickly if a newer model offers better capabilities or cost-efficiencies—without sacrificing reliability.


Atomic Object teams design hybrid AI architectures—sometimes called “agentic” approaches—that tightly constrain how and when an LLM can operate. We rely on:

• Classical software boundaries: Traditional code paths that handle critical logic, data validation, and policy enforcement.
• Structured prompts & guardrails: Techniques like instruction tuning, role-based prompt templates, and vendor-supplied guardrails.
• Continuous testing: Automated and real-world testing that flags undesired behaviors or potential exploits.

By carefully orchestrating these elements, we can retain the creative and adaptive benefits of AI while minimizing unwanted outputs.


Atomic Object consulting teams combine classical automated testing (just as we do with traditional software) and real-world usage data. Tools such as LangSmith or LangFuse help capture and review actual application examples, which we then curate and include in automated test suites.

As models evolve or prompts change, we confirm functionality hasn’t regressed by comparing results to our growing library of known-good outputs.


No. Atomic Object teams avoid providers or configurations that ingest your data into public or shared models. If we use a managed service, we select offerings that explicitly commit—contractually and technically—to isolating and not repurposing your data. If we host the model ourselves, all your data remains strictly private.

Yes. Your project team treats your data and any customized models as confidential and proprietary. If you pursue fine-tuning or custom model training, the resultant model is your asset. We store and serve it in ways that align with your organization’s security requirements.

Most AI workloads benefit from the elasticity and GPU-optimized hardware in the cloud, minimizing up-front infrastructure costs and scaling seamlessly. However, for clients with strict data residency or compliance mandates, on-premises or private-cloud options may be preferable.

We help you weigh cost, performance, and security trade-offs to make the best decision for your business.


Cloud-based inference offers robust performance and easy scaling but comes with ongoing compute costs. Edge-based inference (such as on smartphones or embedded devices) can reduce latency, enable offline functionality, and save on cloud costs.

Your Atomic Object team will look at your usage patterns, performance requirements, and infrastructure constraints to decide on the ideal approach.


We’re committed to continuous learning and early adoption of emerging technologies. Each project team is handpicked based on domain expertise, technology familiarity, and synergy with your specific goals. Whether it’s cutting-edge AI techniques or mature development practices, we match you with the people who can deliver.

Access to talent is essential, and Atomic Object’s offices are strategically located in U.S. regions with rich access to top talent. Our Career Accelerator, an accelerated professional development program for newly graduated computer science majors, helps us build our team of world-class software developers. Fair pay, strong benefits, in-person collaboration, continuous learning, ownership opportunities, and a sense of belonging help recruit top senior practitioners and help us grow an amazing team. Our retention of employees outperforms the market because of the added benefits of team cohesion and social bonds.

We believe in an open, transparent communication approach throughout the AI software development process.

Every AI project at Atomic has a team lead known as a Delivery Lead. This person is your consistent point of contact. Delivery Leads are tasked with three big responsibilities:

• Equipping the team with everything they need to make forward progress
• Making sure that your priorities are our priorities, and
• Keeping you informed about the project’s status.

Regular updates will be provided by your Delivery Lead, keeping you informed about every step and decision throughout the project.

At Atomic, we invest in an in-house technical recruiter who finds us the best U.S. talent. Our interview process, honed over more than two decades, helps us bring on not only exceptionally talented technical practitioners but also team members whose values align well with ours. These practitioners are adept at navigating consulting relationships and finding win-win situations for both clients and teams.

Atomic is carefully run by leaders who make decisions that prioritize the long-term. This means not only are your projects staffed by full-time employees for success, but the employment offering is designed to retain smart, talented consultants. Our turnover rate is only 8.8%, well below industry standards, which reflects our employees' high engagement and satisfaction.

This dedicated and engaged set of team members translates to proven client value; over the last decade, 38% of our revenue has come from satisfied clients coming back to Atomic for additional projects.

When well-architected, AI integrations can be just as maintainable as conventional applications—sometimes more so.

By separating AI “prompts” or model configurations from core business logic, updates can often be made without restructuring your entire system. We also use monitoring tools to help you track performance, manage model drift, and decide when it’s time for a model upgrade.