Strategic tech roadmaps, team structuring, system architecture, and hiring support. Hands-on guidance in both advisory and technical roles
(15% allocation)

We specialize in adapting and fine-tuning ensembles of pretrained models for efficient, cost-effective deployment—ideal for startups with limited model-training budgets or well-funded ventures that need to move quickly. Our approach focuses on leveraging existing AI/ML technologies to accelerate innovation while minimizing computational overhead.

We’ve been doing this since 2005—long before "data science" was a buzzword, before big data, and before large language models dominated the field. Over the years, we’ve worked across the full spectrum of AI/ML technologies, from early robust statistical inference frameworks to today’s state-of-the-art architectures. This experience gives us the practical insight to identify the best approach for each challenge, rather than relying on one-size-fits-all solutions.

We deliver scalable, high-value AI solutions that drive efficiency and long-term impact, whether in startups, enterprise environments, or government applications.

We choose solutions based on best fit to the problem, not vendor hype. We’ve built and scaled systems using PostgreSQL, MongoDB, Redis, MinIO, Kafka, and Neo4j, among others—each selected for its strategic advantages in the given context. These are supported by robust architectures spanning batch workflows, real-time streaming, and serverless compute environments (e.g., RunPod), using containerized or hosted services as appropriate. Our team members have architected both consumer-facing platforms (with data privacy implications) and DoD systems (with strong security requirements), bringing a practical understanding of risk and compliance to every engagement.

Our systems are secure, economical, and production-grade, with a strong commitment to CI/CD, automated QA, and MLOps best practices. When possible, we emphasize technologies that align with the skills of the team—for example, using Python-native development patterns with Faust to reduce ramp-up time and long-term friction. We prioritize maintainability, extensibility, and thoughtful technical debt discipline to ensure systems are built to scale and evolve predictably.

We have deep expertise in Generative AI, particularly in the use of Diffusion Models and Large Language Models (LLMs). These advanced models enable high-quality generation of text and images across industries such as marketing, design, and creative media.

Our strength lies in adapting, guiding, and fine-tuning these models for domain-specific use—ensuring outputs are context-aware, brand-consistent, and free from the embarrassing uncanny valley effects that often result from naïve deployments. We combine practical model selection with cost-efficient orchestration of GPU-enabled compute services, enabling startups and enterprises to deploy generative systems that are not only impressive, but also sustainable and production-ready.

We have a proven track record of crafting winning DARPA proposals, with one program successfully transitioning to DoD operations. Our expertise extends to client presentations, white papers, and detailed project planning—both at the project level (Agile, Kanban, and hybrid methodologies) and at the architecture level, developing long-term roadmaps that successfully incorporate and navigate the inherent uncertainty of R&D. We’re also available for patent writing services, leveraging our deep technical background to craft strong, defensible IP.

Expert in radiolocation and signals intelligence, backed by multiple awarded patents and publications dating to 2005, and multiple fielded DoD systems.  Areas of expertise include: graph-based learning, computer vision, digital signal processing, data fusion and clustering, nonconvex optimization, array signal processing, compressed sensing, robust and nonlinear optimization.