Presh Marketing Solutions

Presh Marketing Solutions AI, implemented. For the IT channel. We architect and ship the agentic systems that separate the companies who lead this era from the ones who watch it.

Mohammad Al-Mousa joins PRESHai as a Forward Deployed Engineer with experience in production engineering, workflow autom...
09/22/2026

Mohammad Al-Mousa joins PRESHai as a Forward Deployed Engineer with experience in production engineering, workflow automation, and client-facing problem-solving. At Epic, he supported live systems, built automation tools, and helped modernize enterprise workflows. He is also expanding his knowledge of systems, AI, and software engineering through Georgia Tech’s OMSCS program.

At PRESHai, Mohammad works directly with clients to turn complex workflows into clear technical requirements and deploy AI solutions that address real problems. Outside of work, he enjoys traveling and visits at least one historical site on every trip.

Here's to turning complex workflows into AI that is practical, trusted, and ready for the real world.

Meet Jeff Monsalve, our new Forward Deployed Engineer.Jeff joins PRESHai with deep experience across AI, data, and infra...
09/17/2026

Meet Jeff Monsalve, our new Forward Deployed Engineer.

Jeff joins PRESHai with deep experience across AI, data, and infrastructure. His background spans Economics, Philosophy, and Mathematics before he transitioned into AI engineering, and along the way he has worked across data science, machine learning infrastructure, data engineering, and OpenShift/Kubernetes engineering. That combination gives him both a broad problem-solving perspective and the technical depth to build reliable, scalable AI systems.

At PRESHai, Jeff applies that experience across AI, data, and infrastructure to help develop and improve our projects. His work includes turning ideas into practical solutions, supporting the systems and architecture behind them, and collaborating with the team to build scalable, reliable products.

Outside of work, Jeff is fascinated by AI's potential to help explore what philosopher David Chalmers calls the "hard problem of consciousness": how physical processes in the brain create our subjective experiences. He is also a soccer fan and player who tries to attend a local match whenever he travels, as a way to connect with the culture of a new destination.

We’re glad to have you as part of the team, Jeff. Welcome aboard.

Will Rogers joins PRESHai as an AI Solutions Architect, bringing experience across federal cybersecurity, data engineeri...
09/14/2026

Will Rogers joins PRESHai as an AI Solutions Architect, bringing experience across federal cybersecurity, data engineering, full-stack development, AI and LLM product development, machine learning, data analytics, and product management.

His background in the natural sciences, particularly physics and chemistry, adds another dimension to his technical work. His experience in Forward Deployed Engineering has also put him directly alongside customers, where he works to understand the problem, design the architecture, and stay involved through development and implementation.

At PRESHai, Will turns complex business requirements into technical plans, architectures, prototypes, and production solutions. He works across AI workflow design, software development, data analysis, validation, and customer-facing technical strategy.

What interests Will most about AI is building systems that can work across workflows, tools, and data rather than stopping at a demonstration. He also continues to pursue personal research in AI and machine learning, combining his interests in physics and chemistry with his cybersecurity background.

We’re glad to have Will on the PRESHai team.

AI model comparisons focus on intelligence, speed, and cost. They often overlook the system that determines how the mode...
09/08/2026

AI model comparisons focus on intelligence, speed, and cost. They often overlook the system that determines how the model operates.

That system is the agent harness. It controls the tools the agent can use, the context it receives, the systems and data it can access, how its work is verified, and what evidence is recorded.

Two platforms can use the same model and produce very different results. The agent harness determines whether that model can perform reliably in real workflows.

When AI supports a customer-facing or revenue-impacting workflow, a failure can quickly become an operational problem.Do...
09/04/2026

When AI supports a customer-facing or revenue-impacting workflow, a failure can quickly become an operational problem.

Document the fallback before you need it. Make sure the team knows it. Test it under real conditions.
What workflow would you put to the test first?

08/28/2026

Local agents can be useful, but long-running work becomes difficult to scale when closing a computer can end the session. Moving the workflow into a controlled cloud environment gives the agent persistent context, a file system, an ex*****on sandbox, and access to approved tools.

08/27/2026

The biggest risk in the AI data-center buildout may not be circular capital. It may be where the credit risk lands.

At the PORTS-Pike campus, SB Energy will build, own, and operate the data center. OpenAI is the tenant. NVIDIA is providing conditional residual-value guarantees tied to the initial buildout and taking on a form of infrastructure exposure that goes well beyond selling chips.

We look past the headline number and ask the more useful question: If the assumptions fail, who is responsible for the downside?

08/26/2026

AI hasn’t cured cancer. But personalized cancer-vaccine research is making measurable progress.

The more useful question is where computational tools and AI (when it is actually part of the workflow) can help researchers handle the patient-specific complexity behind an individualized treatment.

08/25/2026

Three AI stories that sound unrelated reveal the same practical challenge.

Personalized cancer-vaccine research raises questions about what AI contributed and how to expand access without overstating the science. Cursor Origin raises questions about the context, tools, permissions, and rollback paths cloud agents need. NVIDIA’s infrastructure strategy raises questions about who carries the financial and community risk behind the data-center buildout.

Episode 10 of Model Behavior connects all three without flattening their differences. The useful takeaway is to look beyond the model and examine the full system around it.

08/17/2026

The release decision for a high-capability model is not just a product decision. It is a readiness decision.

We consider the value of testing, staged access, and giving security teams time to prepare before broader release.

How should organizations balance speed with preparedness?

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