Most teams get handed AI tools with no real guidance. They pick the wrong models, spend a fortune on tokens, and still get mediocre work back. Pulse trains your people to use AI properly, then builds it into the workflows that run your business.
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We train your team to use AI well, and we build enterprise AI into the workflows that run your business. That’s it. No jargon, no pilots that go nowhere.
Most teams get Claude or ChatGPT and are left to figure it out on their own. They don’t know which model fits the task, how to prompt it, or when to stop, so they spend a fortune on tokens and still get weak work back. We train your people to actually be good at this. They learn which Claude model to use and when, how to build and reuse Claude Skills and Projects, how to turn the things that work into shared workflows the rest of the team can run, and how to set guardrails so AI gets used safely and consistently.
When AI needs to run inside real operations, we build it. We design and deploy it into your real business workflows, connected to your internal knowledge and properly supported after launch. As part of the Claude (Anthropic) partner program, we build on the same tools your team is trained on.
No vague strategy decks. Here is what is actually included in each side of the work.
Every client engagement is led by senior operators with experience from Meta and AMD. The team stays hands-on through discovery, deployment, and adoption so implementation quality does not get delegated away from strategy.
Principal Engineer
Brian Yao leads technical architecture and AI systems at Pulse, bringing senior engineering experience from Meta into AI training and enterprise implementation.
Product Lead
Usman Zia leads product strategy and client delivery from discovery through rollout, combining an AMD engineering background with practical AI adoption planning.
Client Strategy Lead
Gurinder Garcha leads client strategy and translates business problems into practical AI deployment plans, backed by systems engineering experience from AMD.
30 minutes. No commitment. We learn about your business and show you where AI can create measurable workflow ROI.
Straight answers about AI training, enterprise AI, and how Pulse works.
Two things. The first is AI training. We teach your team how to actually use AI well: which model to pick, how to prompt it, which workflows are worth sharing across the company, and how to keep it safe. The second is enterprise AI. When AI needs to run inside real operations, we build and deploy it into your workflows. That is the whole offering, nothing else bolted on.
Because most people get handed Claude or ChatGPT with no real guidance. They do not know which model fits the task, how to prompt it, or when to stop, so they spend a fortune on tokens and get mediocre work back. Training fixes that. Your team gets more out of AI while spending far less doing it.
AI training makes your people good at using AI day to day. Enterprise AI is us building AI directly into your business workflows, connected to your internal knowledge and supported well after launch. Most companies start with training and add enterprise AI once the team knows what good looks like.
Primarily Anthropic Claude, plus OpenAI where it fits. We are part of the Claude (Anthropic) partner program, so your team is trained on the same tools your workflows are built on, configured for how you actually work.
Access is not the hard part. Most teams have already tried the tools. The hard part is knowing which model to use, how to prompt it, which workflows are worth standardizing, and where the guardrails go. That is where the wasted spend and weak output come from, and that is exactly what training removes.
In the right work, teams typically see 30 to 70% less time on repetitive drafting and review, two to five times faster turnaround on repeatable work, and 20 to 40% more capacity without new headcount. They also see a real drop in wasted token spend once the team stops overpaying for output they do not need.
Every engagement is led by senior people with backgrounds from AMD, Meta, and Microsoft, with real engineering, systems, and product delivery experience. The work does not get handed off to juniors.
Training can start fast and show value within days. Enterprise AI depends on the workflow and your systems, but the focus is always getting to something real and used quickly, not a long strategy cycle.
No. The goal is a faster, more consistent team that handles more high-value work and wastes less time on repetitive drafting, review, and information handling.