The latest from faster.

We tried to fix our timesheets and accidentally built a context graph. Here's what we learned about why operational discipline matters more than architecture.

Salesforce spent $8B on Informatica because AI agents need unified context. Here's how to build the data architecture that makes Agentforce actually work in production.

35% of companies are already using agentic AI, with another 44% planning deployment soon. But few have restructured how they deliver projects. That gap is where implementation timelines extend and where fixed-price engagements turn into scope management problems.

We built two Agentforce agents for TASC Outsourcing that handled 5,100+ sales interactions and improved their email response rates from 0.33% to 1.93%.

Developer onboarding costs $21,600 annually per 20-person team. See how ExoPair compresses 3-5 days of onboarding into day-one production deployments.

Developers hate updating tickets. Managers hate chasing updates. We use Exo, our internal AI agent, to make the whole problem disappear.

ExoHelp turns 3-5 day Salesforce requirements into 30-minute sprints. See how AI analysis eliminates clarification cycles and delivers developer-ready tickets instantly.

The best engineers used to be consultants who built foundational software. Then consulting became uncool. Now Forward Deployed Engineers are making it desirable again by solving real business problems instead of hiding behind JIRA tickets.

Transform Salesforce delivery speed with AI-first methodology. See how teams deliver "humanly impossible" results in days, not months.

Two leaders who scaled Gojek to 7M daily orders share why most enterprise AI fails and what actually works beyond demos. Lessons from building realfast.

Salesforce Agentforce flex credits changes everything. From $2 per conversation to 10¢ per action - pay only for real work done. Game changer.

The AI Effort Paradox: Why trying harder costs more

Discover why single prompt testing fails in production. Learn how batch testing revealed a 3x performance gap between AI models and the simple technique that boosted Agentforce speed without sacrificing quality.

AI is transforming IT services' economic model by decoupling revenue from headcount

We were drowning in intro calls. So, we brought in an AI interviewer to do the talking for us—literally. Here’s how it’s helping us scale faster, stay human, and still make great hires without burning out.

On setting up reporting for Agentforce and why reporting is the heart of AI reliability

Best practices and tricks of the trade that ensure your Agentforce agents operate accurately and consistently, even under complex conditions.

New research reveals that simple AI agents fail at enterprise tasks over a third of the time. And the solution isn't just better models.

Let's look at how Agentforce can do the heavy lifting so time-starved recruiters can focus on building relationships and making successful placements.

We ran into some unexpected quirks while testing Salesforce agents. Here's what you need to know before starting.

What if, instead of building better dashboards, we changed how humans interface with data entirely?

While the base pricing for Agentforce seems straightforward, the real cost structure involves understanding conversations, Einstein requests, and data credits – each with its own nuances and optimization opportunities.

Salesforce is a powerful tool, but traditional implementations often lead to data chaos within months. An AI-first Salesforce implementation changes that.

Agents that can do work asynchronously will be crucial to make GenAI scalable and viable for enterprise workloads.

realfast Vayu is an orbital shift in how you deliver Salesforce.

A problem evaluation framework we've developed over the past year for both GenAI founders and investors.

Faster implementations are the Hero USP for AI-based tooling, not cost savings.

You want your agents to be just smart enough to do the task consistently and reliably, and not any smarter than that.

It actually helps to be less ambitious when starting out with AI transformation and take small, meaningful steps toward increasing velocity and throughput.

Foundational AI and Applied AI operate on entirely different planes. So, the approach you take to build an applied AI company will also be very different.

We're talking 12-16x faster test creation, with 95%+ code coverage.

For typical enterprise workloads, LLMs are raw material. You will need software engineering to turn them into a scalable resource.

Evaluating an AI tool's performance in real-world workflows is inherently more complex than benchmarking the models themselves on established datasets.

If you think slapping together a basic interface and calling an API is all it takes, then you're clueless about what's really involved.

The Industrial Revolution for IT Services is here, and we are leading the charge.