In this month’s session, Roland and Mark explored why organizations are moving to Hatz.Ai—a secure platform that centralizes access to leading language models like ChatGPT, Claude, and Grok. We covered real security risks, how to pick the right model for the job, and practical workflows teams are already using to get results. Below is a concise recap with key takeaways.
Session highlights
- Benefits of Hatz.Ai
- Hatz.Ai provides secure, centralized access to major LLMs, so teams can use the best model for each task without juggling multiple tools or risking data exposure.
- Real-world security wake-up call
- An MSP uncovered significant AI usage at a company despite leadership believing none existed—underscoring the need for visibility, governance, and safe-by-design AI adoption.
- Data security and Impress
- We discussed the role of Impress in the security posture: enforcing safeguards, managing access, and ensuring sensitive data remains protected within approved workflows.
- Choosing the right model: AI Model Selector
- The Hatz.Ai model selector offers 50+ models, each with strengths across reasoning, speed, cost, and context length—helping teams match tasks (summarization, RAG, creative gen) to the optimal LLM.
- Consolidating platforms to cut cost and friction
- Unifying AI access in one place eliminates multiple subscriptions, simplifies billing and compliance, and improves adoption through a consistent user experience.
- Integrations and the role of human-in-the-loop
- When integrating projects (e.g., from Claude), there are still boundaries to what AI can automate. Clear prompts, robust context, and manual review remain essential for quality.
- Construction RFP response agent
- A practical example from the Hatz.Ai community: a workflow that accelerates construction RFP responses with structured prompts, document intake, and reusable templates.
- Use anywhere, collaborate easily
- Hatz.Ai works across devices—phone, tablet, desktop—and supports private and community workflows you can create, share, and iterate on with your team.
- Understanding hallucinations
- We revisited AI “hallucinations” and how to prevent them with better retrieval, grounded context, citations, and review steps in production workflows.
- Resources and two key flows
- Two core motions: building repeatable workflows and enabling teams with resources—videos, docs, community—to self-serve and scale responsibly.
- Why adopt AI now
- Organizations that embed AI into everyday processes gain efficiency and a competitive edge. We’ll host monthly sessions focused on specific verticals and use cases.
Pricing and credits recap (from Q&A)
- Plans are credit-based to reflect compute usage:
- Solo: 30,000 credits at $135/month.
- Team Starter (most popular): $340/month for the organization with 100,000 credits.
- Higher tiers are available as needs grow.
- Credit consumption varies by task and model size; larger models use more credits. If you hit your allotment, the platform keeps working with lighter models—no hard shutoff.
- https://www.impresscomputers.com/hatz-ai/
Key takeaways
- Secure by design: Centralized access plus governance prevents shadow AI and protects sensitive data.
- Right model, right job: A selector of 50+ LLMs lets you optimize for quality, speed, and cost.
- Workflows drive ROI: Community and private workflows turn AI from experiments into repeatable outcomes.
- Human-in-the-loop matters: Guardrails, retrieval, and review minimize hallucinations.
- Practical pricing: Credits map to compute; you keep working even if you exhaust your monthly allotment.
Call to action
- Watch for the session recording and deck in your inbox.
- Join next month’s vertical-focused session.
- Want help choosing the best model or building a workflow? Reach out, and we’ll tailor recommendations to your use case.
