The gap between a ChatGPT wrapper and enterprise AI
Every week, someone pitches us on a "quick AI integration" — connect GPT to their internal data, slap a chat interface on it, ship it to users. It sounds fast and cheap. It almost never works in production.
We've spent years building systems for government agencies, hospitals, and financial institutions in Israel. These organizations can't tolerate AI that hallucinates patient data, fabricates legal precedents, or leaks confidential information to a third-party API. They need something built differently from the ground up.
Why API wrappers fail in regulated environments
When you wrap a foundation model like GPT-4 or Claude, you're relying on an external system to process your organization's data. For many enterprises, this creates immediate problems:
Data sovereignty. Healthcare records, legal case files, and government documents often can't leave your infrastructure. Sending them to an external API — even encrypted — may violate HIPAA, GDPR, or Israeli privacy regulations.
Unpredictable output. Foundation models generate different responses to the same prompt. In a customer service chatbot, this is fine. In a system advising doctors on medication interactions, it's dangerous. You need deterministic workflows around probabilistic models.
No task completion. A chat model answers questions. It doesn't log into your CRM, pull the relevant case file, draft a response, route it for approval, and update the status. Enterprise work requires systems that complete tasks, not just discuss them.
What "building AI properly" actually means
At Globalbit, we build what the industry calls agentic AI systems. In plain terms: AI that takes action within your existing business infrastructure, under your security and compliance rules.
The architecture has four layers:
Task decomposition
A user request like "prepare the quarterly compliance report" gets broken into concrete steps: pull data from the finance system, check against regulatory requirements, generate sections, flag anomalies for human review. Each step has a defined input, expected output, and failure handler.
Specialized agents
Instead of one model doing everything, we build focused agents. A data retrieval agent talks to databases. A document generation agent creates structured output. A compliance agent validates against regulatory rules. Each operates independently and communicates through structured interfaces.
Secure integrations
Agents connect to existing enterprise systems — ERP, CRM, document management, case tracking — through APIs that enforce permission models. An agent can read what the requesting user is authorized to see, nothing more.
Human checkpoints
For high-stakes decisions, we build explicit review steps. The AI prepares the work, a human approves or modifies it. This isn't a limitation — it's a design choice that builds organizational trust in the system.
Two paths we see companies take
Path A: Start with a wrapper, hit walls. A company connects GPT to their knowledge base, demos it internally, excitement builds. Then security reviews it and flags data exposure risks. Legal finds compliance gaps. Engineering discovers it can't reliably connect to internal systems. The project stalls.
We get these calls regularly. Sometimes we can salvage the work. Sometimes we start from scratch.
Path B: Design for production from day one. Requirements gathering, domain analysis, security architecture, then implementation. It takes longer upfront (3-6 months vs. 3-6 weeks), but the system actually ships and stays running.
The cost comparison is misleading
A ChatGPT Enterprise subscription costs a few thousand dollars a month. A custom agentic system costs six figures to build. The price difference looks enormous until you factor in:
- The subscription system can't complete tasks in your infrastructure
- You'll spend more on workarounds than on building it right
- The custom system reduces headcount burden on repeatable workflows
- Compliance failures cost more than development
One legal client estimated that Psika.ai saves their firm approximately 2,000 billable hours per year in research time. At their rate structure, that's a seven-figure return on a six-figure investment.
Frequently asked questions
Can we start with a wrapper and migrate to a custom system later? You can, but there's limited reusable work. The prompt engineering transfers, but the architecture, integrations, and security infrastructure need to be built from ground up. It's usually cheaper to start with proper architecture.
Do we need our own ML engineers? During development, no — that's our job. For ongoing operations, you need someone who understands the system architecture, but they don't need deep ML expertise. We design systems that your existing engineering team can maintain.
How do you handle model updates? Our framework is model-agnostic. When a better model becomes available (or a current one's pricing changes), we can swap foundation models without rebuilding the agent architecture or integrations.
If your organization is evaluating AI beyond chatbots — systems that actually do work within your infrastructure — we should talk about what that looks like for your specific situation.