AI Engine

Large language models (LLMs) are prone to “hallucination”—the confident generation of false or inaccurate information. According to the New York Times, OpenAI’s latest system (o3) hallucinated 33% of the time on a benchmark test involving public figures. These errors can include fake financial data or made-up product details, posing serious risks to trust and reliability.

Even without hallucinating, LLMs like ChatGPT can produce inconsistent or self-contradictory answers. Studies show a 17.7% contradiction rate in open-domain text, often due to conflicting training data. This can lead to different responses for the same query, undermining trust—especially in business settings where an AI might advise one compliance policy, then contradict it later.

General LLMs are trained on internet-scale data that may be outdated, low-quality, or biased, with no built-in assurance of source reliability. This can lead to inaccurate or misleading outputs that reflect misinformation or contradictions in the training data. Unlike business intelligence systems based on verified sources, LLMs may produce unvetted content, risking poor decisions and reduced accuracy.

Due to data quality issues, current LLMs cannot be fully trusted for high-stakes enterprise use without rigorous validation. Hallucinations and inconsistencies demand manual oversight or secondary review, reducing the efficiency gains organizations expect. As a result, many firms have realized that deploying a general-purpose LLM “as-is” introduces an unacceptably high level of risk.

LLMs produce free-form text that varies by response, making structured output unreliable. The same task might yield a sentence, a list, or an unexpected format, forcing developers to rely on complex prompts and post-processing. Our teams saw only ~36% reliability using prompt engineering alone, often breaking automated workflows and requiring constant fixes to handle formatting inconsistencies.

As corporate data, tools, or user behavior change, model responses can drift, reducing accuracy. Prompt setups often require frequent updates, and provider API changes can break integrations. This creates a high maintenance burden—ongoing monitoring, retraining, and prompt adjustments are essential. Treating LLMs as “set and forget” is a costly mistake.

Enterprises often face integration gaps—connecting LLMs to systems like ERP or CRM requires custom work, not plug-and-play solutions. Issues like API limits, security, and input formatting demand additional code and infrastructure. Tools for prompt versioning, compliance, and monitoring remain immature, forcing many teams to build their own. As a result, deploying LLMs often comes with high complexity and hidden implementation costs.

Deploying a general-purpose LLM in an enterprise setting comes with significant engineering overhead. The inconsistent outputs and need for constant tuning erode the efficiency gains, while tooling gaps make it hard to incorporate AI into existing workflows seamlessly. These technical friction points contribute to the maintenance burden and total cost of ownership of LLM projects often exceeding initial plans.

LLM costs are often unpredictable and difficult to control. Usage-based pricing means expenses rise with adoption, and popular AI features can trigger unexpected cost spikes. Hosting large models also requires expensive infrastructure. As AI gets embedded into daily workflows, per-query costs can add up quickly—sometimes with diminishing returns. Budgeting is challenging, as actual usage often exceeds estimates, and pricing models may shift. This volatility can turn a promising AI initiative into a financial burden.

Relying on external AI vendors introduces strategic risks. Outages, policy changes, or vendor instability can disrupt critical services, while switching providers may require major rework. Trusting third parties with proprietary data also raises compliance concerns. With evolving markets and shifting APIs, many organizations now prioritize solutions that offer greater control and reduce dependency on any single vendor.