What's Missing in AI For Enterprise Search? Domain Knowledge and Company Language

We've all seen the headlines – MIT report: 95% of generative AI pilots at companies are failing.

Why are these pilots failing? According to Aditya Challapally, the lead author of the report they're failing due to "Not the quality of the AI models, but the 'learning gap' for both tools and organizations".

We here at DaaX agree with Aditya on the learning gap. However, we believe there is one more reason why enterprise AI pilots / applications fail – LLM hallucinations. Sajjad has written extensively about LLM hallucinations, see here and here, and I'm not going to cover hallucinations in this article.

Here, I'm going to focus on the learning gap. For us, the learning gap means LLMs lack of understanding in:

1. Domain Knowledge – the language of your industry
2. Company Language – the language unique to your organization

Domain Knowledge: The Language of the Industry

Every industry has its own language — a set of terms, abbreviations, relationships, and concepts that carry precise meaning for professionals in that domain.

For example in the Oil & Gas industry, terms like completion, mud, perforating, and casing refer to well-defined concepts with specific relationships and meaning.

Or in Healthcare, terms like admission, encounter, and visit are not interchangeable. Each has its own documentation requirements, clinical workflow, billing codes, and reporting implications.

Why Domain Knowledge Breaks Natural Language Systems

Generic AI models trained on general-purpose text almost never handle domain language reliably without additional context. There are several predictable failure modes:

1. Misinterpretation of industry terms - Ask a generic AI for "completion metrics" and it may think you're talking about task completion—unless it knows you're in oil & gas.
2. Incorrect assumptions about relationships - In retail, SKU, variant, and product family have specific relationships. In healthcare, visit, encounter, and admission are not interchangeable.
3. Missing nuances of meaning - Industry terms often have multiple sub-definitions depending on context.
4. LLM hallucinations under domain pressure - When unsure, models can "invent" meaning—dangerous in regulated fields.

Ontologies: How Industries Encode Shared Knowledge

Many industries formalize their shared vocabulary into ontologies or taxonomies. For example:

• OSDU Ontology (Oil & Gas)
• FIBO — Financial Industry Business Ontology (Finance)
• OBO (Biological & Biomedical)

These frameworks define:

• Entities
• Relationships
• Attributes
• Domain-specific terminology
• Business concepts

When properly applied, they act as the "dictionary and grammar" of an industry. Ontologies are fundamental to DaaX 3D graphs.

Company Language: The Vocabulary Unique to Your Organization

Even if two companies operate in the same industry and follow the same ontology, they still speak differently.

Every organization develops its own linguistic universe—its own internal dialect.

Company Language Is Not Industry Language

Company language emerges from:

• Internal processes
• Historical naming conventions
• Proprietary metrics
• Branding
• Etc.

This vocabulary rarely appears in public documentation but is used constantly inside the business. And company language isn't limited to just words – TLAs (three-letter acronyms) are included!

Natural-language search fails when it doesn't understand these internal definitions.

Examples of Company Language

1. "Loyal Customer" Means Something Different Everywhere

• Company A: 3+ purchases in 90 days
• Company B: subscription > 12 months
• Company C: enrolled in rewards program

Now imagine a user asks: "Show me our loyal customers for Q4"

Unless your system knows your company's exact definition of "loyal," the results will be wrong.

2. "Year" Can Mean Fiscal Year, Not Calendar Year

In many organizations:

• "year" → April 1 to March 31
• "quarter" → internally defined periods
• "end of month" → based on processing windows

So when someone asks: "How many teapots did we sell last year?"

A correct answer depends entirely on your internal definition of the word "year." How can an LLM know your internal definition?

3. Internal Shortcodes, Nicknames & Abbreviations

Every team uses shorthand:

• "Ops"
• "Prem SKU"
• "Legacy"
• "High-touch customers"
• "Wave 2 region"

These terms often mean something very specific internally—but nothing to an outsider or generic AI.

Why Company Language Breaks Natural Language Systems

Without this layer of context, AI systems:

• misinterpret internal metrics
• apply the wrong filters
• calculate KPIs incorrectly
• misunderstand business intent
• produce SQL that does not match business definitions

This isn't a model weakness—it's a context weakness.

Domain Knowledge + Company Language = Accurate Enterprise Search

When enterprise search works, it's because the system understands both:

1. Industry Meaning (Domain Knowledge)

The shared vocabulary of your field.

2. Company Meaning (Company Language)

The unique vocabulary, rules, and metrics used inside your organization.

Accuracy depends on aligning both layers.

Without them:

• "loyal customer" is interpreted incorrectly
• "year" uses the wrong date range
• "active user" uses the wrong calculation
• "premium product" is misclassified
• Queries silently produce wrong results

With them:

• Answers match real business logic
• Queries return trusted results
• Teams can ask questions naturally
• AI becomes an extension of the company's knowledge

How Does DaaX Handle Domain Knowledge and Company Language?

Think of our DaaX Neuro-Symbolic AI Engine like the Sony PlayStation – plug in a game and go. Our engine supports pluggable domain ontologies and company language (we call it term dictionary). Give it an industry-standard ontology, such as OSDU for oil & gas, or an ontology you have developed yourself. And give it your term dictionary. Then go and get answers to your natural language queries.

Following our self-service philosophy, you can setup our Agents yourself with this info. Our DaaX engine will ingest the info you give it and use it to help the LLM understand your query and to find the best answers possible from your enterprise data.

Conclusion

AI systems do not fail because they misunderstand language. They fail because they misunderstand your language. The DaaX Neuro-Symbolic AI Engine achieves accuracy through support for pluggable Ontologies and Term Dictionaries, enabling it to interpret natural-language questions based on the real context of your business rather than generic assumptions.