By Martice Nicks, III, Co-Founder and Chief Technology Officer at Danti
In the world of data intelligence, we’ve long relied on structured systems, rule-based logic, and lexical search to organize and retrieve information. These traditional methods formed the backbone of many “knowledge engines” and enterprise data platforms. They were reliable, predictable, and, to some degree, explainable. But they were also rigid, brittle, and increasingly unscalable as the velocity and complexity of data surged.
Today, generative AI and large language models (LLMs) promise a new frontier. One where systems can understand context, disambiguate meaning, and surface relevant information even when users don’t know the perfect syntax or keywords to search for.
The Shortcomings of Traditional Systems
For decades, enterprise search and decision-support systems relied on lexical search. If you typed the exact word or phrase the system expected, you’d find your answer. But if you missed the mark, even slightly, you could easily come up empty. Advancements in fuzzy logic improved the situation, but didn’t solve the underpinning issue of keyword constraints.
Behind the scenes, these systems were powered by structured databases, ingestion pipelines, and inference engines. Knowledge was encoded manually through ontologies and knowledge graphs, with additional help from rule-based extraction and logic systems. Tools like Lucene, SPARQL, and PostGIS became foundational technologies. These showed we can address the problem of semantic based searching, however the engineering required to evolve and maintain these systems came at a high cost with veracious and variable data sets.
The challenge wasn’t that these tools didn’t work, it was that they couldn’t keep up. As domains evolved, new data formats emerged, and more complex questions arose, these systems struggled. Adding new domains often required painful schema changes, updates to ontologies, or entire overhauls of the knowledge graph. These systems were brittle, hard to maintain, and lacked depth of understanding. Their reasoning capabilities were limited to the rules engineers could encode. Not to mention, many ontology development strategies were designed by committee, and by the time an ontology was approved it was outdated.
And while efforts were made to automate some of this using statistical methods or basic data mining, most systems still relied heavily on human-defined structure and logic. The result? Maintenance burdens that scaled with the rate of change. And in today’s world, that’s a losing proposition.
Why LLMs Alone Aren’t Enough
Enter large language models. LLMs are capable of understanding natural language, performing semantic reasoning, and generating summaries and insights across vast, unstructured data. These models are a massive leap forward. But they also come with limitations.
LLMs alone can hallucinate. They lack groundedness in authoritative sources. And without safeguards, they may prioritize fluency over factual accuracy. In high-stakes domains like national security, emergency response, and defense, those risks are unacceptable.
That’s why we’ve taken a hybrid approach combining the contextual power of LLMs with the reliability and structure of traditional machine learning and data engineering pipelines.
The Best of Both Worlds: Our Hybrid Approach
Instead of discarding the proven techniques of the past, we’ve brought them forward and fused them with what’s now possible.
– Lexical + Semantic Search: Our system supports both traditional keyword search and vector-based semantic search. This means users can be as precise or as open-ended as they need. If you know the exact term, great, the system will find it. If you’re exploring unfamiliar terrain, semantic search helps uncover what you didn’t even know to ask for.
– Structured Pipelines with AI Reasoning: We continue to use structured data pipelines for high-trust domains, integrating geospatial data, sensor outputs, and verified reports. But instead of relying solely on rules and schemas, we overlay LLMs to provide context, summarize findings, and interpret results for non-technical users.
– Built-In Resilience: By anchoring generative AI outputs to structured, validated sources, we reduce hallucination risk and maintain traceability. Our models aren’t just generating answers, they’re guiding users through a system grounded in data integrity.
– Human-in-the-Loop: We build systems that amplify human expertise, not replace it. Analysts, planners, and operators stay in control, but they spend less time wrangling data and more time making decisions.
– Agentic Reasoning: In leveraging modern agentic reasoning technologies, we are able to unlock dynamic ways to mine information in an intuitive way. These technologies enable connections to modern and classical methods that enrich content and provide direct answers to questions with confidence.
Looking Ahead
We’re entering an era where data is no longer the bottleneck, context is. Systems that simply store or retrieve data won’t cut it. What matters is helping users ask better questions, understand complex situations, and act with confidence.
By combining cutting-edge LLM capabilities with time-tested ML and engineering practices, we’re building intelligence systems that are as flexible as they are reliable. Ones that adapt to evolving domains without constant rework, and scale with the needs of modern missions.
If you’re navigating high-stakes environments and drowning in data, let’s talk. Our platform is built for teams like yours where getting to the answer isn’t just important, it’s urgent.
