How ContextIQ Solutions Enhanced Data Insights with llmcontrols.ai

Disclaimer: The following stories are fictitious and generated using AI; they represent potential implementations using LLM Controls, and may include elements under active development or to be jointly developed with customers

The Challenge

Emma, founder of ContextIQ Solutions, led a team providing advanced text analytics services to clients from diverse industries. Their challenge was extracting meaningful entities, like people, organizations, dates, and locations, from large volumes of unstructured text such as contracts, emails, and social media posts.

“Our analysts were overwhelmed by raw data," Emma says. "Detecting important names, timelines, and places among the noise took too much manual effort, and crucial context sometimes got lost.”

Discovering llmcontrols.ai

Emma’s team discovered llmcontrols.ai, a visual AI workflow platform with powerful entity recognition capabilities and customizable models that could learn domain-specific terms.

“What impressed us was how flexible the entity recognition was,” Emma explains. “We could detect common entities like names and dates, but also train the system on custom entities unique to our clients’ industries. Plus, llmcontrols.ai understood context to avoid misclassification.”

Building Their First Workflow: Named Entity Recognition (NER) Pipeline

Emma’s team wanted to replace manual tagging with automated text analysis. Using llmcontrols.ai’s visual builder, they built a Named Entity Recognition (NER) Pipeline that detected names, organizations, dates, and locations across emails, reports, and social posts.

The Setup:

They connected Text Ingestion component, configured AI Recognition Components for entity detection, and added Context-Aware Rules for accuracy. Outputs were structured into JSON files and linked to Knowledge Bases to resolve ambiguities like “Jordan” the person vs. “Jordan” the country.

Soon, ContextIQ expanded into Custom Model Training for industries like law, healthcare, and finance, integrating Custom Output Schemas, APIs, and Validation Layers for reliable, analysis-ready data.

The Result:

Entity tagging time dropped from hours to minutes, with higher accuracy and contextual clarity. Analysts shifted from data cleanup to generating insights, turning complex text into structured intelligence powered by llmcontrols.ai.

Scaling Up: Custom Entity Model Training

Encouraged by early wins, ContextIQ refined its workflows by training custom models for domain-specific entities:

• Legal clauses and contract terms for law firms
• Medical conditions and treatments for healthcare providers
• Financial instruments and metrics for investment clients

This expanded capability allowed precise extraction and classification tailored to unique client needs.

Contextual Awareness

llmcontrols.ai’s deep contextual understanding ensured that entity recognition was not just about spotting text patterns but involved semantic grasp:

• Dates were interpreted within a narrative context (e.g., ‘next Friday’ relative to the document date)
• Locations were linked to geopolitical hierarchies (city-province-country)
• Person and organization names were disambiguated by related terms and prior mentions.

This holistic approach enriched insights and enabled sophisticated analytics applications.

The Impact

Emma’s team transitioned from manual entity tagging to deploying scalable, accurate entity recognition systems. Analysts could focus on generating insights and advising clients instead of laborious data preparation.

Key results included faster data processing, higher extraction accuracy, domain-specific customization, and improved client satisfaction with contextual, actionable insights.

Building Entity Recognition Workflows for Every Client

Today, ContextIQ runs many specialized NER workflows in llmcontrols.ai, each tailored to client data types and business contexts. Their visual builder accelerates prototyping, customization, and deployment, enabling ongoing innovation in entity extraction.

Whether you need to detect names, dates, locations, or train custom entity models for your domain, we’ll help you create business entity recognition automation workflows that unlock deeper meaning in your data.

Whether you need to detect names, dates, locations, or train custom entity models for your domain, we’ll help you create intelligent, adaptable workflows that unlock deeper meaning in your data.

We collaborate with you to:

• Define entity detection targets tailored to your needs
• Build multi-entity, context-aware recognition systems
• Train and deploy custom models for domain-specific entities.
• Integrate outputs into your analytics and decision systems.
• Train your team to manage and enhance workflows independently.

Just like ContextIQ Solutions transformed their entity recognition, we’ll help you extract greater insight, faster and more reliably.