Intelligence Beyond Computation
How we deployed a custom Large Language Model (LLM) agent to automate 70% of internal queries and deliver real-time predictive insights.
Experience the Neural Interface
Interact with our simulated mobile AI client. Toggle modes to see how the agent handles multimodal inputs and visualization.
Agent Modes
The Information Paradox
Our client, a Fortune 500 logistics firm, was drowning in unstructured data. Employees spent 40% of their day searching PDF manuals, legacy databases, and email threads to find basic operational procedures.
Avg. Query Time
Manual search duration.
Unstructured Data
Of total enterprise knowledge.
The Cognitive Architecture
We built a custom RAG (Retrieval-Augmented Generation) pipeline, connecting a fine-tuned GPT-4 model to the company's secure internal knowledge base.
Semantic Search
Vector embeddings allow the AI to understand the "intent" behind a query, not just keyword matching.
Enterprise Privacy
Deployed within a Virtual Private Cloud (VPC) ensuring zero data leakage to public model training sets.
Real-time Inference
Optimized token streaming provides users with answers in < 1.5 seconds, mimicking human conversation speed.
Deployment Impact
| KPI | Before AI | With Prohuman AI |
|---|---|---|
| Employee Search Time | ~3 Hours/Day | ~15 Minutes/Day |
| Answer Accuracy | 65% (Human Error) | 98.5% (Cited Sources) |
| Operational Savings | Baseline | $2.4M / Year |
Future-Proof Your Workforce
Stop searching, start knowing. Book a consultation to see how our custom Enterprise AI agents can integrate with your data.
Schedule AI Assessment