Enterprise AI Solutions

Use-Case Scenario

An enterprise onboarding process spans 14 steps across Workday, DocuSign, IT ticketing, email, and facility management systems. HR spends 3 weeks per new hire co-ordinating across teams, with frequent delays caused by missed handoffs and inconsistent compliance checks.

Hasman's Approach

Hasman designs and deploys multi-agent AI systems where specialised agents handle each workflow domain autonomously. A supervisor agent orchestrates sequencing, handles exceptions, and escalates only when human judgement is genuinely required — creating a reliable, auditable process that runs 24/7.

Technology Stack

Architecture Overview

1. Supervisor Agent (LLM Orchestrator)
2. Domain Agents: HR · IT · Finance · Facilities
3. Tool Connectors (APIs, ERP, Email, DocuSign)
4. Exception Management & Retry Logic
5. Human-in-the-Loop Escalation Layer
6. Full Action Traceability Log

Key Outcomes

• Onboarding cycle reduced from 3 weeks to 2 days
• 85% of workflow tasks completed without human intervention
• Consistent compliance outcomes across all jurisdictions
• Full traceability and timestamp log for every agent action
• Scales to 10× hire volume with no incremental headcount

Works With

Use-Case Scenario

A precision parts manufacturer relies on human inspectors to catch surface defects at 120 parts per minute. Defect slip-through rate is 1.8%, generating significant warranty costs downstream and eroding customer trust with a Tier-1 automotive client.

Hasman's Approach

Hasman deploys a Computer Vision inspection system on edge hardware, using fine-tuned YOLO detection models trained on the client's specific defect library. The system flags, classifies, and rejects defective parts in real time with under 50 ms latency — feeding defect data into a predictive maintenance loop that catches equipment degradation before it causes production failures.

Technology Stack

Architecture Overview

1. Industrial Camera Array (area-scan / line-scan)
2. Edge Inference Device — NVIDIA Jetson Orin
3. YOLOv8 Fine-Tuned Detection Model
4. Defect Classification & Severity Engine
5. SCADA / MES Real-Time Integration
6. Predictive Maintenance Data Pipeline

Key Outcomes

• Defect slip-through rate reduced from 1.8% to 0.08%
• Line throughput maintained at 120 parts per minute
• ROI achieved in under 6 months
• Defect data feeds predictive maintenance — equipment MTBF improved 30%
• Inspector headcount redeployed to higher-value QA tasks

Works With

Use-Case Scenario

A financial services firm has 2M+ documents — contracts, policies, audit reports — that analysts must manually search. Average research time per compliance query is 4.5 hours. During regulatory audits, teams work nights and weekends to surface evidence that a purpose-built AI could retrieve in seconds.

Hasman's Approach

Hasman builds a Retrieval-Augmented Generation (RAG) application: documents are chunked, embedded, and stored in a vector database behind an enterprise security perimeter. A secure internal copilot lets analysts query the entire corpus in natural language and receive cited, accurate answers in seconds — with no data ever leaving the firm's infrastructure.

Technology Stack

Architecture Overview

1. Document Ingestion & Chunking Pipeline
2. Vector Embedding Store (Pinecone / pgvector)
3. RAG Hybrid Retrieval Layer
4. LLM Response Layer — GPT-4o / Anthropic Claude
5. Secure Enterprise UI (SSO / RBAC)
6. Full Citation & Audit Response Layer

Key Outcomes

• Research time reduced from 4.5 hours to under 4 minutes
• 94% answer accuracy validated on a curated benchmark set
• Full source citations included with every AI response
• Zero data leaves the enterprise perimeter at any point
• Analyst capacity effectively multiplied 60× per query

Works With