msg.ProfileMap and Amazon Bedrock deliver 95% accurate skill matching and 70% faster HR workflows

How msg Uses Amazon Bedrock to Make HR Data Work Harder

HR teams don't need more dashboards. They need cleaner data, faster matching, and decisions they can trust. That's exactly where msg's integration of Amazon Bedrock with msg.ProfileMap delivers.

By pairing managed foundation models from Bedrock with ProfileMap's workforce and competence management features, msg automated the messy work of data harmonization across resumes, job descriptions, and skill inventories. The result: consistent profiles your staffing engine can actually use.

What's new

Using large language models like Anthropic's Claude and Meta's Llama via Amazon Bedrock, ProfileMap enriches and standardizes HR data. It maps varied terms to shared concepts-so "Python programming" and "scripting in Python" don't split candidates into two buckets.

According to results shared publicly, the system achieves up to 95% accuracy in concept matching and cuts manual processing time by about 70%. For HR, that means fewer spreadsheets, fewer review loops, and faster staffing cycles.

Why it matters for HR

• Fill roles faster: Cleaner, standardized data speeds up shortlisting and internal mobility.
• Better project fit: Skill-to-demand matching improves allocation and reduces rework.
• Focus on strategy: Automation frees your team from rote admin to workforce planning.

How it works

• Aggregate HR data from multiple sources into msg.ProfileMap.
• Use Bedrock-hosted LLMs to normalize skills, titles, and experiences across regions and formats.
• Apply concept matching to align people, roles, and projects with greater consistency.
• Feed results into staffing workflows and talent analytics for real-time planning.

Compliance and governance, built-in

The approach bakes in transparency and privacy aligned with GDPR and EU AI Act expectations. Bedrock guardrails and fine-tuning options help reduce bias risks in skill assessments, while auditability supports oversight for regulated industries.

Serverless scaling on Bedrock removes the infrastructure burden, so HR IT doesn't need to stand up or maintain ML stacks to benefit.

Proven impact

In one published example, a European bank used ProfileMap to surface skill gaps in real time, reducing project delays by roughly 40%. LLM-driven analytics projected future competence needs using external market signals, improving headcount planning and training priorities.

What you can do now

• Standardize your skill ontology: Define the skill dictionary your teams will use globally.
• Start with one high-volume use case: e.g., matching engineers to client projects.
• Set clear review loops: Keep humans in the loop for sensitive assessments.
• Track the basics: time-to-staff, data correction rate, and project delay reductions.
• Establish governance: Document prompts, guardrails, and audit processes early.

What's next

Msg plans to extend ProfileMap with multimodal models via Bedrock to analyze resumes with images or videos. Expect more personalized development paths and tighter links between learning, internal mobility, and future demand.

Bottom line for HR

Clean, consistent skills data is the lever. Automate enrichment, keep humans in the loop, and measure outcomes weekly. With ProfileMap plus Bedrock, HR can move from reactive staffing to proactive workforce planning-without spinning up its own AI infrastructure.

Learn more: Explore Amazon Bedrock or review practical AI learning paths for teams at Complete AI Training.