AI-First Talent Decisions
At Enterprise Scale
Mithril partnered with Swabhav Venturelabs to evolve into an AI-first talent platform—turning learning and assessment data into instant, explainable role decisions with human oversight.
Mithril reduced mentor workload, accelerated staffing, and improved talent retention by embedding AI directly into core talent workflows—without removing human judgment.
Learning Management System (LMS) integration
Data transformation & scoring engine
️
Generative AI (LLM-based reasoning)
️
Mentor review & validation dashboard
Security, audit logs, and access control
Key Services
Move talent decisions from manual judgment to AI-augmented intelligence without losing trust.
Enterprises invest heavily in training, but the final step—assigning people to the right roles—is often slow, inconsistent, and dependent on overburdened mentors.
Mithril’s vision was to:
- Reduce dependency on manual spreadsheets and subjective reviews
- Speed up time-to-productivity after training
- Improve retention by placing people in roles where they actually fit
- Do all of this with human oversight , not blind automation
Business Impact
~98% Reduction
in mentor
effort
(from ~12 hours/week to ~5 minutes per batch review)
+15% Increase
in trainee
retention
(better role-fit, lower frustration)
25% Faster
staffing and
deployment
(reduced time-to-billability)
Enterprise-Ready
governance &
explainability
(AI decisions with audit trails and validation)
An AI-driven role recommendation system embedded into Mithril’s core talent workflows—combining speed, consistency, and mentor validation.
Addressing Core Challenges
Before AI:
- Mentors spent hours every week manually allocating trainees
- Role-fit decisions were inconsistent across batches
- Delays in allocation slowed billability and deployment
- Misaligned roles increased disengagement and attrition
Hands-on Delivery Structure
Swabhav designed and deployed a generative AI–based role allocation engine that:
- Ingests learning scores, assessments, and performance data from the LMS
- Transforms raw data into role-specific signals
- Uses an LLM to generate role recommendations
- Routes recommendations to mentors for review and approval
Data Ingestion & Normalization
Collected trainee scores, behavioral ratings, and history from the LMS and normalized them to prevent bias.
Generative AI Role Reasoning
Used a generative AI model (instead of rigid ML classification) to reason across limited historical data and produce flexible role-fit recommendations.
Human-in-the-Loop Validation
Built a lightweight mentor dashboard where AI recommendations are reviewed, adjusted, and approved ensuring trust and adoption.
Governance & Guardrails
Implemented explainability, hallucination checks, access control, and audit logs to meet enterprise governance standards.
Turn talent data into intelligent decisions.
Build AI-first talent platforms that scale with trust.
