The Technology
Most tools score a survey. GloCoach reasons from your organization’s own behavioral history — retrieving real evidence, structuring the query, and getting sharper with every interview.
The architecture
Most HR technology operates at Layer 1. GloCoach integrates all three — the outputs are fundamentally different.
Three independent behavioral signals feed the engine — observed behavior, performance history, and structured AI interviews — each capturing what the others miss. See the three signals in detail →
Lance aggregates all three signals into a single evidence base — RAG retrieves the records, prompt orchestration structures the query, and the recursive learning loop makes every interview improve accuracy. The output is a structured inference from your organization’s actual behavioral history — not generated internet text. Every signal is scored against the GloCoach Leadership Model — the validated competency framework, refined over seven years, that gives the engine one consistent language for leadership.
RAG + prompt orchestration + recursive learning loop. Not fine-tuned. Not generic.
The output layer translates behavioral intelligence into the decisions that matter: succession readiness, transformation risk maps, hi-po lists, strategic gap analysis. Every output carries an evidence trace — the CHRO defends the recommendation with behavioral specifics, not a vendor’s algorithm.
Human in the loop: GloCoach consultants validate outputs before any adverse recommendation is surfaced.
Layer 1, in the field
The signals are captured on the phone — in the flow of everyday work, not in an annual review cycle. A leader opens the app, has a natural conversation with Lance, and minutes later a structured behavioral read exists. That’s the raw evidence the engine reasons over.
The signals — captured in the flow of work
Lance runs the Observation Based Assessment conversationally
Minutes later: a structured, competency-mapped read (sample shown)
Inside the brain
The intelligence layer that competitors can’t replicate — because they don’t have the data.
Behavioral data handling and consent model: see Responsible AI & Trust
The recursive learning loop
Every decision outcome feeds back into the evidence base — so each cycle sharpens the inference behind the next. This is the mechanism that turns behavioral history into decision-grade intelligence.
Evidence in, decisions out — and each cycle sharpens the next.
See it in action
The methodology — why observed behavior beats self-reported data
Lance running a live behavioral interview — voice-first, adaptive
“My coach helped me to think outside the box and try new behaviors and methods of working. I would recommend it to other colleagues.”
— Divestiture Specialist, Siemens SLEP III (anonymized)
Request a demo. Bring three real leadership challenges. We’ll show you the platform — and what your bench intelligence would look like.