Training Consultancies

Know what to train before you build it. Prove it worked after.

We designed a two-day programme for 300 people. Six months later, nobody could tell us what changed.

When to use this

Needs analysis takes too long

You're running pre-training interviews manually - calls, surveys, focus groups. By the time you've understood the real gaps, half the budget window has closed.

Training is designed on assumptions

You're building programmes based on what managers think their teams need, not what people on the ground actually struggle with day to day.

No way to prove impact

The programme went well. Feedback scores were high. But six months later, nobody can show the client where their teams actually moved. Without a scored baseline, there's nothing to measure against.

How it works
1

Interview before you design

Deploy AI-led interviews to every stakeholder group the training will touch. Surface real skill gaps, workflow friction, and barriers to adoption - in days, not weeks.

2

Build a benchmark before you train

Every response is scored automatically. Build a quantified picture of where people stand today - by team, function, or skill area - so you have a real baseline to measure against.

3

Design with evidence

Use the findings to shape training content around real needs. Every module maps to gaps your stakeholders actually described.

4

Repeat the benchmark, prove the shift

Run the same interviews post-training. Compare scored results against the baseline to show exactly where teams moved, what stuck, and where gaps remain.

Impact
Real gaps, not assumed ones
Training designed around what people actually struggle with, not what their managers assumed
Baseline from day one
Quantified benchmarks before training starts, so you can measure change, not just satisfaction
Proof that sticks
Before-and-after data that shows what shifted - evidence the client can take to the board
Repeatable across cohorts
Same methodology across every programme and every client, run by any member of your team