Operations & Data

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

Measures how thoroughly people prepare for meetings they lead and attend — agendas, objectives, materials, and roles — and where preparation habits break down. An AI follow-up interview reconstructs a specific under-prepared meeting to surface the real friction behind missed prep steps, for operations teams trying to improve meeting quality.

Maps when employees are genuinely available and willing to meet, how their meeting time actually breaks down, and where scheduling friction eats into focus work. An AI follow-up interview digs into one specific recent meeting that went wrong and what would have fixed it. Built for ops, IT, and people teams auditing meeting culture.

Measures how well meetings are planned, run, and followed up on — cadence, agenda discipline, and the biggest time-wasters — for teams and ops leaders auditing their meeting culture. An AI follow-up interview reconstructs a recent unproductive meeting to surface the specific root cause behind the numbers.

Measures how people actually schedule, reschedule, and coordinate meetings today — time spent, satisfaction with current tools, and which features would save the most time. Built for product and operations teams evaluating or building meeting planning software. An AI follow-up interview reconstructs a specific recent scheduling breakdown instead of relying on a generic satisfaction score.

Evaluates how well your organization sources from certified diverse-owned suppliers — spend levels, barriers, and category priorities — for procurement and sourcing leaders. An AI follow-up interview reconstructs a real recent sourcing decision to surface what actually helped or blocked a diverse supplier from winning the business.

Captures how easy and effective it is for internal teams to reach and work with a vendor's point of contact — responsiveness, accuracy of contact info, and follow-through. An AI follow-up digs into the most recent real interaction to surface what actually happened when things went well or went wrong, beyond the rating.

Measures how much customers trust your supply chain claims, which transparency details (sourcing, labor, environmental impact, certifications) actually matter to them, and what they'd pay for verified information — with an AI follow-up that digs into the real story behind a specific trust or mistrust moment.

Assesses how a vendor or supplier is performing on quality, reliability, pricing, and support, and gauges internal appetite for renewal. Built for procurement teams and vendor managers running periodic supplier reviews, with an AI follow-up interview that digs into the specific incident or driver behind a respondent's satisfaction score instead of stopping at a number.

Measures how a vendor performs on quality, delivery, pricing, and support, plus renewal intent and what factors matter most in the relationship. Built for procurement and operations teams running periodic vendor reviews, with an AI follow-up interview that digs into the specific incident behind a low or high score instead of leaving it as a number.

Diagnoses friction in a specific internal process — where time gets lost, which steps break down, and what to fix first — built for ops leads, IT, or process owners running a targeted improvement review. A trade-off ranking surfaces the top pain point, and the AI follow-up interview digs into a concrete recent example instead of a generic complaint.

Measures how predictable, swappable, and fair hourly employees find their work schedules, plus early burnout signals like clopening shifts and exhaustion. Includes an AI follow-up that reconstructs a specific recent scheduling incident — a denied swap or a last-minute change — instead of relying on general impressions.

Pinpoints which internal processes waste the most employee time, how often people route around official steps with workarounds, and which tasks are the best candidates for automation. Built for ops, IT, or PMO teams doing a process-improvement audit; the AI follow-up reconstructs the actual workaround steps behind the numbers so you know exactly what to fix first.

Captures what went well, what broke, and why on a just-completed project, then surfaces the process changes the team would realistically adopt. An AI follow-up reconstructs the actual sequence of events behind the biggest setback instead of settling for a vague 'communication issues' answer. Built for project leads and PMOs running structured retros.

Assesses how ready employees are for digital transformation across tool adoption willingness, skill confidence, process pain points, and change fatigue. Built for operations, IT, and transformation leaders sequencing a rollout. An AI follow-up digs into the specific reason behind hesitation or exhaustion that scale scores alone can't explain.

Measures how reps actually experience your sales tech stack — CRM friction, tool overlap, hours lost to admin, and which tools they'd cut or keep — for sales ops leaders auditing their stack. An AI follow-up reconstructs the specific moment a tool slowed a rep down, instead of collecting generic complaints.

Measures how employees experience IT support — ticket resolution speed, first-contact resolution, technician communication, and whether self-service resources actually deflect tickets. An AI follow-up reconstructs what really happened on the respondent's most recent ticket instead of a generalized impression, surfacing the specific breakdowns behind low satisfaction scores.

Replace single-point guesses with honest uncertainty: experts build a probability distribution over outcomes instead of naming one number, and the AI interviewer elicits the assumptions behind their shape. Ideal for sales forecasts, launch estimates, and planning reviews.

Give stakeholders 100 points and make them spend it: constant-sum allocation across initiatives forces the prioritization that agree-scale surveys hide. Follow-up questions capture what they'd cut entirely, and the AI interviewer pressure-tests their biggest bet.

Collects structured partner feedback on reliability, delivery, communication, and value over a 90-day period to generate actionable supplier performance scorecards for operations optimization.