What we audit

Seven functions. Read end to end by AI, with an operator on site only at the top tier.

We audit the functions that make or lose the money: the call floor, the sales desk, the hiring funnel, the service queue, the lead engine, the back office and the tech stack. Every audit runs through the platform on your full data record; in-person undercover embedding is a separately priced Field Cover option. Every finding is ranked, evidenced and costed.

Contact centres and call quality

01

What does your customer actually hear?

We sit on the floor, take live calls or shadow them, and then run every recording we are given through the AI layer. Not a 2% QA sample — the whole month.

What the AI layer does

Speech-to-text on 100% of calls, then scoring for objection handling, compliance language, hold discipline, empathy and close attempts.

  • Answer, abandon and repeat-contact rates by hour
  • Talk-to-listen ratio and dead air per agent
  • Compliance phrases missed on regulated calls
  • First-contact resolution versus recorded outcome

Sales teams and pipeline

02

Is it a lead problem or an execution problem?

We work the desk alongside your reps, sit in on pitches, and reconcile what we saw with what the CRM claims. Built on the methodology behind The School of Sales.

What the AI layer does

Pipeline hygiene analysis, stage-conversion modelling, and language analysis across calls, notes and email threads to find where deals actually die.

  • Speed to first contact on inbound leads
  • Follow-up sequence completion versus claimed activity
  • Stage conversion and slippage by rep and by source
  • Discounting behaviour and margin leakage

Recruitment and hiring engine

03

Why do the wrong people arrive and the right people leave?

We enter your funnel as a candidate and as a new starter. We experience the advert, the screen, the interview, the offer and week one exactly as a hire does.

What the AI layer does

Time-in-stage modelling across the ATS, drop-off attribution, and scoring of adverts, scorecards and interview notes for consistency and bias risk.

  • Time to hire and candidate drop-off by stage
  • Ghosted applicants and unreturned interview feedback
  • Onboarding to unassisted competence, in days
  • Early attrition against the reason recorded

Customer service and retention

04

How much revenue leaves before anyone notices?

We take the tickets, the complaints and the cancellations. We measure recovery on the worst day, not the average one.

What the AI layer does

Ticket clustering to find the ten root causes behind the thousand contacts, churn-driver analysis and sentiment trends across every channel.

  • First-response and resolution time by channel
  • Repeat-contact rate and its root causes
  • Save rate on cancellation and complaint calls
  • Churn drivers ranked by revenue at risk

Marketing and lead generation

05

What are you paying for leads nobody works?

We follow a lead from ad click to the moment it goes cold, then trace it back through every system it touched.

What the AI layer does

Channel-to-revenue attribution, cost-per-qualified-outcome modelling and spend reconciliation across platforms, agencies and tooling.

  • Cost per lead versus cost per closed outcome
  • Leads received but never contacted
  • Duplicate and mis-routed enquiries
  • Agency and platform spend without an owner

Operations and back office

06

Where does the day break, and what does the break cost?

Shifts, handovers, queues, approvals, rework. We time the sequences your team runs on instinct and find the point where the process stops matching reality.

What the AI layer does

Process mining across system timestamps, throughput analysis at the constraint, and rework and exception costing.

  • Cycle time and queue depth at each constraint
  • Manual re-entry between systems, in hours per week
  • Exception and rework volume against standard
  • Scheduled versus worked coverage against demand

AI readiness and tech stack

07

What could a machine be doing by next quarter?

Noahsure Group builds and operates seven live AI platforms. We use that engineering judgement to separate what is genuinely automatable from what is a vendor's pitch deck.

What the AI layer does

Full stack map against real usage, automation candidates ranked by payback period, and a build-versus-buy call on each one.

  • Tool overlap, dormant licences and renewal exposure
  • Tasks automatable with existing tooling
  • Data quality blocking any AI deployment
  • Ranked automation roadmap with payback in months

Sample size

Traditional QA reviews 2% of calls. We review all of them.

That is the whole difference. A consultant can interview twelve people and read the reports you already produce. Our operator experiences the job, and the AI layer reads every call, ticket, CRM note and timestamp in scope — so a finding arrives as a pattern with a number, not an opinion.

Inside evidence

Real calls, real tickets, real candidate journeys — and on-site shifts at Field Cover.

Full-population analysis

Every recording, ticket and pipeline record in scope, not a sample.

Costed and ranked

Each finding carries severity, an impact estimate and a named owner.

Pick the three questions that keep you awake. We answer those first.