The challenge
Every quote the business produced required a sales rep or admin to cross-reference engineering specifications, customer history, real-time stock data, and product pricing rules - pulled from a mix of spreadsheets, PDF catalogues, the ERP, and tribal knowledge. For straightforward enquiries this took 20 to 40 minutes. For custom configurations it could absorb half a day.
Across the 20-person sales team and three admin staff supporting them, the cumulative drag was well over 50 hours every week - time that should have been spent on customer conversations and close work. At peak season the team was bottlenecked on quote turnaround. Buyers waiting more than 48 hours for a quote were quietly going elsewhere, and the team had no way to measure exactly how much revenue this was costing them. Hiring more estimators to brute-force the problem would have solved a symptom and added ongoing headcount cost. The right answer was process leverage, not headcount leverage.
The approach
We started with a NxtLayr AI Business Audit - a focused engagement to surface where the biggest operational drag actually sat and score every AI opportunity against impact and effort. The audit ran a deep-dive call, a floor walk with the operations lead, and short interviews across estimators and sales reps. We mapped every step from inbound enquiry through to quote dispatch, including the informal rules estimators applied that weren’t written down anywhere.
Eight viable AI opportunities surfaced across the business. Quoting won the top slot on the impact-versus-effort matrix - largest source of recoverable hours, clearest ROI, most tractable build path. The audit produced a costed roadmap and a recommended quick-win build. Leadership signed off the build immediately.
The build
The system sits inside the manufacturer’s existing tooling - no new dashboard for the team to learn, no new login. When a sales rep starts a quote, the AI assistant pulls the relevant product data from the catalogue, applies the right engineering and pricing rules based on customer history and configuration, checks current stock, and produces a draft quote document ready for review.
- Product data and engineering rule library indexed and queryable
- Customer history surfaced inline - previous orders, pricing tier, payment terms
- Stock check against the ERP in real time
- Draft quote rendered in the firm’s template, with line items pre-filled and notes for human review
- Human-in-the-loop required - the sales rep reviews, edits if needed, and approves before dispatch
End-to-end timeline from kickoff to live deployment was three weeks - audit, build, pilot inside a subset of the sales team, and full rollout - all inside that window. Every workflow change was documented in a runbook handed to the operations lead, so the system is fully owned by the business, not by us.
The result
Across the 23-person sales-and-admin team, more than 50 hours a week were returned to the business - a structural shift, not a one-off saving. Quote turnaround dropped from an average of 90 minutes to under 10, and from half-day worst cases to under 30. The sales team now spends its recovered time on close conversations and customer follow-ups rather than spec collation. Quote consistency improved at the same time: where two estimators might previously have produced subtly different quotes for the same enquiry, the assistant applies the same rules every time, flagging exceptions for human review rather than silently varying.
The revenue impact compounds on top of the time saved. Faster, more consistent quoting lifted quote-to-close by 2 percentage points across the team - a modest move on paper, but a significant one when applied to the business’s pipeline:
- Time recovered: 50+ hours per week × 52 weeks = 2,600+ hours per year of operational capacity returned to the business.
- Pipeline value: ~$100M annual qualified pipeline (typical 3× revenue ratio for B2B manufacturing at this scale).
- Close-rate uplift: +2 percentage points on $100M pipeline = ~$2M additional revenue per year.
- Combined impact: ~$2M in incremental revenue capacity, plus the labour value of 2,600+ hours redirected from admin to customer-facing work.
The payback math. A 3-week build that returns ~$2M in annual revenue capacity pays itself back in weeks, not months - and keeps compounding every week the system runs. The team now has clear capacity headroom to grow revenue without proportional headcount growth, which is exactly the constraint that prompted the audit in the first place.