Finding the Workflow behind a Sales Bottleneck for a North American Manufacturer
Client: A manufacturer selling specialized devices to construction companies across North America
Engagement: Workflow audit, opportunity mapping, AI design, prototyping, build, and testing
OUTCOMES AT A GLANCE
2
Planned hires avoided Estimated $120K-$145K year
40-50 hours/week
of repetitive work reduced: request investigation product matching, quote drafting, etc.
4-7 dys -> 1-2 dys
helped regional reps respond faster to standard and near-standard requests
The starting point
The company was growing faster than its sales process could handle.
It sold specialized devices to construction companies across North America, with each regional sales rep responsible for a specific territory. As demand increased, the sales process began to strain.
Requests were piling up. Response times were slipping. The backlog had grown to an estimated 250-300 open requests.
Finding the Workflow behind a Sales Bottleneck for a North American Manufacturer
Client: A manufacturer selling specialized devices to construction companies across North America
Engagement: Workflow audit, opportunity mapping, AI design, prototyping, build, and testing
OUTCOMES AT A GLANCE
2
planned hires avoided Estimated $120K-$145K year
40-50 hours/week
of repetitive work reduced: request investigation product matching, quote drafting, etc.
4-7 dys -> 1-2 dys
helped regional reps respond faster to standard and near-standard requests
The starting point
The company was growing faster than its sales process could handle.
It sold specialized devices to construction companies across North America, with each regional sales rep responsible for a specific territory. As demand increased, the sales process began to strain.
Requests were piling up. Response times were slipping. The backlog had grown to an estimated 250-300 open requests.
The team was receiving around 140 sales requests per week, or approximately 550-600 per month, across three main channels:
- 50% by email
- 20% through the website
- 30% through construction bid platforms
The starting point
The company had approximately 6 regional sales reps covering North American territories.
The pressure became so high that engineers were being pulled into sales processing for roughly 6-10 hours per week to help clarify requests, interpret specs, and support quoting.
That was an expensive way to handle sales paperwork – and a distraction for people who should have been designing and improving products.
The company was considering hiring more people: one sales coordinator to sort and route incoming requests, and one junior sales representative to take pressure off the regional reps. Before adding headcount, the company asked us to look at why the process was so slow.
The team was receiving around 140 sales requests per week, or approximately 550-600 per month, across three main channels:
- 50% by email
- 20% through the website
- 30% through construction bid platforms
The company had approximately 6 regional sales reps covering North American territories.
The pressure became so high that engineers were being pulled into sales processing for roughly 6-10 hours per week to help clarify requests, interpret specs, and support quoting.
That was an expensive way to handle sales paperwork – and a distraction for people who should have been designing and improving products.
The company was considering hiring more people: one sales coordinator to sort and route incoming requests, and one junior sales representative to take pressure off the regional reps. Before adding headcount, the company asked us to look at why the process was so slow.
Mapping how the work really happens
We audited the end-to-end sales workflow and timed how long each step actually took.
The workflow we mapped covered: incoming request -> intake review -> region identification -> routing -> spec review -> product matching -> inventory check -> quote drafting -> rep approval -> customer response.
Two bottlenecks stood out.
Mapping how the work really happens
We audited the end-to-end sales workflow and timed how long each step actually took.
The workflow we mapped covered:
incoming request
-> intake review
-> region identification
-> routing
-> spec review
-> product matching
-> inventory check
-> quote drafting
-> rep approval
-> customer response.
Two bottlenecks stood out.
Bottleneck 1: The intake problem
Sales requests arrived through three inconsistent channels: email, website forms, and construction bid platforms.
Someone had to read each request, understand what the customer needed, identify the region, and route it to the right sales rep.
That sounds simple – until the request was incomplete.
Many incoming requests were missing key information, especially the project address. When that happened, the coordinator or rep had to investigate the origin manually.
They would check:
- the sender’s email domain
- the company website
- the construction company’s location
- project details in the bid platform
- and sometimes search online to infer the right territory
This detective work could take up to 15 minutes for one incomplete request.
Based on the workflow audit, we estimated that 35-40% of requests required some form of manual investigation before routing.
At 140 requests per week, that meant roughly 50-55 requests per week required extra intake work. Even at an average of 10-12 minutes each, this created approximately 8-11 hours per week of pure coordination overhead before any selling began.
Bottleneck 1: The intake problem
Sales requests arrived through three inconsistent channels: email, website forms, and construction bid platforms.
Someone had to read each request, understand what the customer needed, identify the region, and route it to the right sales rep.
That sounds simple – until the request was incomplete.
Many incoming requests were missing key information, especially the project address. When that happened, the coordinator or rep had to investigate the origin manually.
They would check:
- the sender’s email domain
- the company website
- the construction company’s location
- project details in the bid platform
- and sometimes search online to infer the right territory
This detective work could take up to 15 minutes for one incomplete request.
Based on the workflow audit, we estimated that 35-40% of requests required some form of manual investigation before routing.
At 140 requests per week, that meant roughly 50-55 requests per week required extra intake work. Even at an average of 10-12 minutes each, this created approximately 8-11 hours per week of pure coordination overhead before any selling began.
Bottleneck 2: The processing problem
Once a request reached a sales rep, a predictable sequence followed.
The rep had to:
- read the customer specification
- match it against standard device models
- check whether the required product or parts were available
- draft a response
- suggest a better-fit model where appropriate
- recommend upgrades
- and explain the value of those recommendations to the customer
Experienced reps were especially valuable at this stage. They knew when a customer had selected a basic model but would benefit from an upgraded version. They understood which device was better suited to the construction context. They could advise, upsell, and protect the customer from making the wrong purchase.
But much of their time was being consumed by repetitive matching and quote preparation.
For standard requests, reps were spending an estimated 20-25 minutes reading the request, matching the product, checking inventory, and drafting the response.
Around 60-65% of incoming requests were standard or near-standard product requests.
That meant reps were spending roughly 30-35 hours per week on repeatable quote-preparation work that could be partially automated.
The truly skilled work was different: bespoke device design, complex customer advice, upgrade recommendations, and technical judgment. But the repetitive work was taking time away from that.
Bottleneck 2: The processing problem
Once a request reached a sales rep, a predictable sequence followed.
The rep had to:
- Read the customer specification
- match it against standard device models
- check whether the required product or parts were available
- draft a response
- suggest a better-fit model where appropriate
- recommend upgrades
- and explain the value of those recommendations to the customer
Experienced reps were especially valuable at this stage. They knew when a customer had selected a basic model but would benefit from an upgraded version. They understood which device was better suited to the construction context. They could advise, upsell, and protect the customer from making the wrong purchase.
But much of their time was being consumed by repetitive matching and quote preparation.
For standard requests, reps were spending an estimated 20-25 minutes reading the request, matching the product, checking inventory, and drafting the response.
Around 60-65% of incoming requests were standard or near-standard product requests.
That meant reps were spending roughly 30-35 hours per week on repeatable quote-preparation work that could be partially automated.
The truly skilled work was different: bespoke device design, complex customer advice, upgrade recommendations, and technical judgment. But the repetitive work was taking time away from that.
The problem behind the problem
The company thought it had a capacity problem.
It looked like they needed more people.
But after mapping the workflow, the deeper issue became clear: skilled people were spending too much time on repetitive routing, investigation, matching, and quote preparation.
Hiring more people would have helped temporarily, but it would also have scaled the same inefficient workflow.
The right problem was not: “How do we hire more people to handle the backlog?”
The right problem was: “How do we free sales reps and engineers to do the work only they can do – advising customers, upselling, solving complex problems, and designing bespoke solutions?”
That reframing changed the solution.
The problem behind the problem
The company thought it had a capacity problem.
It looked like they needed more people.
But after mapping the workflow, the deeper issue became clear: skilled people were spending too much time on repetitive routing, investigation, matching, and quote preparation.
Hiring more people would have helped temporarily, but it would also have scaled the same inefficient workflow.
The right problem was not: “How do we hire more people to handle the backlog?”
The right problem was: “How do we free sales reps and engineers to do the work only they can do – advising customers, upselling, solving complex problems, and designing bespoke solutions?”
That reframing changed the solution.
How we worked
PivotPath led the engagement from workflow discovery to final build.
- Workflow audit – We mapped the full sales request journey from intake to customer response and identified where time was being lost.
- Opportunity mapping – We separated repetitive work from high-value human judgment and identified where AI could reduce workload without removing human control.
- AI design – We designed two AI agents around the real workflow: one for first-contact routing and one for rep support.
- Prototyping – We built an MVP and tested it with real sales users for one week.
- Build and integration – We connected the agents to the sales team’s existing workflow and key systems.
- Testing and iteration – We ran multiple feedback cycles with the coordinator and sales reps, checking whether the team trusted the tools enough to use them.
- Handover and adoption support – We delivered the final solution with usage guidance, escalation rules, and a human-in-the-loop approval process.
The measure of success was not simply whether the AI could produce outputs. The measure of success was whether the team actually used the system in the flow of daily work.
How we worked
PivotPath led the engagement from workflow discovery to final build.
- Workflow audit – We mapped the full sales request journey from intake to customer response and identified where time was being lost.
- Opportunity mapping – We separated repetitive work from high-value human judgment and identified where AI could reduce workload without removing human control.
- AI design – We designed two AI agents around the real workflow: one for first-contact routing and one for rep support.
- Prototyping – We built an MVP and tested it with real sales users for one week.
- Build and integration – We connected the agents to the sales team’s existing workflow and key systems.
- Testing and iteration – We ran multiple feedback cycles with the coordinator and sales reps, checking whether the team trusted the tools enough to use them.
- Handover and adoption support – We delivered the final solution with usage guidance, escalation rules, and a human-in-the-loop approval process.
The measure of success was not simply whether the AI could produce outputs. The measure of success was whether the team actually used the system in the flow of daily work.
What PivotPath delivered
- end-to-end sales workflow audit
- bottleneck and time-loss analysis
- AI opportunity map
- MVP prototype
- first-contact AI routing agent
- sales rep-support AI agent
- territory identification logic
- product/spec matching logic
- inventory-checking workflow
- CRM connection
- email inbox connection
- website form connection
- construction bid platform input workflow
- product catalogue and technical specification connection
- company website and domain lookup process
- human escalation rules
- rep approval workflow
- testing plan
- user feedback cycles
- final build
- and handover documentation
We did not build AI as a standalone tool. We built it into the workflow the sales team already used.
What we delivered
- end-to-end sales workflow audit
- bottleneck and time-loss analysis
- AI opportunity map
- MVP prototype
- first-contact AI routing agent
- sales rep-support AI agent
- territory identification logic
- product/spec matching logic
- inventory-checking workflow
- CRM connection
- email inbox connection
- website form connection
- construction bid platform input workflow
- product catalogue and technical specification connection
- company website and domain lookup process
- human escalation rules
- rep approval workflow
- testing plan
- user feedback cycles
- final build
- and handover documentation
We did not build AI as a standalone tool. We built it into the workflow the sales team already used.
What we built
We built two AI agents mapped directly to the two bottlenecks.
1. First-contact agent
The first-contact agent supports the sales coordination role.
It reads incoming requests from:
- website form submissions
- construction bid platforms
- CRM records
- and product/request intake data
When the origin is not clearly stated, the agent investigates automatically. It uses available information such as sender email domain, company website, bid/project details, customer location, and online search signals.
It then identifies the likely region and routes the request to the correct regional sales rep. Only requests the agent cannot classify confidently are escalated to a person.
This means the human now handles genuine edge cases instead of manually reviewing every request.
2. Rep-support agent
The rep-support agent supports each regional sales rep. It receives the routed request, reads the specification, and checks whether it matches a standard device.
If the request matches a standard or near-standard product, the agent:
- identifies the most relevant model
- checks the product catalogue
- checks inventory availability
- drafts a customer response
- suggests possible upgrades
- and prepares the draft for rep review
The agent’s recommendations were patterned on successful past rep behaviour, so the suggestions reflected how experienced reps actually advised customers.
Nothing goes to the customer automatically. The rep reviews, adjusts, and approves the response before it is sent.
If the request does not match a standard model, the agent escalates it to the rep for bespoke design and technical judgment. This kept the skilled work with the human and moved the repetitive work to the AI-supported workflow.
What we built
We built two AI
1. First-contact agent
The first-contact agent supports the sales coordination role.
It reads incoming requests from:
- website form submissions
- construction bid platforms
- CRM records
- and product/request intake data
When the origin is not clearly stated, the agent investigates automatically. It uses available information such as sender email domain, company website, bid/project details, customer location, and online search signals.
It then identifies the likely region and routes the request to the correct regional sales rep. Only requests the agent cannot classify confidently are escalated to a person.
This means the human now handles genuine edge cases instead of manually reviewing every request.
What we built
We built two AI agents
2. Rep-support agent
The rep-support agent supports each regional sales rep. It receives the routed request, reads the specification, and checks whether it matches a standard device.
If the request matches a standard or near-standard product, the agent:
- identifies the most relevant model
- checks the product catalogue
- checks inventory availability
- drafts a customer response
- suggests possible upgrades
- and prepares the draft for rep review
The agent’s recommendations were patterned on successful past rep behaviour, so the suggestions reflected how experienced reps actually advised customers.
Nothing goes to the customer automatically. The rep reviews, adjusts, and approves the response before it is sent.
If the request does not match a standard model, the agent escalates it to the rep for bespoke design and technical judgment. This kept the skilled work with the human and moved the repetitive work to the AI-supported workflow.
Before and after workflow
Before PivotPath
- Incoming request
- Manual review across email, website, and bid platforms
- Missing address or unclear region
- Manual investigation: email domain, website, online search
- Request routed to regional rep
- Rep reads spec from scratch
- Rep manually checks standard models
- Rep checks inventory
- Rep drafts quote and response
- Engineer may be pulled in for clarification
- Customer waits 4-7 business days
After PivotPath
- Incoming request
- First-contact agent reads and classifies request
- Agent identifies region using request and company data
- Low-confidence cases escalate to a person
- Request routed to correct regional rep
- Rep-support agent reads spec and checks product match
- Agent checks catalogue and inventory
- Agent drafts approval-ready response with possible upsell
- Rep reviews, edits, and approves
- Complex bespoke requests stay with the rep
- Customer receives response in 1-2 business days for standard requests
Before and after workflow
Before PivotPath
- Incoming request
- Manual review across email, website, and bid platforms
- Missing address or unclear region
- Manual investigation: email domain, website, online search
- Request routed to regional rep
- Rep reads spec from scratch
- Rep manually checks standard models
- Rep checks inventory
- Rep drafts quote and response
- Engineer may be pulled in for clarification
- Customer waits 4-7 business days
Before and after workflow
After PivotPath
- Incoming request
- First-contact agent reads and classifies request
- Agent identifies region using request and company data
- Low-confidence cases escalate to a person
- Request routed to correct regional rep
- Rep-support agent reads spec and checks product match
- Agent checks catalogue and inventory
- Agent drafts approval-ready response with possible upsell
- Rep reviews, edits, and approves
- Complex bespoke requests stay with the rep
- Customer receives response in 1-2 business days for standard requests
How much time was saved?
The largest time savings came from three areas.
1. Intake and routing
Before the AI agent, incomplete requests required manual investigation. At an estimated 50-55 incomplete or unclear requests per week, with an average of 10-12 minutes saved per request, the first-contact agent saved approximately 8-11 hours per week in intake and routing time.
2. Standard quote preparation
Around 60-65% of weekly requests were standard or near-standard product requests. That equals roughly 85-90 requests per week.
Before the rep-support agent, each standard request took an estimated 20-25 minutes to review, match, check, and draft. After the agent, reps spent around 5-7 minutes reviewing and approving the draft.
That created an estimated saving of 15-18 minutes per standard request, or roughly 21-27 hours per week in rep time.
3. Engineer interruptions
Before the workflow change, engineers were being pulled into sales processing for approximately 6-10 hours per week.
After the agents were introduced, engineers were only involved in genuinely bespoke or technically unclear requests. Estimated engineering time recovered: 5-8 hours per week.
Total estimated weekly time saved
Together, the workflow reduced approximately 40-50 hours per week of repetitive coordination, sales admin, and avoidable engineering support. That is roughly equivalent to one full-time role, plus meaningful time returned to sales reps and engineers.
How much time was saved?
The largest time savings came from three areas.
1. Intake and routing
Before the AI agent, incomplete requests required manual investigation. At an estimated 50-55 incomplete or unclear requests per week, with an average of 10-12 minutes saved per request, the first-contact agent saved approximately 8-11 hours per week in intake and routing time.
2. Standard quote preparation
Around 60-65% of weekly requests were standard or near-standard product requests. That equals roughly 85-90 requests per week.
Before the rep-support agent, each standard request took an estimated 20-25 minutes to review, match, check, and draft. After the agent, reps spent around 5-7 minutes reviewing and approving the draft.
How much time was saved?
That created an estimated saving of 15-18 minutes per standard request, or roughly 21-27 hours per week in rep time.
3. Engineer interruptions
Before the workflow change, engineers were being pulled into sales processing for approximately 6-10 hours per week.
After the agents were introduced, engineers were only involved in genuinely bespoke or technically unclear requests. Estimated engineering time recovered: 5-8 hours per week.
Total estimated weekly time saved
Together, the workflow reduced approximately 40-50 hours per week of repetitive coordination, sales admin, and avoidable engineering support. That is roughly equivalent to one full-time role, plus meaningful time returned to sales reps and engineers.
The outcome
The company came in believing it needed more people. After mapping the workflow, we found that the deeper issue was not simply headcount. It was that valuable people were spending too much time on repetitive work.
The AI-supported workflow helped the company:
- avoid or delay two planned hires, would likely have cost approximately $120K-$145K CAD annually once base salary, employer payroll costs, and benefits are considered.
- reduce repetitive admin by an estimated 40-50 hours per week
- shorten standard request response time from 4-7 business days to 1-2 business days
- reduce the backlog from approximately 250-300 requests to near-current processing
- reduce engineer involvement in sales processing by 5-8 hours per week
- and give reps more time for advisory selling, upselling, and bespoke customer solutions
The result was not just faster processing. The result was a better use of human expertise.
Sales reps could spend more time selling and advising. Engineers could spend more time designing. Customers received faster responses. And the company increased capacity without immediately adding more people.
“Our reps finally spend their time selling and solving, not sorting emails.”
The outcome
The company came in believing it needed more people. After mapping the workflow, we found that the deeper issue was not simply headcount. It was that valuable people were spending too much time on repetitive work.
The AI-supported workflow helped the company:
- avoid or delay two planned hires, would likely have cost approximately $120K-$145K CAD annually once base salary, employer payroll costs, and benefits are considered.
- reduce repetitive admin by an estimated 40-50 hours per week
- shorten standard request response time from 4-7 business days to 1-2 business days
- reduce the backlog from approximately 250-300 requests to near-current processing
- reduce engineer involvement in sales processing by 5-8 hours per week
- and give reps more time for advisory selling, upselling, and bespoke customer solutions
The outcome
The result was not just faster processing. The result was a better use of human expertise.
Sales reps could spend more time selling and advising. Engineers could spend more time designing. Customers received faster responses. And the company increased capacity without immediately adding more people.
“Our reps finally spend their time selling and solving, not sorting emails.”
The PivotPath difference
The company was ready to hire for a problem that was not really about people.
PivotPath mapped how the work actually happened, found where the time was truly going, and built AI around the workflow – not around hype.
The result was a practical system that took repetitive work off skilled people while keeping humans in control of every important customer-facing decision.
In this case, the opportunity was sales intake and quoting. In another organization, it might be reporting, reconciliation, onboarding, claims review, compliance documentation, or internal coordination.
The method is the same: find the real workflow problem first. Then build the AI solution around how people actually work.
The PivotPath difference
The company was ready to hire for a problem that was not really about people.
PivotPath mapped how the work actually happened, found where the time was truly going, and built AI around the workflow – not around hype.
The result was a practical system that took repetitive work off skilled people while keeping humans in control of every important customer-facing decision.
In this case, the opportunity was sales intake and quoting. In another organization, it might be reporting, reconciliation, onboarding, claims review, compliance documentation, or internal coordination.
The method is the same: find the real workflow problem first. Then build the AI solution around how people actually work.
Curious where AI genuinely fits in your business?
Start with a two-week AI Opportunity Audit
Let’s Build together
Reach out now:
connect@pivotpath.ca
#250 – 2482 Yonge St
Toronto, ON M4P 2H5
Schedule a Chat
Privacy Policy
YASHAR
Head of AI Strategy & Design
SASHA
Head of AI Engineering
Curious where AI genuinely fits
in your business?
Start with a two-week AI Opportunity Audit:
Let’s Build together
Reach out now:
connect@pivotpath.ca
#250- 2482 Yonge St
Toronto, ON M4P 2H5
Schedule a Chat
Privacy Policy
YASHAR
Head of AI Strategy & Design
SASHA
Head of AI Engineering