City Company · United States · Canada
An AI commercial operating system that replaced a ten-person sales desk.
An AI operations system now finds City Company's prospects, qualifies them across every channel, tracks tenders before they expire, and hands the sales desk only the buyers who are ready to talk — replacing what used to be a five-to-ten-person sales and dispatch team.
- Client
- City Company
- Industry
- Freight and trucking
- Market
- United States and Canada
- Fleet
- Own fleet plus owner-operators; LTL to FTL, reefer, open deck, drayage
10 → 2
People running the commercial operation
At the same revenue
3×
More deals closed than human brokers
From the same target list
33 vs 10
Monthly opportunities
AI vs. brokers, same contact list
20+ hrs
Weekly research eliminated
Now continuous and automatic
01The opportunity
A maxed-out team, and revenue leaking through the gaps.
City Company is a US-asset-based freight and trucking carrier operating across the US and Canada — its own fleet plus owner-operators, running everything from LTL to full truckload, reefer, open deck, and drayage. Like most mid-size carriers, growth was capped not by demand but by capacity: the sales and dispatch team was maxed out, leads were slipping through before anyone could act on them, marketing spend was going to waste because nobody followed up fast enough, and the leadership team was spending its time firefighting instead of growing the business.
Decision-maker research alone consumed more than 20 hours per week of sales-team time.
The underlying problem wasn't a shortage of leads — City Company, like most carriers, already had lists. The real gap was research and speed: nobody had time to confirm who the actual decision-maker was, what phone number would reach them, what their profile was, or whether they were even in the market right now. Finding that out manually, per contact, was consuming 20+ hours a week of a team's time that should have been spent talking to buyers.
02The approach
An AI operations system, not a lead list.
The system is not a lead-generation tool in the traditional sense — MagnaQore doesn't hand City Company a new list of names. It enriches and works the contacts City Company already targets, using data that would otherwise take the team 20+ hours a week to assemble by hand, and then runs the entire outreach-to-qualification cycle automatically.
- 01
Contact enrichment
Every target contact is run through automated enrichment before any outreach happens: decision-maker names and titles identified automatically; phone numbers and email addresses verified against 50+ data sources; revenue, employee count, growth signals and tech stack pulled in; and relationship mapping showing who reports to whom, plus alternative contacts if the primary target isn't reachable.
- 02
Intent signals, not guesswork
The system aggregates signals that indicate a company might need freight services soon, so outreach is timed against real buying intent rather than a cold list: new job postings signalling growth, leadership changes such as a newly hired VP of Operations, and lookalike matches against City Company's existing customer profile.
- 03
Multi-channel, personalized outreach
Campaigns launch automatically across LinkedIn, email, and SMS where relevant — every open, reply, click, and meeting booking tracked in a live events dashboard. Per segment the system maintains up to 10 LinkedIn message variations, two email sequences, three SMS templates, and supporting materials ready to send with no manual copying and no forgotten follow-ups.
- 04
AI voice agent
Once a contact is warmed up, an AI voice agent takes over — calling, navigating receptionists, identifying the decision-maker, and running a structured qualifying conversation covering cargo volume, routes, current providers, active tenders, and interest level. Every call produces a full transcript and summary, and the agent books meetings or gathers proposal requirements directly.
- 05
Tender tracking and operational visibility
Beyond outreach, the system watches for tenders before their deadlines and surfaces high-priority notifications the moment something changes. Dispatchers see live load data on a map; managers get commissions auto-calculated instead of worked out by hand.
A typical morning at City Company
- 47 new prospects added overnight, with full contact details and decision-maker names.
- 230 personalized emails already sent across 8 segments — each one unique and tailored.
- 12 LinkedIn connection requests accepted; 6 replies asking for proposals.
- The AI voice agent made 83 calls, qualified 19 leads, scheduled 4 meetings, and delivered full call transcripts.
- 3 high-priority notifications: a target company posted a tender, a logistics manager changed roles, a prospect replied.
The sales team walks in to a list of warm, qualified leads — not a cold contact list to work through.
03The impact
From a ten-person sales operation to two people, at the same revenue.
City Company restructured its commercial operation around the system. Managers and brokers were no longer needed to carry the volume the AI system now handles — the company retained a dispatcher and a technical specialist responsible for keeping the AI system running, with the founder able to run the business essentially alone if needed.
Monthly output, AI system vs. human brokers
| Metric | AI system | Human brokers |
|---|---|---|
| Contacts worked / month | 2,000–4,000 | 2,000–4,000 (same pool) |
| Warm leads produced | 3 | 3 |
| Potential leads produced | 30 | 7–8 |
| Total opportunities / month | 33 | ~10 |
| Team required to run it | 1 dispatcher + 1 technical specialist | 5–10 managers / brokers |
Warm leads move into active sales work within a week; potential leads convert within a month — giving City Company a predictable, two-speed pipeline instead of a single undifferentiated contact list that everyone worked at the same intensity.
Business impact, summarized
- Reduced operating costs — a 10-person sales/dispatch function reduced to 2 people at unchanged revenue.
- Reduced process time — decision-maker research that took 20+ hours/week of manual work now runs automatically, continuously, in the background.
- Increased sales capacity — 3× the deal output of human brokers working the identical contact list.
- Built internal capability — City Company now runs the system with one technical specialist rather than depending on an external vendor for day-to-day operation.
- Established operational visibility — live dispatcher load maps, auto-calculated manager commissions, and zero-miss tender tracking replacing manual, ad hoc processes.
→Result
The outcome
- Commercial operation reduced from 10 people to 2 at the same revenue
- 3× more deal output from the same target list
- 33 monthly opportunities generated by the AI system compared with approximately 10 through human brokers
- Continuous automated contact research and validation
- Tender alerts and real-time opportunity signals
- Internal technical capability to run the system without day-to-day vendor dependency
About these case studies
These case studies are based on real MagnaQore client engagements. Certain company names, figures, technical details, and operational information may be simplified, anonymized, or aggregated to respect confidentiality obligations.
They are provided to illustrate the type of AI Organization Transformation work MagnaQore delivers. They do not constitute a guarantee of results, a public offer, or a complete representation of a client's internal systems, strategy, or financial performance.
Additional technical and commercial detail may be shared for qualified due-diligence purposes under a mutual non-disclosure agreement.
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