Proof We Deliver

Results, not promises

Every case study below is a real engagement. Real numbers. Real companies that were losing money to broken AI before they called us.

$47K
avg. annual savings
171%
avg. portfolio ROI
Up to 420%
top-client ROI year one
89%
failures auto-recovered
Real Estate

60% reduction in lead response time

Ottawa Real Estate Group

ServiceCustom Agent Build + Managed Operations
Investment$38,000 build + $9,000/mo managed
ROI311% in year one

The Challenge

Ottawa Real Estate Group was processing over 400 inbound leads per month across seven agents. Response time averaged 4.2 hours — long enough that leads were going cold and choosing competitors. Their lead routing system was a patchwork of manual Slack notifications and spreadsheet tracking. One agent's vacation could back up leads for days. Worse, they'd tried to automate with a simple chatbot the year before. It crashed every few weeks, left leads in a dead-end conversation flow, and the team spent more time managing the bot than it saved them.

The Solution

We built a three-agent pipeline: a classifier agent that categorized inbound leads by property type, price range, and urgency; a routing agent that matched leads to the right sales agent based on specialty and current workload; and a follow-up agent that sent personalized initial responses within 8 minutes while the human agent was being notified. Every agent was built on the Nexus self-healing framework. When the classifier hit a rate limit at 11pm, it backed off and retried automatically. When the routing agent received malformed data from their CRM, it detected the error, logged it with a full diagnosis, and routed to a fallback handler — instead of crashing and blocking 40 leads.

Results

60%
Reduction in lead response time (4.2h → 1.7h)
18%
Increase in lead-to-showing conversion rate
0
Agent-caused lead loss in 6 months post-launch
$127K
Additional revenue attributed to faster response

"Before Nexus, our lead routing agent would crash every few days and nobody would notice for hours. New leads were going cold. Now it just... runs. We've had zero downtime in three months and our conversion rate is up 18%."

JM
Jennifer Malik
VP Operations, Ottawa Real Estate Group
Legal Services

Automated document processing saves 120 hours per month

Smith & Associates Law

ServiceAI Agent Audit + Custom Agent Build
Investment$12,000 audit + $44,000 build
ROI420% in year one

The Challenge

Smith & Associates is a 40-person Ottawa law firm handling primarily real estate, corporate, and estate law. Their intake process required paralegals to manually review incoming documents, extract key information into their case management system, flag urgent items, and route files to the right lawyer. This process consumed approximately 120 paralegal hours per month — roughly $9,600 in labor at their billing rate. More critically, the manual nature created a backlog: documents received after 4pm weren't processed until the next morning, which caused downstream delays and occasionally missed deadlines.

The Solution

The audit identified document processing as the highest-ROI opportunity. We built a four-agent document processing pipeline: an OCR extraction agent that processed incoming PDFs; a classification agent that identified document type (deed, will, corporate filing, etc.) with 94% accuracy; an extraction agent that pulled structured data into their practice management system; and a triage agent that flagged urgent documents and notified the right lawyer via Slack. The pipeline runs 24/7. Documents received at 2am are processed and waiting in the lawyer's queue when they arrive at 8am. The self-healing framework handled the firm's legacy scanner, which produced occasionally malformed PDFs — rather than crashing, the extraction agent would apply a fallback OCR strategy and log the anomaly for review.

Results

120hrs
Paralegal hours saved per month
94%
Document classification accuracy
8min
Average document processing time (was 2 hours)
$115K
Annual labor cost savings

"We were skeptical. Law firms don't move fast on new tech. But the ROI projection Nexus gave us was too compelling to ignore. Document intake that used to take a paralegal two hours now takes four minutes. And when the system has an issue, it fixes itself."

RC
Robert Chen
Managing Partner, Smith & Associates Law
SaaS / Technology

Self-healing support bot handles 89% of tickets automatically

TechStart Inc

ServiceCustom Agent Build + Managed Operations
Investment$29,000 build + $7,500/mo managed
ROI508% in year one

The Challenge

TechStart is a 60-person SaaS company with a developer productivity tool used by 8,000 active teams. Their support volume had grown to 1,200 tickets per month, but the support team was only 3 people. Average resolution time was 18 hours. Churn analysis showed a direct correlation between slow support response and cancellations in the first 90 days. They had tried two AI support solutions in the previous year. The first was a static FAQ bot that couldn't handle nuanced questions. The second was a more capable AI system that produced excellent answers but crashed under load twice in the first month — requiring a full restart each time, which left hundreds of tickets unresolved for hours.

The Solution

We built a three-tier support agent system: a triage agent that classified incoming tickets by category (billing, technical, account, feature request) and urgency; a resolution agent with deep knowledge of TechStart's product, documentation, and known issues — capable of handling the full lifecycle of a support conversation; and an escalation agent that prepared full context summaries for the human support team on tickets that required human judgment. The key technical challenge was reliability. We implemented circuit breakers at every external API integration point (their ticketing system, their product database, their account management system). When any integration failed, the agent degraded gracefully rather than crashing. The self-healing framework recovered 97% of infrastructure failures automatically, with only 3% requiring manual review.

Results

89%
Tickets handled fully automatically
4.8min
Average first response time (was 18 hours)
97%
Infrastructure failures recovered automatically
31%
Reduction in 90-day churn

"We tried two other AI support solutions before Nexus. Both fell apart in production. The Nexus team was the first to show us a live demo where they deliberately broke the agent mid-conversation and watched it recover on its own. That's when we knew."

ST
Sarah Thompson
CTO, TechStart Inc

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