Five systems. Five sets of numbers.
Every case study on this page leads with the outcome metric. Anonymous clients are identified by industry and use case, not invented names. Every number is real.
80% reduction in manual order processing.
The CEO sees the entire business in real-time.
SuperBinz — multi-location retail/liquidation chain, Canada. Named client.
Gregory Van Duyse, CEO of SuperBinz, was running a multi-location retail operation on disconnected tools. Orders were processed manually across locations. Loyalty programs required staff intervention for every redemption. Marketing campaigns took days to deploy. And Gregory could not see his business without waiting for end-of-week reports assembled by hand.
The team was capable. The manual process was not. Revenue was growing but operational capacity was not keeping up, a bottleneck at exactly the $2M–$10M ARR stage where manual systems start to break.
A full operations engine across five integrated systems: QR code and POS integration for automated order routing, 100%-automated loyalty program (earn, track, redeem without staff intervention), marketing hub with segmented campaign automation, an executive real-time dashboard showing all locations in one view, and a centralized error-handling and alerting system.
The infrastructure was built to handle 3x current transaction volume without additional headcount.
"The AI automation engine built by Calyber AI has changed how we operate. What used to take our team days now happens automatically. I can see our entire business at a glance and make decisions in real-time instead of waiting for weekly reports."
10x outreach per sales rep.
3x pipeline in the first quarter.
B2B SaaS company, enterprise email platform. Anonymous client — identified by vertical and use case.
A B2B SaaS sales team was spending the majority of its day on manual outbound work: finding prospects, researching companies, writing personalized emails, following up. Each rep could send 20–30 personalized outreaches per day at full effort. The pipeline was growing slowly despite a strong product.
The pain was not hiring or skill. It was leverage. Each rep was capable of building relationships and closing deals. They were spending their time doing work that should be automated.
A complete outbound engine: automated ICP validation and lead list generation using Apollo.io integration, email verification pipeline, AI-powered personalized ice-breaker generation for each prospect, multi-touch email sequencing via Instantly.ai, and a response detection and routing system that alerts reps only when a conversation needs a human.
Each rep now reviews a daily prioritized list of warm responses instead of spending the day on prospecting. 5x sales team capacity. Same headcount.
100+ candidates screened per day.
Was 10–15 with a full HR team.
HR team at a talent acquisition organization. Anonymous client — identified by vertical and use case.
The HR team at this talent acquisition organization was spending 30–45 minutes per screening call: scheduling, running the call, taking notes, scoring candidates against a rubric, and entering results into the ATS. At 10–15 candidates per day, the team was at capacity. Any hiring surge and the process broke.
The subjective scoring problem was equally costly. Two HR staff could watch the same interview and score the same candidate differently. Inconsistent screening meant inconsistent hiring quality.
An AI voice screening agent built on Bolna.ai, capable of conducting natural-language phone screenings 24/7. Dynamic questioning logic adapts based on candidate responses. The agent scores candidates against the employer's rubric in real-time, generates structured evaluation notes, and syncs results directly into the ATS via API.
Candidates who requested a human review were flagged and routed directly to the HR team within minutes. False positive rate below 5%. Candidate satisfaction 4.2/5 across all AI-conducted screens.
10x candidates sourced per week.
75% lower cost-per-hire.
Recruiting firm, talent acquisition vertical. Anonymous client — identified by vertical and use case.
Recruiters at this firm were spending 60–70% of their time on LinkedIn sourcing: manually searching profiles, manually evaluating fit, manually personalizing connection requests, and manually following up with non-responders. At 20–30 sourced candidates per week per recruiter, pipeline fill rate was a constant constraint.
Recruiter burnout from repetitive search work was a secondary problem. The manual process was sustainable only up to a volume that did not meet client demand.
A LinkedIn sourcing and outreach system using PhantomBuster for profile discovery and data extraction, AI-powered candidate scoring against a configurable fit rubric, personalized outreach message generation for each profile using GPT-4, and a multi-touch sequence manager with response detection and routing.
Recruiters now review AI-scored profiles and respond only to warm leads. Sourcing time dropped from 60–70% of their day to under 15 minutes of review.
15 minutes per video. Was 4–8 hours.
300% organic reach growth.
Digital marketing agency, short-form video production. Anonymous client — identified by vertical and use case.
A digital marketing agency producing short-form video content for clients was spending 4–8 hours per video on a manual production workflow: scripting, sourcing B-roll and images, generating motion graphics, recording voice-over, editing, and publishing to each platform.
Client demand was outpacing production capacity. The bottleneck was not creative quality. It was the ratio of human hours to output. More clients meant more hours, and there were no more hours to give without hiring a production team.
An AI content pipeline: AI-generated scripts from brief inputs, automated text-to-image generation for B-roll, image-to-video synthesis for motion, AI voice-over generation via ElevenLabs, automated assembly and captioning, and direct publishing to client social accounts.
The human creative role shifted from production to direction. The creative team reviews scripts and approves output before publish. Production time per video dropped from 4–8 hours to 15 minutes of human-in-loop review.
Your use case is next. Tell us the bottleneck.
None of these five clients knew exactly what their sprint would produce before the Day 0 scope call. That is what the call is for. 30 minutes. No commitment.