When a mid-sized e-commerce retailer needed AI-powered personalization at scale, they faced a problem most businesses of their size share — the capability gap between knowing what to build and having the team to build it. They didn't get a referral. They ran a Google search, landed on Goodfirms artificial intelligence companies' listing page, and found Plavno. What followed was a 12-week engagement that produced a recommendation engine running at 94.2% accuracy and $124,500 in measurable revenue lift.
This case study covers what the client needed, what Plavno built, and the results.
Company: Plavno
Client Industry: E-commerce / Retail
Lead Source: Goodfirms (organic Google search)
Platform Built: Nexus AI Engine · Retail Analytics & Personalization
Plavno Spokesperson: Alisa Shevialevich, Plavno
The Results @ a Glance - The Full Story Unfolds Below
In just under three months, Plavno's project, Nexus AI Engine, moved from concept to production, delivering measurable improvements in revenue, prediction accuracy, and system performance.
Nexus AI Engine — Overview Dashboard showing live recommendation accuracy, revenue lift, CLV prediction, and active deep models
The Challenge That Started It All: Personalization at Scale Without an Internal Team
Mid-sized e-commerce businesses face a persistent gap: AI-driven personalization has been proven to deliver revenue lifts of 10–30%, according to McKinsey, but building the infrastructure requires a machine learning team that most companies of this size simply do not have.
That was exactly where this client stood. They had a clear goal — personalize the storefront experience for every visitor in real time — but no clear path to build it.
Previous providers had fallen short in three areas: limited AI depth, generic delivery approaches, and insufficient confidence in end-to-end execution.
They searched Google for a development partner who could close the gap. Goodfirms came up. Plavno’s profile was on it.
Technical and Business Challenges
| Technical Challenges | Business Challenges |
|---|---|
| No clear architecture path for a scalable ML- powered personalization platform |
Internal teams are unable to move at the desired pace without a reliable technical partner |
| Uncertainty around model selection, training pipelines, and real-time inference at scale |
Planning and onboarding cycles are extending due to delivery uncertainty |
| Risk of costly rework if foundational data and API decisions were made incorrectly |
Speed to market is constrained by the absence of a full ML-to-frontend team |
| Need to integrate live behavioral signals, segmentation clusters, and storefront rendering into one system |
Previous providers fell short: limited AI depth, generic delivery, insufficient end-to- |
The urgency was real. Research from Epsilon finds that 80% of consumers are more likely to purchase from brands that offer personalized experiences — and the client’s competitors were already moving in that direction.
Where Goodfirms Entered the Decision Journey
The client wasn’t referred. Nobody made an introduction. They typed a search query, Goodfirms appeared, and Plavno’s profile was on it.
Before getting in touch, they spent time on the profile — going through past work, reading what previous clients had said, and getting a sense of whether Plavno understood the kind of problem they were trying to solve. By the time they reached out, the trust-building had already happened.
“The Goodfirms lead experience felt different because the client came in with much
stronger initial intent and a higher level of trust. Unlike cold inquiries, where we usually
spend the first calls explaining who we are, this client had already reviewed our profile
before contacting us.” — Alisa Shevialevich, Plavno
What Made the Profile Convert
When Alisa Shevialevich reflected on why this particular lead converted, three profile elements stood out — none of which required a sales call to deliver.
| Trust Signal | What It Demonstrated |
|---|---|
| Reviews & Ratings |
Reduced perceived risk; validated consistent delivery quality across complex engagements |
| Portfolio & Case Studies |
Proved ability to handle complex, high-responsibility domains end-to-end |
| Positioning Consistency |
Showed a coherent AI and custom software identity — signaling a specialist, not a generalist |
Goodfirms did not close the deal. But it shaped the conditions under which the deal could happen—aligning visibility with credibility before any direct interaction.
What Plavno Built: The Nexus AI Engine
During discovery, it became clear that a standard development model would create limitations at scale. The client needed a platform that could run multiple recommendation models in parallel, surface real-time behavioral signals to influence storefront layout, and support ongoing A/B testing without interrupting live traffic.
Plavno moved from a conventional build path to a custom ML architecture and modular delivery model — aligning every technical decision directly with the client’s operational and business goals.
Platform Architecture: Three Integrated Views
- Overview Dashboard — Real-time metrics including recommendation accuracy (94.2%), revenue lift ($124,500), average CLV prediction ($840), and 12 active deep models
- Model Management — Active and training model registry with F1 scores, live training
curves, A/B test management, and AI-generated optimization suggestions - Live Storefront — Real-time simulation of how the AI engine personalizes the storefront per active session, with predicted segment, checkout probability, and cart value visible per user
Live Storefront Personalization
The Live Storefront view gives operators a real-time simulation of exactly how each active session experiences the platform — predicted user segment, estimated checkout probability, current cart value, and the real-time triggers driving AI recommendations.
Behavioral Analytics and Segmentation
The platform’s behavior heatmap and AI segmentation cluster views give operators live visibility into how different user cohorts are interacting with the storefront — enabling rapid intervention and optimization without waiting for batch reporting cycles.
Managing Constraints
Breaking work into clear priorities, sequencing business-critical ML functionality first
Maintaining a delivery structure that preserved momentum without sacrificing the model
quality or system integrity
Balancing speed with architecture decisions that would not require costly rework as the platform scaled
Transparency and Communication
Plavno maintained a structured delivery process throughout: Jira for project management, Slack for direct communication, regular sync meetings to review progress and blockers, and milestone-based reporting to give stakeholders continuous visibility.
Results: What the Nexus AI Engine Platform Delivered
| 94.2% | $124,500 | $840 | 12 |
|---|---|---|---|
| Recommendation Accuracy |
Revenue Lift (AI- driven) |
Avg. CLV Prediction | Active Deep Models |
Operational Impact
- Faster, more structured path from model experimentation to production deployment
- Reduced reliance on manual merchandising — AI drives personalized product ranking in real time
- Live A/B testing framework enables continuous model improvement without interrupting live traffic
- Segmentation clusters give operators actionable cohort visibility without batch reporting delays
- Platform built to scale — architecture supports expanding model registry and growing user base without rework.
Performance Scorecard
| Dimension | Score | Max |
|---|---|---|
| Onboarding Speed | 4 | 4 |
| Operational Efficiency | 4 | 4 |
| System Performance | 4 | 4 |
| Model Accuracy | 4 | 4 |
| Customer Experience | 4 | 4 |
“The client knew the project was being handled by a team that understood both the technical complexity and the business context — which made the entire process feel more predictable, transparent, and manageable.” — Alisa Shevialevich, Plavno
After Launch: From Vendor to Long-Term Technology Partner
The engagement did not end at launch. The client now treats Plavno as a long-term technology partner — involved in product decisions, not just executing a brief.
From a competitive standpoint, the client now has a personalization capability that most e-commerce businesses of its size do not. It was previously the kind of infrastructure only large platforms with dedicated machine learning teams could afford to build.
What This Means for Agencies on Goodfirms
Goodfirms opened the door. The client found Plavno through a search, spent time on the profile, found the trust signals, and felt confident enough to reach out. That is where Goodfirms’ influence helped Plavno step in and deliver.
The decision to hire came down to the quality of that first conversation, the clarity of Plavno’s thinking about the problem, and the sense that the team genuinely understood what the client was trying to build.
Alisa Shevialevich’s Advice to Other Agencies
- Treat a Goodfirms lead differently from a cold one. The person reaching out has already looked at your work. Skip the credentials pitch and get straight to the problem they're trying to solve.
- Reply fast and make the first call count. A quick response signals that you are on top of things. A focused first conversation signals that you understand their world.
- Let your profile do the heavy lifting before the call. Your reviews, case studies, and how you describe what you do are working for you before anyone picks up the phone.
Is Your Agency Listed on Goodfirms?
The client found Plavno because the profile was there, said the right things, and showed up when it mattered. If you're an e-commerce business looking for an AI development partner, browse verified agencies at Goodfirms AI Development Companies listing page. If you're an agency looking to be found the same way, get listed on Goodfirms today.
