Proof of what we build
We don't just pitch services. These are real products — designed, engineered, and operated by our team.
Products we design, build, and operate
The same engineering we apply on client work — architecture reviews, CI/CD, AI integration, production monitoring — is what runs these products daily.
Evizra — AI Hiring Platform
evizra.com · Built and operated by Plattr Tech Studio
The Challenge
Traditional hiring is slow, inconsistent, and biased by first impressions. Recruiters spend hours on early-stage screening calls only to discover candidates who looked strong on paper are not a fit — or miss candidates who interview poorly but perform well. The market for AI-assisted hiring was growing fast, but most tools either scored resumes mechanically or replaced human judgment with opaque models.
We wanted to build something different: an AI that could conduct a real, adaptive conversation with each candidate — one that adjusts to their specific resume and the job's actual requirements — and then deliver a structured intelligence report that a recruiter can actually trust and act on.
5–7 min
Personalized AI voice interview per candidate
0 scheduling
Candidates interview on their own time, no calendar coordination
100%
Evidence-backed reports — every score has a transcript quote
What We Built
AI Job Analysis
Paste a job description — Evizra extracts seniority level, must-have skills, responsibilities, and evaluation criteria automatically. No manual setup.
Bulk Resume Intake & Parsing
Upload a folder of PDFs. Each resume is parsed into a structured profile — skills, experience, projects, technologies — instantly and consistently.
AI Voice Screening Interviews
Each candidate gets a personalized voice interview generated from their specific resume and role requirements. Natural adaptive conversation, not rigid Q&A scripts.
Candidate Intelligence Reports
Auto-generated after every interview: overall match rating, competency breakdown, strengths, risks, and evidence quotes from the conversation. No black box.
Candidate Management & Filtering
Searchable, filterable table across all candidates. Filter by match rating, interview status, contact status. Full transcripts available. One-click CSV export.
Pay-As-You-Go Billing
Prepaid token model with Starter, Growth, and Scale packages. Full usage ledger, CSV export, and downloadable invoices. No hidden fees or seat commitments.
Engineering Stack
Glitze — AI Commerce Platform
glitze.in · Built and operated by Plattr Tech Studio
The Challenge
Growing businesses — especially those in retail and direct-to-consumer commerce — run their sales, marketing, and customer data across a patchwork of disconnected tools. CRM in one spreadsheet, email campaigns in another, customer orders somewhere else. There's no single view of a customer's journey, and the manual effort of keeping everything in sync kills productivity as the team scales.
We built Glitze to unify sales pipeline management, marketing campaign execution, and customer relationship management in a single platform — with AI features layered in where they actually reduce work rather than add complexity.
Unified
Sales, marketing, and CRM in one platform — no sync required
AI-assisted
Deal insights, campaign suggestions, and customer scoring
Built for scale
Designed from the ground up for growing commerce businesses
What We Built
Visual Sales Pipeline
Kanban-style deal tracking with custom stages, deal values, and close-date forecasting. Full activity history on every deal.
Marketing Campaign Management
Plan, schedule, and track campaigns across channels from one interface. Campaign performance is connected directly to customer records and deal activity.
Unified Customer Records (CRM)
Every customer contact, purchase, and interaction in one record. Link contacts to deals and campaign activity — no manual stitching between tools.
AI Deal Intelligence
AI-assisted deal health scoring, win-probability estimates, and next-action suggestions based on activity patterns and historical close data.
Reporting & Analytics
Revenue forecasting, pipeline velocity, campaign ROI, and customer segment analysis — all in one reporting layer without a BI tool bolt-on.
Workflow Automation
Trigger-based automation for deal stage transitions, follow-up reminders, campaign enrollment, and internal notifications — configured without code.
Engineering Stack
Client work
A representative example of how we approach client engagements — from discovery through delivery.
AI-Powered Ecommerce Platform Rebuild
Mid-market retailer · Legacy platform migration + AI integration
The Situation
A mid-market retailer was running their ecommerce operations on a 10-year-old platform that had been patched and extended past its limits. Checkout conversion was declining, mobile performance was poor, and every product catalog update required developer involvement. The team had been promised a replatform twice before — both times it stalled because the scope ballooned before any code shipped.
They came to us not just to rebuild, but to ship something useful in 90 days — a working storefront, a manageable product catalog, and enough foundation to add features without a full team. AI-assisted product search and recommendations were a stretch goal for phase two.
90 days
To first production deployment — fully functional storefront
3× faster
Page load times on mobile vs. the legacy platform
Zero downtime
Migration from old platform with parallel traffic cut-over
What We Delivered
- Next.js storefront with headless commerce architecture — product team manages catalog without engineering
- Parallel-run data migration with zero customer-facing downtime during cut-over
- Semantic AI product search using vector embeddings — customers find products with natural language queries
- Order management, inventory sync, and returns flow rebuilt as a clean microservice layer
- Full observability stack — error tracking, performance monitoring, and alerting from day one
How We Approached It
We ran a 2-week discovery to define scope before writing a line of code. The client had grown used to scope creep killing previous replatform efforts — so we fixed the 90-day scope in writing and left phase-two features explicitly out.
We used a strangler-fig approach: routing traffic from specific product categories to the new platform while the legacy system ran in parallel. This let us validate the new checkout in production with real customers before committing to full cut-over.
The AI search feature shipped in week 11 of the 13-week project — earlier than scoped, because the headless architecture made it straightforward to add without touching checkout or catalog flows.
Engineering Stack
Ready to talk about what you're building?
A discovery call is a conversation about your situation — not a sales pitch. We'll tell you honestly whether we're the right fit.
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