Real Results. Real Businesses.

Deployed over 3 weeks using a multi-agent architecture. The lead capture agent monitors their website contact form, Facebook Messenger, and Google Business Profile simultaneously. When a lead comes in, the triage agent determines intent (booking vs. question vs. complaint) and routes accordingly. The booking agent integrates with their calendar and sends real-time availability. We used a fine-tuned LLM trained on 6 months of their past conversations to match their tone and service knowledge. The system handles Serbian, English, and code-switching between them naturally.

Within 30 days, response time dropped from 14 hours to 2 minutes. Monthly revenue increased by $12,000 from recovered leads alone. The owner, Tomislav, now spends Fridays on strategic growth instead of administrative catch-up. The system paid for itself in the first 11 days.

The system crawls 14 data sources daily, scoring each prospect on purchase intent (based on property views, listing age, price changes, and social signals). The communication engine A/B tests subject lines, message length, and send timing — continuously optimizing. We integrated with their existing CRM (Baton) and set up a dashboard showing real-time pipeline value. The entire system runs on a $47/month server and handles all three agents' outreach simultaneously.

Each agent now contacts 200+ prospects per week with zero manual work. The system books an average of 14 qualified meetings per week across the team. In the first 60 days, they closed $47,000 in commission from AI-sourced leads. The managing broker estimates they'll triple their team size within 12 months without increasing admin overhead.

Built over 14 weeks with a team of 4 (2 frontend, 1 backend, 1 ML engineer). The frontend uses Next.js 14 with ISR for sub-second page loads. The recommendation engine processes 40M+ events/month using a hybrid collaborative filtering + content-based approach. We migrated 200K+ products, 1.2M customer accounts, and 8 years of order history without a single hour of downtime. The platform handles 50K concurrent users during flash sales without degradation.

Revenue grew 147% in 6 months. Page load time dropped from 6.2s to 0.8s. Cart abandonment fell from 78% to 16%. Mobile conversion tripled. The AI recommendation engine drives 42% of all purchases. TechFlow's CTO estimated the ROI at 11:1 within the first quarter.

Each client gets a custom-trained AI model on their existing content library (average 50-200 articles). The system generates first drafts, then the human editor reviews and publishes in 20 minutes instead of 4 hours. We integrated with their existing RankMath and Yoast setups. The keyword discovery engine monitors Google Search Console data and competitor moves daily, suggesting new topics with predicted difficulty and traffic potential.

Content output went from 4 articles/week to 24 articles/week with the same team. Organic traffic across all client accounts grew an average of 312% over 5 months. One client hit 47 keywords in the top 10 positions. GrowthCo retained 100% of their clients and signed 5 new accounts based on the results. Their monthly retainer revenue doubled.

The 8-week process started with deep stakeholder interviews to uncover FutureLab's brand essence: "making the complex accessible." The visual identity uses a custom geometric typeface paired with generative gradient patterns that shift based on page context. The website features 3D interactive elements that explain their methodology, video case studies with client testimonials, and a proprietary "innovation readiness" assessment tool that generates leads. Every touchpoint — from email signatures to proposal templates to their Notion workspace — was redesigned.

Brand recall in their target segments increased 185%. Website bounce rate dropped from 68% to 25%. Average session duration more than doubled. Their pitch win rate went from 30% to 67% in 4 months. FutureLab was featured in two design publications and their CEO reported that the new brand "opened doors that were previously closed."

Built with React Native (Expo) for rapid iteration across both platforms. The AI engine uses a time-series anomaly detection model trained on 2M+ health data points. The Bluetooth layer handles device pairing, data sync, and offline storage for up to 30 days of readings. Backend uses Node.js microservices on AWS with HIPAA-compliant encryption. The app includes push notification reminders, family sharing for caregivers, and exportable PDF health reports for doctors. We delivered the MVP in 11 weeks — one week ahead of the investor deadline.

52,000 downloads in the first month with zero paid marketing. 4.8-star average across both app stores. 73% 30-day retention rate (industry average is 30%). StartupXYZ secured €1.2M seed funding at a higher valuation than projected, with investors specifically citing the app quality as a deciding factor.

The quote generator uses a database of 200+ service types with region-specific pricing rules. When a lead comes in via web form, Google Business Profile, or phone (transcribed by AI), the system identifies the service type, property size, and extras, then generates a tiered quote (basic, standard, premium). The dispatch engine uses a simple constraint solver that considers crew location, equipment needs, job duration, and customer time preferences. The system integrates with their existing accounting software for seamless invoice reconciliation.

Quote turnaround dropped from 3 days to 60 seconds. Lead conversion increased from 18% to 63%. Admin hours dropped from 25/week to 3/week. Monthly savings of $4,200 in administrative costs. Mateo scaled from 4 crews to 7 crews in one season without hiring any office staff. The system paid for itself in the first 19 days.

The system ingests data from LinkedIn Sales Navigator, Crunchbase, company websites, news feeds, and their existing CRM. For each prospect, it generates a personalized research brief (company news, growth signals, tech stack, decision-maker insights). The email generator creates 5 unique message variants per campaign, referencing specific details about the prospect's business. The deliverability engine manages sending limits, warm-up periods, and spam score monitoring across multiple domains. Integration with HubSpot keeps everything synced in real-time.

Outbound volume went from 200 to 2,400 emails per week with the same team of 5 SDRs. Meeting booking rate held steady at 22% (industry average for cold outreach is 3-5%). Pipeline value grew 320% in 90 days. They closed 14 new enterprise accounts worth $1.8M in ARR within the first quarter. The SDR team now focuses exclusively on high-value conversations instead of manual research.

The 10-week UX process began with a discovery phase: shadowing lawyers as they used the platform, analyzing session recordings (300+ hours), and mapping every user journey. The biggest insight: lawyers think in terms of legal outcomes, not software features. We redesigned the entire navigation around practice areas (\"I need a contract\" instead of \"Document → Templates → Commercial → Agreements\"). The new onboarding uses a \"first document in 5 minutes\" challenge that walks users through their first contract with smart defaults and inline guidance. We implemented undo/redo everywhere — lawyers' #1 anxiety was making irreversible mistakes.

30-day retention doubled from 28% to 59%. First-document completion rate went from 27% to 84%. Support tickets dropped 58% because users could self-serve. NPS jumped from +12 to +53. LegalEase's monthly recurring revenue grew 89% in the 3 months after launch, primarily from reduced churn and increased referrals from happy users.