Real challenges. Measurable results.

We spent two weeks embedded with the operations team, documenting every manual step, every copy-paste, every email sent to trigger an action. The result: a complete process map with 47 identified automation opportunities.

We designed and deployed a middleware layer connecting all three systems via API. Custom AI agents handle exception routing, when something breaks the normal flow, the right person is notified with full context, not just an error code.

Four weeks of parallel running, daily stand-ups with the team, and iterative refinement. We handed off a fully documented system that the internal team can maintain and extend without us.

We interviewed every rep and sales manager to understand what data actually mattered. Then rebuilt the CRM data model from scratch, fewer fields, better structure, mandatory only what’s truly necessary.

We trained a scoring model on 3 years of historical deal data. Every lead now gets a score (0–100) with the top 3 reasons. Reps focus on the right opportunities. The model retrains monthly.

Personalized follow-up sequences triggered by deal stage, last activity, and lead score. Reps get a daily digest of who to call and why. No more falling through the cracks.

We analyzed 5 years of the brand’s best-performing content to extract tone, vocabulary, sentence structure, and topic patterns. This became the foundation of the AI writing model, not generic GPT output, but content that sounds like them.

A custom workflow: keyword research → brief generation → AI draft → human review → publish. The human review step takes 15 minutes per article instead of 4 hours. The team approves, edits tone, and hits publish.

Every piece of content is tracked. The model learns from what performs, articles that rank, emails that convert, posts that engage. Monthly retraining keeps quality improving over time.

We mapped every client touchpoint across all 6 systems, identified the 20% of features used 80% of the time, and designed a unified information architecture. The goal: one login, one interface, everything accessible.

12 weeks from wireframe to production. React frontend, custom API layer connecting all backend systems, AI-powered support chat that resolves 70% of queries without human intervention. Mobile-first, fully accessible.

We migrated clients in cohorts of 500, with a dedicated support line during each phase. Parallel running for 8 weeks. Zero data loss. The old systems were decommissioned on schedule.

6 weeks of customer discovery interviews, competitive analysis, and pricing experiments. We validated three distinct buyer personas and identified the minimum viable feature set that would justify a $15K/year subscription.

React frontend, Node.js backend, Stripe billing, multi-tenant architecture. The methodology was encoded into a structured assessment engine with automated scoring, benchmarking, and report generation. First 5 paying customers before launch.

We built the growth infrastructure alongside the product: SEO-optimized content, automated onboarding sequences, in-app analytics, and a referral program. The consulting firm’s existing client base became the first growth channel.

We ran a data governance workshop with all department heads to agree on canonical definitions for every key metric. Then built a central data warehouse that all source systems feed into. One definition. One number. Everyone aligned.

Custom dashboards for each executive role, not one dashboard for everyone, but role-specific views with drill-down capability. Anomaly detection alerts when a metric moves outside expected ranges. Predictive models for demand forecasting and inventory optimization.

We redesigned the Monday morning meeting around the new dashboard. Automated weekly briefings sent to each executive Friday afternoon. The data team now spends 80% of their time on analysis and modeling, not report production.