Built, shipped, and running in production.

What changed after the demo.

Freedman InternationalMarketing localisationRead the case studyAI campaign localisation pipelineThe problemA wrong call early, such as a misread brief, travelled silently into every market and every output size, across thousands of standing brand rules.What we builtThe reasoning core of a multi-agent localisation pipeline: a structured client rule base, the agents that reason over it, and producer approval at key decision points.Multi-agent reasoning serviceClient rule knowledge baseOrchestration and human sign-offEnd to endfresh jobs passed every step, from brief to packaged multi-market, multi-format deliverablesEvery decisiontraced back to the rule behind it, with producer approval at key decision pointsOpen LoyaltyLoyalty softwareRead the case studyAI SDR and inbound sales agentThe problemInbound demand arrived faster than the team could qualify it, and a retried CRM write could record one conversation as two leads.What we builtA production sales agent that qualifies inbound leads, answers product questions from a live knowledge base, and writes structured outcomes back to the CRM.Lead qualificationSource-grounded answersCRM handoffNo duplicatesone CRM write per conversation turn, held by idempotency keys and a timeout regression testEvery turntraced and inspectable, so a wrong answer is debuggable after the factGamratManufacturingRead the case studyAI assistant for sales offersThe problemComplex offers can take two to three days as salespeople gather prices, check availability, and confirm delivery details.What we builtA proposed assistant turns customer emails into offer drafts using approved prices, availability and delivery details, then routes them to sales for review.Offer preparationPrice and availability checksSalesperson approval≤ 2 hours≤ 2 hours≤ 2 hoursproposed pilot target for a complete eligible offer draft. Most should take an hour or less.≥98%≥98%≥98%proposed accuracy target: product, quantity, price, and delivery details correct at first salesperson reviewShen.aiHealthcareRead the case studyPatient education assistantThe problemPatients could not read their own scan results, and in healthcare a confident wrong answer is a safety incident, not a bug.What we builtA patient-facing assistant that explains health scan results from an approved medical knowledge base. Low-confidence and clinical questions route to a clinician.Source-grounded answersConfidence-based routingHuman in the loop15%15%15%of conversations routed to a clinician; the rest answered from approved material40%40%40%fewer unnecessary clinician reviews after threshold tuningRyba Wooden ProductsManufacturingRead the case studyAI and operations auditThe problemAI ideas were stuck behind the records: every system described the same customer differently, so no automation could be trusted.What we builtAn audit and consulting engagement that prioritised practical AI opportunities, moved operational data to PostgreSQL, and helped the team select and implement a CRM.AI opportunity auditPostgreSQL foundationCRM implementation30% faster30% faster30% fasterprocess completion after the audit, the PostgreSQL move and the CRM went in42% fewer42% fewer42% feweroperational errors across the same processesWojasFootwear retailRead the case studySEO and GEO visibility platformThe problemVisibility was split between classic search and generative answers, so nobody could see where the brand actually stood or who was cited instead.What we builtA production platform that monitors search and AI citation visibility, compares competitors, and turns reports into content and strategy recommendations.SEO and GEO monitoringCompetitor analysisContent recommendations2% to 23%2% to 23%2% to 23%GEO visibility, the share of generative answers featuring the brand4 months4 months4 monthsfrom the first monitored baseline to that position