Case Studies
Fourteen real, completed engagements. Client names are withheld for confidentiality.
Recruitment firm, United Kingdom
A UK Recruitment Firm: From 5 to 50+ Active Postings Without Adding Headcount
The Problem
Manual candidate outreach was consuming hours of recruiter time every day, response delays were damaging the candidate experience, and the firm could only run a handful of active job postings at once because there was no capacity to follow up consistently. Records were also being updated by hand in Bullhorn CRM after every call.
What We Built
We assessed the recruitment workflow end to end, then designed and built an AI voice and chat system (using Retell AI) integrated directly with the firm's existing Bullhorn CRM through automated workflows. The system screens candidates over voice and text, keeps CRM records synchronised in both directions, reaches out across calls, SMS and chat, and routes qualified candidates to the right recruiter automatically, with a full audit trail.
The Result
The firm scaled from 5 to more than 50 active job postings at once without adding recruiting headcount. Average candidate response time dropped from roughly three days to about four hours. Engagement coverage reached effectively 100 percent, every candidate got a timely response, and the firm reports monthly savings in the region of six to eight thousand US dollars in recruiter hours previously spent on manual outreach and data entry.
Consulting firm, United States
A US Consulting Firm: Ending the Manual Form-to-Inbox Bottleneck
The Problem
The client services team was manually copying every website contact form submission into a spreadsheet, then drafting a reply by hand, often a near-identical one to the last. Inquiries were tracked across disconnected systems, which meant delays and inconsistent replies depending on who picked up the inquiry.
What We Built
We built an automated intake system that connects the firm's contact form directly to an AI categorisation layer, which reads each inquiry, sorts it by type, and drafts an appropriate first response. Every inquiry logs automatically to Gmail and Google Sheets, so the team has one consistent record instead of three scattered ones.
The Result
Client-facing responses now go out consistently and quickly instead of depending on whoever happened to see the form submission first, and the manual copy-paste step between the form, the inbox and the spreadsheet has been removed entirely.
Service provider, 50,000+ customer accounts
A High-Volume Service Provider: Bringing Order to 5,000+ Monthly Support Tickets
The Problem
With over five thousand support inquiries landing every month, manual ticket triage was consuming the team's time and knowledge that should have been easy to find was scattered, which slowed down resolution. Communication was fragmented across channels, there was no real-time view of what was actually happening, and compliance and audit requirements were creating real regulatory risk.
What We Built
We designed a twelve-agent AI support system: natural language processing to read and categorise incoming tickets, a connected knowledge base so agents (human and AI) can retrieve answers instead of re-researching them, an autonomous decision layer for routine decisions, and a chatbot with a full orchestration layer sitting over the top. Every system we build ships under the Adoption Standard: a full handover with documentation, not just a login and a dashboard.
The Result
Ticket handling moved from scattered and manual to structured and traceable, with a single system giving the team visibility into ticket status, resolution history and compliance records in one place.
Sales and marketing team, Dubai, UAE
A Dubai Sales Team: Closing the Gap Between Google Ads Leads and the CRM
The Problem
High volumes of Google Ads inquiries were arriving by email, and every one had to be read, understood and manually re-typed into GoHighLevel CRM. That manual step created delays, inconsistent follow-up, and leads that were lost simply because nobody got to them in time.
What We Built
We connected Google Ads lead capture directly to an automated workflow: an AI layer reads each inquiry, scores it, and pushes it straight into GoHighLevel with no manual re-entry, so the sales team sees a qualified, CRM-ready lead the moment it arrives instead of an email to transcribe.
The Result
Leads now move from ad click to CRM record without a human retyping anything in between, closing the exact gap that had been costing the team follow-up speed and, with it, deals.
E-commerce firm, United States
A US E-Commerce Firm: Automating Hundreds of Individual PayPal Payouts
The Problem
The finance team was manually processing hundreds of individual PayPal payments from a spreadsheet, one at a time. That created bottlenecks, a high error rate on payment amounts and currency conversion, and delays getting money out the door.
What We Built
We built a system that takes payout data straight from the intake form, validates it with an AI payment assistant, and pushes it through the PayPal Mass Payout API as a single batch instead of hundreds of manual entries, with automatic email notifications to recipients once paid.
The Result
Bulk payouts that used to mean a finance team member manually processing hundreds of line items now run as a validated batch, removing the manual data entry step that was the biggest source of both delay and error.
Marketing agency, United Arab Emirates
A UAE Marketing Agency: Taking the Manual Grind Out of SEO Content Production
The Problem
The agency's SEO content process was slow, manual and inconsistent from writer to writer. Keyword research, drafting and publishing were all separate manual steps, which meant skilled staff spent their time on routine production work instead of strategy, and the process could not scale without hiring more writers.
What We Built
We connected keyword research (via the SEMrush API) to an AI drafting layer built on the agency's own content rules, with drafts flowing through to WordPress for publishing and results tracked automatically in Google Sheets, replacing a chain of manual handoffs with one connected pipeline.
The Result
Content production moved from a fully manual, writer-by-writer process to a connected pipeline the team can scale without proportionally adding headcount, freeing the agency's strategists to focus on strategy rather than routine drafting.
Sales and marketing agency, Canada
A Canadian Sales & Marketing Agency: Automating Prospecting Without Losing Lead Quality
The Problem
Manual lead research and prospecting was slow and could not scale, and staff were spending their time on low-value data entry instead of actually talking to prospects. Worse, the resulting lists mixed qualified and unqualified contacts together, so sales time was still being spent filtering leads that should never have reached them.
What We Built
We built an automated prospecting pipeline: a scraper pulls business listings and contact details, an intelligent filtering layer removes unqualified prospects before a human ever sees them, and everything lands in a centralised, always-current Google Sheet on a schedule, with no manual research required.
The Result
The agency's team now works from a pre-qualified, continuously refreshed prospect list instead of spending hours building and cleaning lists by hand, freeing that time for actual client engagement.
Financial services provider
A Financial Services Provider: Cutting the Friction Out of Loan Applications
The Problem
Loan applications were getting stuck in manual verification, routing and CRM syncing, which frustrated applicants and drove up abandonment. Every extra manual step between application and approval was a chance to lose the applicant.
What We Built
We built a voice-powered intake system: phone-based routing connects applicants to an AI voice assistant that collects and verifies information in real time, an automation layer analyses and routes each application, and everything syncs straight into the CRM, no manual re-entry, no waiting for a human to key in the details.
The Result
Applicants now move through intake and verification in one continuous, voice-guided step instead of a manual, multi-handoff process, removing the friction points that were driving abandonment.
Marketing agency, Australia
An Australian Marketing Agency: Reclaiming 15 to 20 Hours per Client per Month
The Problem
Manual content creation was consuming fifteen to twenty hours per client every month, an amount of time that made it impossible to take on more clients without hiring, and kept the team stuck in research and revision cycles instead of the conversion-focused work that actually grows client accounts.
What We Built
We designed a multi-agent content system, each agent with one job: one tracks trends and gathers signal, one plans topics strategically, one checks everything against brand guidelines, one scores performance, one writes, and one edits. Together they replace a large share of the manual research-write-revise cycle with a coordinated, brand-consistent pipeline.
The Result
The fifteen-to-twenty-hour manual monthly workload per client has been replaced by a system that produces on-brand drafts automatically, freeing the team to focus on strategy and client relationships instead of repetitive production work.
Marketing agency, United States
A US Marketing Agency: Standardising Video Requests That Used to Take 10 to 15 Hours Each
The Problem
Manual video production was taking ten to fifteen hours per video, and requests were arriving scattered across emails and chat messages with no standard intake process, which meant every video started with someone hunting down the brief before any actual production could begin.
What We Built
We built a system where every video request starts in one place (a Telegram bot), then flows through an automated pipeline: AI agents handle content generation, image and audio synthesis, and final assembly, with finished videos delivered instantly through cloud storage instead of passing through multiple manual handoffs.
The Result
Video requests now follow one standard intake path instead of arriving scattered across inboxes and chat threads, and the production stages that used to require manual handling at every step now run through a connected pipeline.
Small business (US) and finance team (UK)
A US Small Business and a UK Finance Team: Ending Manual Invoice Data Entry
The Problem
Finance staff were manually processing hundreds of invoices a month from multiple vendors, extracting data, verifying details, chasing approvals and updating the ERP system by hand. Approval delays were straining vendor relationships, and manual entry was creating data errors with real compliance implications.
What We Built
We built an automated invoice pipeline: incoming invoices are read directly from the inbox, an AI layer performs the data extraction that used to be done by hand, approval workflows route each invoice to the right person automatically, and validated data flows straight into the existing ERP system (QuickBooks), with automatic notifications at each stage.
The Result
Invoice data entry that used to consume hours of finance staff time every week now happens automatically, with approval routing and ERP updates flowing through the same connected system instead of separate manual steps.
E-commerce retailer, United States
A US E-Commerce Retailer: Handling High-Volume WhatsApp Support Without Losing the Personal Touch
The Problem
Customer support was overwhelmed by high-volume WhatsApp inquiries. Manual handling meant slow, inconsistent responses, and off-the-shelf chatbots the team had tried before lacked any real knowledge of the company's own products and policies, which just pushed more conversations into escalation instead of resolving them.
What We Built
We built a WhatsApp support system trained specifically on the retailer's own product and policy knowledge, with intelligent routing so conversations the AI cannot resolve go straight to the right human, and a full conversation history so no context gets lost between messages.
The Result
Customers get consistent, knowledgeable first responses instead of generic chatbot replies, with the escalation path reserved for conversations that genuinely need a person.
Marketing agency, United States
A US Marketing Agency: Rebuilding a Leaking SMS Follow-Up Funnel
The Problem
The agency's manual SMS follow-up process was inconsistent and time-consuming to run by hand, and leads were falling out of the funnel at every stage where a follow-up message should have gone out but did not. This is the kind of lead leakage teams typically see once nurturing depends on someone remembering to send the next message.
What We Built
We built a multi-sequence SMS nurturing system with ten customisable templates, real-time tracking, and scheduled follow-ups that fire automatically at each stage, connected directly to the agency's Shopify customer data so sequences trigger off real customer behaviour rather than a manual list.
The Result
Follow-up messages now go out on schedule automatically instead of depending on a person remembering to send the next one, closing the gap where leads had been falling out of the funnel.
SaaS company on Shopify
A Shopify-Based SaaS Company: Ending Spreadsheet-Tracked Subscription Renewals
The Problem
With thousands of software subscriptions to track, the customer management team was manually monitoring expiry dates in spreadsheets and searching for upcoming renewals by hand. That process was producing missed renewals, incorrectly segmented customers, and real revenue loss.
What We Built
We connected the company's Shopify store directly to an automated renewal-tracking system with multi-stage reminders and smart customer segmentation, replacing the spreadsheet entirely with a system that knows which customers are up for renewal and reaches out on schedule.
The Result
Renewal tracking moved from a manually maintained spreadsheet to an automated system with staged reminders and proper segmentation, closing the gap where renewals had previously been missed and revenue quietly lost.
