Real businesses. Real problems. Real outcomes.

The situation

A 12-person residential real estate agency was receiving 80 to 100 inbound enquiries per week across calls, web forms, and social DMs. Their agents were only reachable during business hours. Anyone who called after 6pm or on weekends either hit a voicemail and gave up, or called a competitor.

The problem

The agency had no system to capture, qualify, or respond to leads outside working hours. Agents were manually calling back cold leads the next morning, often 14 to 18 hours after first contact. The conversion from enquiry to booked viewing was sitting at 19%.

What we built

We built a voice AI agent that answers every inbound call 24 hours a day, 7 days a week. The agent introduces itself as part of the team, asks 6 qualification questions (budget range, timeline, preferred area, property type, financing status, how they heard about the agency), logs the answers to their CRM, and books a viewing slot directly into the relevant agent's calendar. For web and DM enquiries, an automated SMS follow-up fires within 90 seconds.

The situation

A mid-sized industrial components manufacturer with 90 employees and three production lines had grown faster than their systems. Production scheduling, inventory, purchase orders, and supplier communication all lived in separate spreadsheets maintained by different people. Data was constantly out of sync.

The problem

Production managers were spending 3 to 4 hours every morning reconciling data across systems before the day could begin. Inventory shortfalls were only discovered when a production run was already scheduled. Emergency purchases at premium prices were becoming routine, eating directly into margins. There was no single source of truth.

What we built

We designed and deployed a custom ERP system that unified all four data sources into one platform. On top of this, we built an AI operations layer that monitors inventory in real time, flags potential shortfalls 14 days in advance, auto-generates draft purchase orders when stock hits reorder thresholds, and produces a plain-English daily operations summary for leadership every morning at 7am. The entire system runs on their existing infrastructure with no new hardware.

The situation

An independent creator in the business education space had built a strong following across YouTube, Instagram, and a newsletter. Despite the audience, they were posting inconsistently, their newsletter had dropped to bi-weekly, and they described the content production process as "exhausting". Most of their time was going into production logistics, not the ideas themselves.

The problem

Each piece of content required separate effort for scripting, caption writing, thumbnail briefing, newsletter drafting, and cross-platform reformatting. Nothing connected. There was no system. The creator was doing every step manually and the quality was inconsistent as a result. Newsletter open rates had fallen from 43% to 26% over six months.

What we built

We built a connected content production system. The creator records one long-form video. From that single input, AI extracts the key arguments, drafts the newsletter edition, generates platform-specific captions for Instagram, LinkedIn, and X, creates a script outline for the short-form clip, and produces a SEO-optimised blog post. Everything is staged for review in one workspace. The creator approves or edits, then publishes. One recording session now produces seven content pieces.

The situation

A direct-to-consumer e-commerce brand selling across their own website and two marketplaces was processing 480 to 600 support enquiries every day. The team of three support agents was handling order tracking, return requests, product questions, and complaint escalations manually from a shared inbox.

The problem

Average response time had climbed to 9 hours. Customer satisfaction scores were falling. Two of the three support agents were considering leaving. The business owner was spending evenings catching up on tickets. During peak sale periods, the backlog became unmanageable.

What we built

We trained an AI support system on the full product catalogue, return and refund policies, courier integration data, and 18 months of historical support conversations. The system handles all routine enquiries (order status, return initiation, product FAQs, delivery timescales) automatically. Complex complaints and edge cases are flagged and routed to a human with full context already written up. The system works across email, website chat, and the marketplace messaging portals.

The situation

A 7-person business consulting firm was writing bespoke proposals for every new client enquiry. Each proposal took 4 to 6 hours to produce, required sign-off from the founding partner, and went through two to three revision rounds before being sent. The firm was winning roughly 4 in 10 proposals.

The problem

The proposal process was inconsistent. Different team members wrote in different styles, emphasis varied by who wrote it, and pricing was not presented clearly. Onboarding a new client after winning took 2 to 3 weeks of back-and-forth collecting information. The firm was leaving money on the table and wasting senior time on administrative work.

What we built

We built a two-part system. First, an AI proposal generator: the team fills in a structured intake form about the client and the scope, and the system produces a complete, on-brand proposal document in 8 minutes. Partners review and adjust tone or pricing, then send. Second, an automated client onboarding workflow that collects all required documents, signs contracts, and runs the welcome sequence without manual chasing.