Four systems, four numbers that moved

The client

Little Birdy Careers is a fast-growing recruitment firm working across real estate, construction, technology, professional services and hospitality. As their client base expanded, so did the pressure on a small business-development team to keep the pipeline full without burning out.

The challenge

The team was combing LinkedIn by hand to find hiring companies and decision-makers. Slow, inconsistent, and impossible to scale.

• Time drain: 10–12 hours per rep per week on manual research
• Inconsistent data: missing contact info, outdated job posts
• Growth bottleneck: manual systems couldn't match the firm's ambitions

What we built

• Smart targeting. Live sourcing filtered to US companies under 200 people, job postings inside the last week, and the industries that actually convert
• AI enrichment. Decision-makers at C-suite, VP and director level, company summaries, industry tags, verified contact data
• Lead scoring. Ranked on seniority, industry fit, data completeness and hiring activity, then grouped High / Medium / Low
• Instant reports. Exportable files with full metadata and the reasoning behind every ranking visible

The impact

What once took an entire week of manual work was finished in under an hour. A single run produced more than 1,000 qualified leads, including nearly 200 top-priority targets ready for outreach, and the sales team went back to building relationships instead of digging through LinkedIn.

Business outcomes

• 99% reduction in lead research time
• 10× increase in qualified lead volume
• Shorter sales cycles and faster conversions
• A system that scales with the firm rather than against it

Built with

The client

Hank Robotics is pioneering drone-powered exterior glass cleaning, making high-rise maintenance safer, faster and cheaper. Expanding across South Florida meant finding the right buildings and, harder, the people who actually authorise the work.

The challenge

Growth was capped by a manual, fragmented prospecting process spread across property databases, Google Maps and LinkedIn.

• Low output: around 25 usable leads per week
• Data silos: ownership, management and operator data in separate places
• Repetition: hours of redundant research per market
• Lack of depth: single-contact leads limited reach and slowed deals

What we built

A lead engine that merges real estate databases, commercial registries and public records into one workflow, then uses web search agents to close the gaps in ownership and contact hierarchy.

• Data fusion across databases, registries and public datasets
• Agent tooling to structure, validate and enrich each record
• Web search to fill ownership and management gaps with current data
• Automation pipeline producing ready-to-use files in their own format

The output isn't a contact list. It's a relationship map for each building, connecting property details to verified contacts across every layer of the decision.

Business outcomes

• Integrated intelligence. One source from many disconnected ones
• 40+ hours a month freed from manual research
• Validated contacts across management, ownership and operations
• Scalable. The framework extends to new metros and property classes

The takeaway

Within 90 days, outreach capability expanded in both volume and precision. The sales team now receives structured, verified datasets covering the most relevant high-rises in the region, with contact hierarchies attached, and spends its time on the conversations rather than the spreadsheet.

Built with

The client

Luxn is a fast-growing digital PR agency helping brands build visibility and credibility through earned media and thought leadership. As the team scaled, client meetings and outreach volume surged, and manual follow-ups started slowing everything behind them.

The challenge

• Delayed follow-ups: drafting personalised responses after every meeting took hours each week
• Generic messaging: the obvious fix, templates, would have cost them the voice their business is built on
• Workflow disruption: managing outreach outside Slack added friction to a team that lives in Slack

What we built

• Drafts from real conversations. The agent reads each client or journalist meeting transcript and writes a follow-up that reflects tone, intent and context
• Delivered into Slack. Each draft lands in a dedicated channel to review, edit or send, with no third-party branding and no new tool
• No context switching. Follow-up management stays inside the workflow the agency already runs on

Business outcomes

• Time efficiency. Multiple hours saved weekly on manual drafting
• Improved engagement. 25% increase in email responses
• Integrated workflow. Fully native to Slack
• Consistent brand voice. Personalised, on-brand communication at scale

The takeaway

One of the agency's most repetitive, time-consuming tasks became an intelligent workflow: more personalised outreach, faster responses, and zero disruption to how the team already works.

Built with

The client

Leaf Guardian is a healthtech startup helping hospitals reduce pressure injuries through intelligent monitoring and prevention. As the company prepared to expand its hospital partnerships, outreach became the operational bottleneck.

The challenge

Lead generation was entirely manual. A team member spent roughly one hour compiling hospital contact data for just 80 records, pulling from databases, copying into Excel, and verifying email accuracy by hand. Slow, hard to scale, and inconsistent in quality across the pipeline.

What we built

• Dramatic time savings. An hour for 80 records became minutes
• Higher contact accuracy. Better hospital-executive targeting from fresher, verified data
• Privacy-first design. Built for healthcare compliance; no protected health information touches the workflow
• Scalable foundation. The system extends as new hospital partners are added

The impact

Leaf Guardian now generates accurate hospital executive contact lists roughly ten times faster. The time comes back as strategic outreach, relationship management and pilot preparation, which for a lean team is the difference between growing and maintaining.

The takeaway

Agentic AI let a small healthtech team scale faster without compromising on data quality or compliance, so the founders could spend their hours on what they're actually here to do: improving patient outcomes.

Built with