A working lab for turning proven business judgement into practical AI workflows across communications, partnerships and growth.
The goal is not to build a generic assistant or automate every task. The goal is to identify how top performers recognise priorities, assess risk, qualify opportunities and decide what to do next — then give them an AI execution layer capable of carrying out the repetitive, time-consuming and information-intensive work around those decisions.
AI handles scale and consistency. People continue to own strategy, context, relationships, negotiation and final decisions.
The business owner defines the strategy, standards and priorities. AI executes repeatable tasks across a wider set of information, accounts and channels. Each cycle produces evidence about which signals mattered, which opportunities converted and which actions created value — and those insights improve the next round. The objective is not only to complete work faster. It is to make every cycle of execution strengthen the next one.
An AI assistant designed to identify external signals early and help teams respond before manageable issues become larger reputation problems.
Media coverage, social conversations, user comments and community sentiment are distributed across multiple platforms. Manual monitoring makes it difficult to maintain sufficient coverage, distinguish meaningful signals from noise and respond quickly when a problem begins to spread.
It monitors news publications, media websites, social platforms, community channels, selected keywords, brand and product mentions, and user sentiment — consolidating relevant information, removing duplication, identifying changes in discussion volume or sentiment and prioritising signals that may require attention.
The workflow helps teams determine: whether an issue is genuine, isolated or spreading; which users, media outlets or communities are involved; whether sentiment is changing; which facts require internal verification; and whether the situation requires observation, direct engagement or a public response.
The objective is to reduce the time between the first external signal and an informed internal decision.
The same system supports influencer and creator partnerships by checking whether agreed content has been published and reviewing content volume, quality, message accuracy, views, engagement, comment sentiment, brand-safety signals and campaign completion. AI organises the evidence and identifies exceptions; the team interprets context, verifies facts and decides how to respond.
The assistant does not independently manage public crises. Final decisions remain with the team — factual confirmation, response strategy, public messaging, relationship management and escalation.
An AI execution layer designed to follow a top BD's commercial strategy across prospecting, partner operations and portfolio management.
Business developers spend substantial time collecting account information, building prospect lists, updating records, following up and handling routine communication — reducing the time available for high-value work: identifying commercial potential, developing relationships, negotiating terms and designing partnerships.
Shadow BD supports social-account discovery, prospect research, criteria-based scoring, commercial-opportunity identification, lead segmentation, priority ranking, personalised outreach drafts, response support, negotiation preparation, contract drafting and partnership-stage tracking — applying the standards and priorities set by the BD owner to reduce a large, unstructured pool of potential partners into a focused set of qualified opportunities.
For existing partners: partner tagging and segmentation, communication records, follow-up reminders, targeted information delivery, routine FAQ responses, product and account guidance, collaboration checks, performance consolidation, partnership-value analysis, inactivity detection and reactivation opportunities.
Shadow BD does not independently determine market strategy, commercial terms or partnership priorities. The BD owner remains responsible for relationship judgement, personalised pitching, negotiation, collaboration design and final agreements.
Applying social-profile analysis, rule-based qualification, automated communication and content evaluation to a live creator campaign.
AI reviews applicants' public social-media profiles against the campaign's defined rules and criteria — basic eligibility, content category, posting activity, audience signals, engagement performance, historical content quality, campaign relevance and brand fit. Each applicant receives a structured initial assessment: qualified · additional information required · human review required · criteria not met. The campaign team concentrates on ambiguous, exceptional or strategically important cases instead of reviewing every profile from the beginning.
Once a result is confirmed, the workflow triggers the appropriate notification and updates the participant's status. Standard communication is automated; special cases and important creator relationships remain under direct human management.
After creators submit content, AI evaluates each entry against the campaign framework — completion of requirements, information accuracy, content quality, originality, brand and product presentation, views, engagement and comment sentiment — producing criterion-level scores, an overall assessment and a preliminary ranking. Borderline or abnormal cases are escalated for human review. Final rankings, rewards and future partnership decisions remain with the campaign team.
Whether a campaign can support more applicants and submissions without requiring an equivalent increase in manual operations.
What I am measuring:
These projects are still being built and tested. This page will be updated with verified evidence rather than projected impact.