how we partner

Intelligence that gets acted on. Not shelved.

Four ways to work with us. Most clients start with the diagnostic. The findings usually determine what comes next.

core engagements

Where most clients start

The organisational diagnostic

A full-scope diagnostic combining confidential stakeholder interviews with rigorous financial, operational and systems analysis. The output is two deliverables: an executive intelligence brief for the leadership team and a governance-ready presentation for the board, both specific enough to act on immediately.

Delivered in 10 to 12 weeks.

Entry point

Funder insight sprint

For organisations that need a clear picture quickly, or are not yet ready for a full diagnostic. This is a complete engagement in its own right. We work alongside your leadership team from internal hypothesis through to external validation and a full set of actionable outputs.

Typically four to six weeks.

Entry point

AI workflow design and knowledge architecture

For organisations where the problem is what leaves with their people. We design and build systems that process unstructured organisational knowledge, funder conversations, board meetings, stakeholder interactions and turn it into structured intelligence that compounds over time and survives staff transitions. Delivered with a specialist technology partner, embedded alongside our advisory team from discovery through to handover.

Typically 10 to 12 weeks for the initial build phase.

Ongoing

Signal pulse

Quarterly funder conversations and leadership debriefs that keep the intelligence live between engagements. On-call for pitch preparation, board moments and funder relationship inflection points. Available at a fixed monthly or quarterly rate.

Two to three conversations per quarter.

faqs

What organisations typically want to know first.

  • Most come because something has shifted. A funder has gone quiet. New leadership is in place and needs to move quickly. Income has plateaued despite strong delivery. The board wants to help but something is blocking it. Leadership needs endorsement for a future roadmap and the confidence to ask for investment. What these situations have in common is that the right intelligence, gathered properly, changes what is possible.

  • Most stakeholder research tells organisations what people think. We tell them what people think and why, what they are not saying out loud and what it would take to change it. That comes from the quality of the interviews and the analytical layer that follows, not the volume of responses.

  • Yes. Some of the most useful engagements happen when a strategy already exists but confidence in it is low or funder response has been flat. We come in to test assumptions, not to restart from scratch.

  • We work upstream of communications. What we produce tells you what to say and to whom. Your agency works with that. Most clients find the two relationships reinforce each other rather than overlap. If funder appetite is shifting but your messaging has not, Signal Pulse exists precisely for this.

  • Typically organisations with teams of 40 and above and annual income between £5m and £50m, managing a mixed income base across philanthropy, government, and earned revenue. The common thread is not size but situation: a board that needs to make a significant income strategy decision and does not yet have the full external picture.

  • The full organisational diagnostic runs 10 to 12 weeks. The Funder Insight Sprint runs four to six weeks. AI Workflow Design and Knowledge Architecture typically runs 10 to 12 weeks for the initial build phase, with a structured implementation period following.

  • Yes. For knowledge architecture and AI workflow engagements we work with specialist implementation partners with demonstrated experience across the platforms involved, typically Salesforce, Slack, SharePoint and AI APIs. Where the engagement calls for it, partners also build AI agents and custom interfaces designed for the way your team actually works. Partners are selected for the specific engagement rather than as a standing arrangement, which means the technical capability is matched to the problem.

  • The most common reason knowledge management improvements fail is not the technology. It is that the system depends on staff discipline that erodes under pressure. We design against that from the start: automating what can be automated, building AI agents that give your team a simple interface they will actually use, and embedding changes in workflows people already use. What we hand over is a system that runs without us, not a set of recommendations that require a champion to survive.

  • The problems are consistent across organisations: intelligence that lives in Slack but never compounds, institutional knowledge that walks out the door with each hire and CRM records that are incomplete because logging competes with client work. We map where knowledge is created, where it stalls and what it would take to make it flow. The result is a system where meeting outcomes route automatically into structured records, historical data is processed and surfaced by relationship or topic and your team accesses what they need through interfaces built for the way they actually work. The specifics vary by platform and organisation. The underlying logic does not.