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Impact is rarely created by one person, one organisation or at one moment in time. Its evidence is just as distributed — across collaborators, documents, systems and stages of a project.
Hivve helps people working together build a shared picture of impact over time. We use AI to reduce the burden of bringing information together, structuring evidence, identifying gaps and developing reporting — giving people more time to focus on creating and strengthening impact.
Meaningful impact can take years to emerge. Research may move from discovery through engagement and adoption before changes become visible. Conservation programmes may develop over decades. Funded programmes may involve multiple cohorts, partners and phases of work.
Hivve is designed around that reality. Collaborators contribute different parts of the picture — activities, engagement, stakeholder perspectives, outcomes and supporting evidence — as the work develops. That creates a shared, evolving evidence base, rather than leaving one person to reconstruct the story when a report is due.
As more evidence is contributed, the picture becomes richer. AI helps make sense of that growing body of information; Hivve provides the structure that keeps it useful and connected.
Understand and communicate the difference your work makes.
Build a shared evidence base with partners as the work develops.
Understand impact across every project and programme you run.
A clearer view across grantees, investments or portfolios.
The value is not simply in generating another report. It is in preserving and connecting the evidence of an impact journey, so knowledge is not lost when people leave, projects evolve or reporting requirements change.
Hivve combines AI with our impact expertise, methodology and taxonomy. This helps turn complex and often unstructured information into more consistent, structured and aggregatable impact data.
When information is structured consistently, it becomes possible to understand what is happening not only within an individual project, but across programmes, organisations and portfolios — identifying patterns, understanding progress and building a clearer picture of change.
Documents, notes, emails, feedback and files from many contributors and systems.
Applied through a consistent framework, so outputs mean the same thing everywhere.
Consistent and aggregatable across projects, programmes, organisations and portfolios.
Good impact practice takes time, capacity and expertise. For busy researchers, project collaborators, stretched professional teams and smaller organisations, consistently capturing evidence and reporting impact can be difficult to sustain.
Yet regular evidence collection matters. Waiting until a report, assessment, board meeting or funding application is due can mean valuable evidence and context have already been lost.
We believe AI can help reduce that burden. By making it easier to capture, structure, analyse and report impact, more people and organisations can contribute to good impact practice — not only those with large teams or specialist resources.
When impact is captured consistently and evidenced clearly, it becomes easier for others to see, understand and stand behind — helping meaningful work gain the recognition, support and funding it needs to continue.
AI can do more of the heavy lifting. It should not replace human expertise, relationships, context or judgement.
AI does more of the heavy lifting. People drive the impact.
The opportunity AI creates also comes with responsibilities. Our approach is built around human oversight, secure data handling and responsible use. AI supports professional judgement rather than replacing it, and accountability remains with people.
Client data used for AI processing is handled within approved, access-controlled and isolated environments. It is not entered into public or consumer AI tools. Temporary AI workspaces and their contents are deleted when authorised processing concludes, subject to any explicitly approved retention requirements.
Hivve does not permit client data, confidential information, prompts, files or outputs to be used to train, fine-tune, benchmark, optimise or improve public or third-party AI models.
We apply data-minimisation principles and use only the information required for the authorised task. Where feasible, information is anonymised or pseudonymised.
AI technologies, infrastructure and providers are assessed against Hivve's requirements for security, confidentiality, processing location, retention, deletion, data use and contractual protections before they are approved. Our infrastructure continues to evolve as we expand our services and support collaboration across organisations and jurisdictions.
Hivve is for people and organisations working to create, support, understand or evidence impact — from individual researchers and project collaborators to research and impact teams, NGOs and not-for-profits, foundations, funders and impact investors.
Because some of the most time-consuming parts of impact practice — bringing information together, structuring evidence, analysing it and developing reports — are areas where AI can provide valuable support. Our aim is to reduce that burden so people can spend more of their time and expertise creating and strengthening impact.
Hivve helps collaborators contribute evidence as work develops, creating a shared and evolving picture of impact over time. This is particularly valuable where projects span organisations, jurisdictions or many years and where different people contribute different parts of the impact journey.
No. AI is a capability that helps us advance Hivve’s wider purpose. Our expertise remains in impact: helping people and organisations understand, measure and demonstrate the difference they make.
Hivve combines AI with our impact methodology, taxonomy, structured data and practitioner expertise. Rather than simply generating text, our aim is to help people build and understand impact evidence over time and turn it into data that can increasingly be used across projects, programmes and portfolios.
No. AI supports analysis and provides drafts, insights or recommendations. People remain responsible for interpreting the evidence, applying context and professional judgement, and making decisions.
No. Client data, prompts, files and outputs are not permitted to be used to train or improve public or third-party AI models.
No. Client-identifiable, confidential or proprietary information is not entered into public or consumer AI tools.
We maintain further security, data protection and AI due-diligence information covering infrastructure, hosting and processing arrangements, relevant providers and subprocessors, retention and security controls. Current arrangements applicable to your organisation can be provided to authorised IT, security and procurement teams as part of our due-diligence process.
Our external statement on how AI is governed, assessed and used across Hivve.
Infrastructure, processing arrangements, providers and subprocessors, retention and security controls.
A perspective on capacity, evidence and who gets to do good impact practice.
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