Data to Intelligence to Decisions
KlutchAI builds AI systems for M&E teams, grant-funded organizations, and research and health institutions that don't have AI engineers on staff. We work from your own reports, proposals and records, show the source behind every answer, and leave the final call with your people.
Most organizations aren't short of information. Evaluation reports, proposals, survey results, case files and donor correspondence pile up across drives, inboxes and systems that don't talk to each other. The knowledge is there. Getting it out in time to shape a decision is the hard part.
Reports in one place, spreadsheets in another, the context in an email thread from two years ago. Nobody has the full picture in front of them.
When an experienced colleague moves on, what they knew about a donor, a programme or a method often leaves with them.
Every proposal, evidence review and quarterly report starts over, even when your organization has answered the same question before.
Everything we build follows the same three steps. It keeps the work tied to the decisions you actually need to make, not to the technology.
01 · Data
We connect the information you already have: reports, proposals, evaluations, survey results, case files. Where it helps, we add public data about the communities and sectors you work in. It all becomes one organized layer your AI systems can work from. You don't need a data engineering team to get there.
02 · Intelligence
We build AI systems around your field and your history: assistants that answer from your own documents and cite them, and workflows that draft, summarize, compare and flag. They work from your material, not from whatever happens to be on the internet.
03 · Decisions
Every output arrives with its sources attached, so your team can check it. The AI drafts and suggests; your people review, edit and approve. Nothing is final until someone on your team says so, and there's a record of who did.
The same approach works wherever an organization has data and decisions to make. Here is where we focus.
M&E and grant-funded organizations
Draft responses to calls for proposals using the proposals, evaluations and results your organization has already produced. Your team edits and signs off, and each approved document becomes material for the next one, so the knowledge stays even when people move on.
Research and health
Ask a question across your literature, protocols and field reports and get an answer that cites where each point came from. Researchers spend their time judging the evidence, not hunting for it.
Compliance and risk
Combine the rules you already trust with models that learn from your own history, so analysts spend their time on the cases that matter. Every flag carries its reasoning and a record of who reviewed it.
Underneath every system we build is the KlutchAI platform. It has six parts. You don't have to manage any of them. We do. Your team uses what it needs and we look after the rest.
Where your sources live. Connect documents and data, keep them catalogued, and see where every piece of information came from.
Where predictive models are built, tested and tracked. For example, a model that scores which cases need attention first.
Where language-based systems are built: assistants that answer from your documents, drafting tools, and agents that carry out multi-step tasks, all grounded in your data.
Where the steps are joined into workflows: what runs when, where a person needs to check the work, and what happens next. No servers for you to run.
Dashboards and reports that show what your systems are doing and what they're finding, for leadership and for the team doing the work.
Who can see and do what. Role-based access, a separate workspace for each organization, and an audit trail of every action.
You know your work. We bring the engineering. You shouldn't have to hire data scientists before you can use AI responsibly.
Every system is built around your documents, your history and your field, not a general-purpose chatbot. Your data stays in your own workspace, is never shared with other organizations, and is never used to train models for anyone else.
Outputs come with the sources behind them, so anyone on your team can check a claim before it goes into a report, a proposal or a decision.
The AI drafts, suggests and flags. Your team reviews, approves and owns the result. That's how it's designed, not an optional setting.
KlutchAI is shaped around the needs of research, health and monitoring-and-evaluation organizations: donor reporting, evaluation evidence and field data.
Start with a demo, try it on your own data, prove the value on one problem, then expand. You decide when to take each next step.
No big rollout, no long contract up front. We show you the platform first, prove the value on your own data and one problem you care about, and grow from there at a pace you set.
Step 1
A live walkthrough with our team, shaped around your work. We learn what you need, you see what KlutchAI does, and together we decide whether a trial is worth your time.
Step 2
For teams where it's a good fit: a private workspace set up with a sample of your own documents and data, so you see KlutchAI working on your material.
Step 3
Together we choose one use case that matters, such as proposal drafting or an evidence review, and build it properly with your team, refining it as we go.
Step 4
Once it's working, we extend to more workflows and more teams, on the same data and the same security foundation.
Research, health and development organizations we're working with and in conversation with.
No. KlutchAI is built for organizations without AI engineers. We do the technical work of connecting your data and building the system. Your team uses it through a web workspace and brings what it knows best: the work itself.
Each organization gets its own separate workspace with role-based access controls and an audit trail. Your data is never shared with other organizations and never used to train models for anyone else.
We build systems that answer from your own documents and show where each point came from, so your team can check it. No AI is perfect, which is exactly why a person on your team reviews and approves every output before it's used.
Usually, material you already have: past proposals, evaluation reports, survey exports, spreadsheets, case files. In our first conversation we'll look at what you have and what's worth starting with.
We build on established models and tools from providers including OpenAI, Anthropic, Google and Hugging Face, and pick what suits each task. The platform runs on Amazon Web Services.
It depends on the scope of the work. Every engagement starts with a demo; teams for whom it's a good fit then get a trial workspace on their own data, move to a focused pilot, then scale. Talk to us about your situation.
Tell us about your organization and the problem you'd most like to solve. We'll set up a demo shaped around your work and, if it's a good fit, a trial workspace with your own data.
Book a demo