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Romanos Boraine

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Messy information into traceable evidence systems.

Romanos Boraine is a data consultant and evidence systems consultant helping research teams, public-sector projects, donor-funded contractors, NGOs, evaluation firms, policy teams and service businesses turn messy information into structured systems, traceable evidence, reporting workflows and clearer decisions.

  • Min project size
    $5,000+
  • Hourly rate
    $50 - $99 / hr
  • Employees
    Freelancer
  • Year founded
    Founded 2025

Overview by:

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Romanos Boraine
Independent Consultant About the Team

Service Lines
BI & Big Data Consulting & SI
30%
AI Consulting
20%
Business Consulting
20%
Market Research
10%
AI Agents
5%
API Development
5%
Generative AI
5%
IT Strategy Consulting
5%

No Focus Areas have been added yet…

Industries
Business services
25%
Government
25%
Non-profit
25%
Other industries
25%
Clients
Midmarket ($10M - $1B)
50%
Enterprise (>$1B)
25%
Small Business (<$10M)
25%

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Our Story

Romanos Boraine is an independent data and evidence systems consultant. The work started from a practical need: helping teams make better use of information already sitting across spreadsheets, PDFs, interviews, submissions, reports, folders and draft documents. The focus is simple: collect better data, structure it into traceable evidence, and turn it into reports, dashboards, AI knowledge bases and decision-support outputs that people can actually use.

Meet the Team

Romanos Boraine

Romanos BoraineIndependent Consultant

What Sets Us Apart

Structure before AI

I do not start with tools. I start by mapping the information workflow, source material, review steps and required outputs. AI is only added where it helps retrieval, coding, comparison, summaries or drafting with human review still in place.

Source traceability stays visible

For evidence-heavy work, claims, quotes, findings and recommendations need to link back to source material. I build source IDs, evidence tables, review flags and QA checks so teams can move faster without losing the evidence trail.

From raw material to usable outputs

I work across the full route: data collection, evidence structuring, analysis support, reporting workflows and decision-support outputs. That means clients do not only get a spreadsheet or AI tool. They get a working system they can use.

Tools and Technology

Google Meet
Google Drive
Google Sheets
Google Cloud Platform
Google Workspace
Microsoft Excel
Airtable
Zapier
GitHub
Power BI
Vercel
Cloudflare

Commonly Asked Questions

I measure success by whether the client can use the information more easily after the project than before. That usually means the team has moved from scattered spreadsheets, PDFs, interviews, submissions, notes, reports, folders or emails into a clearer working system. Success can include a structured database, source register, evidence table, AI knowledge base, reporting workflow, dashboard, briefing pack or decision-support output. For evidence-heavy work, I look at whether claims, quotes, findings and recommendations can be traced back to source material. For reporting work, I look at whether writers and reviewers can find what they need faster. For data collection work, I look at whether new information is captured in a cleaner format from the start. For AI-supported work, I look at whether outputs are grounded in approved source material and checked by people. The final test is practical: has the system reduced manual work, improved traceability, made review easier, and left the team with something they can keep using after handover?

I usually start by understanding the client’s current workflow before choosing tools or suggesting a build. That means looking at where the information comes from, where it currently sits, who reviews it, what output it needs to become, and where the process is slowing down. From there, I agree the scope, source material, tools, review points and expected outputs. I often work with samples first, such as a few reports, spreadsheets, submissions, interviews, forms or folders, so the structure can be tested before applying it across the full project. Collaboration is practical and hands-on. I can work inside the client’s existing tools where possible, such as Google Workspace, Microsoft 365, Excel, Sheets, Airtable, Framer, Zapier, Make or approved AI tools. I usually share working structures, evidence tables, source registers, prompts, dashboards, drafts or workflow maps as the project develops, then adjust them based on client review. For evidence-heavy or AI-supported work, I keep human review and source checking in the process. The aim is not to disappear into a build and return with something confusing. The aim is to leave the team with a clear system, useful outputs, handover notes and a workflow they can keep using.

I am currently most interested in projects where teams have useful information but need a better route from raw material to usable outputs. Good-fit projects include data collection and intake systems, public submission analysis, qualitative data synthesis, donor reporting workflows, evidence databases, AI knowledge base builds, research data processing, reporting workflow automation, dashboards, briefing packs and decision-support tools. I am especially interested in work with research teams, evaluation firms, public-sector projects, policy teams, donor-funded contractors, UNICEF or UN delivery partners, NGOs and programme teams. These projects often involve interviews, case studies, public submissions, open-text survey responses, fieldwork notes, partner reports, PDFs, spreadsheets and draft documents. The strongest fit is when source traceability matters. That means the team needs to know where claims, quotes, findings, recommendations or report sections came from. I also take on practical operational projects where service businesses or internal teams need better lead capture, admin workflows, data capture forms, folder automation or reporting systems. I am less interested in generic AI experiments, bulk data entry, unchecked automated outputs or software builds where the information workflow has not been defined. The best projects have a real output, a clear owner, useful source material and a need for a system the team can keep using.

I do not give a universal ROI percentage because each project starts from a different level of source volume, team capacity, deadline pressure and reuse value. For this work, return on investment is usually seen in time saved, fewer manual review hours, smaller delivery teams, faster reporting, better source traceability and a system the team can keep using after handover. A clear example is the UNICEF Zambia evidence workflow for 120 narrative case studies. Processing moved from roughly 60 to 90 minutes per case to about 15 minutes per case, saving an estimated 120 analyst hours. In practical terms, that meant experienced report writers and analysts could spend less time manually searching, coding and rebuilding evidence, and more time reviewing, interpreting and writing. For the UNICEF Palestine situation analysis recovery project, the return was delivery speed and budget protection. The team had a very short three-week recovery window. Instead of needing a large team to manually rebuild the evidence base, the project used a structured workflow to organise qualitative material, support analysis, prepare recommendations and move the draft forward within the available budget and timeline. For a South African Local Government White Paper process, the value came from handling a large public evidence process with a small team. The workflow supported more than 250 initial public submissions, more than 220 public comments on the draft, weekly reporting, live database updates, coded claims, thematic synthesis and an AI-supported evidence assistant for report writers. Across projects, the ROI is usually the same pattern: less time lost to manual searching and copying, fewer people needed to process large evidence volumes, faster movement from raw material to report-ready outputs, and a clearer evidence trail from source material to final report.

My approach to communication is to keep the client close to the work without creating unnecessary meetings or long status documents. At the start of a project, I confirm the workflow, source material, tools, review points, expected outputs, timeline and handover needs. I also clarify who owns decisions, who reviews outputs, and where the working files will sit. During the project, I usually communicate through short written updates, working documents, shared spreadsheets, review notes, and focused calls where needed. For evidence and reporting projects, I make the working structure visible as it develops. This can include source registers, coding frameworks, evidence tables, dashboards, AI prompts, synthesis notes, draft outputs, QA flags and handover notes. Reporting depends on the project. For a data collection or workflow automation build, I report on what has been mapped, built, tested and handed over. For evidence-heavy work, I report on source coverage, coding progress, review issues, gaps, risks and outputs prepared. For AI-supported work, I keep the source base, prompts, limitations and human review process visible. The aim is simple: clients should know what has been done, what needs review, what decisions are pending, and how the work is moving from messy information into a usable system or output.

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