Delivery Intelligence Systems for Research, Policy and Impact Teams
Delivery Intelligence Systems for Research, Policy and Impact Teams
Delivery Intelligence Systems for Research, Policy and Impact Teams
I build end-to-end workflows that turn messy inputs into structured evidence and standards-aligned reporting outputs, with audit trail, QA, and handover-ready documentation.
Trusted on high-stakes reporting and delivery work
Selected partners and projects across research, policy, operations, and digital.
When inputs are messy, reporting becomes slow, risky, and inconsistent
Evidence lives in PDFs, folders, inboxes, and spreadsheets with no single source of truth
Different analysts code and summarise differently, so reviews drag and rework piles up
It is hard to defend findings because the evidence-to-claim trail is unclear
What I deliver instead
A clean, structured evidence base (with a data dictionary and validation rules)
Faster synthesis with review guardrails and traceability back to source
Reporting packs and draft outputs aligned to your required standards and templates
What you can expect
Structured data capture tools (forms and calculators as needed)
Clean, standardised database with a documented data dictionary
AI-assisted categorisation and synthesis with review guardrails
Insight interface (Custom GPT) to query and reuse the evidence base
Standards-aligned reporting outputs (tables, summaries, draft sections)
Governance built in: QA checks, versioning, source traceability
Handover pack: SOP + training for your team
Access control and confidentiality approach (work within your environment where possible)
Service options (choose the layer you need)
Capture & Automation Engine
Capture → compute → route (automated).
Best for
Teams who need structured intake and automated follow-up.
Outcomes
Clean inputs from day one
Instant scoring/categorisation
Automated routing into tools and comms.
Evidence, Insight & Reporting Engine
Raw inputs → structured evidence → reporting-ready outputs.
Best for
Teams drowning in documents, submissions, or messy datasets.
Outcomes
Faster synthesis
Consistent categorisation
Traceable, defensible insights.
Insight Copilot
A Custom GPT over your evidence base for self-serve answers.
Best for
Teams that need rapid answers without re-reading everything.
Outcomes
Faster internal insights
Better reuse across workstreams
Less dependence on one analyst.
Report Writer System
Standards-aligned drafting workflow with a human review loop.
Best for
Research and policy teams with tight reporting cycles and heavy review pressure.
Outcomes
Faster drafts
Consistent structure
Clear evidence-to-claim linkage.
Best for larger projects: Full Evidence & Reporting System
A complete workflow that runs Capture → Compute → Analyse → Report, designed for traceability, audit trail, and handover-ready documentation.
Outcomes
Capture & Automation Engine + Evidence, Insight & Reporting Engine
Optional Insight Copilot + Report Writer layer depending on scope
How it works
1) Scoping (objectives, standards, constraints, stakeholders)
2) System design (data schema, taxonomy, workflow map)
3) Build (capture tools, database, automations, AI workflows)
4) QA + traceability (checks, versioning, audit notes)
5) Handover (SOP + training + recommended next steps)
Real World results
Short case snapshots showing outcomes and how we got there.
Short snapshots of what changed and how. Real outcomes, not just activity.
TheFutureMe (TFM), a systems-first wellness score for real life
LGWP26 Case Study: Turning 270 Submissions into a Traceable Evidence Base
08 Sept 2025
UNICEF AI Case Study: Child Poverty Study (Zambia)
08 Sept 2025
Crafting a Digital Brand: A Case Study on the Launch of Craft Cask
20 Jun 2025
Frequently Asked Questions
1) What do you mean by “Delivery Intelligence Systems”?
I build practical, end-to-end workflows that turn messy inputs into structured evidence and standards-aligned reporting outputs. That usually includes a capture layer, an analysis-ready database, QA checks, traceability back to source, and a handover pack your team can run without me.
2) Can you build an ETL pipeline to turn messy inputs into an analysis-ready database?
Yes. I design the schema first, then build an ETL flow that cleans, standardises, validates, and loads your data into a format your team can actually use. The goal is repeatable processing, not a once-off “data dump”.
3) Do you do intelligent document processing (IDP) or PDF extraction?
Yes, when it makes sense for the project. I can extract structured fields from documents, normalise them, and route them into your evidence base with source references so reviewers can trace any finding back to the original material.
4) Can you create a codebook or coding framework so multiple analysts code consistently?
Yes. I help you define a defensible taxonomy (themes, sub-themes, tags, definitions, inclusion rules, exclusions, and examples). This reduces reviewer friction and makes synthesis far more consistent across analysts and workstreams.
5) Can you build a qualitative evidence workflow that is traceable and “audit-ready”?
That is the point of the system. I build in evidence-to-claim traceability, versioning, QA checks, and clear documentation so your outputs can stand up to scrutiny from senior reviewers, funders, or oversight bodies.
6) Do you build RAG or “knowledge base chatbots” over internal documents?
Yes. I can build an Insight Copilot (Custom GPT-style assistant) grounded in your curated evidence base, with guardrails so it is useful for internal queries without inventing answers. It is designed for reuse across teams, not novelty.
7) Can the system produce donor-aligned or standards-aligned reporting packs?
Yes. I align outputs to your required templates and standards (for example: MEL or M&E reporting formats, policy briefs, annexures, evidence tables, and structured summaries). The aim is to reduce drafting time and rework, while keeping the underlying evidence trail intact.
8) What tools do you build this in?
It depends on your environment and constraints. I typically work “spreadsheet-first” where useful, then move into more robust database and workflow tooling when scale demands it. Where possible, I build within your existing stack to reduce friction and handover risk.
9) How do you handle confidentiality and access control?
I treat your evidence base as sensitive. Access is limited to named team members, and I prefer working inside your environment (or with secure shared workspaces) where feasible. I also document what data is stored where, and how it is governed.
10) What does a typical engagement look like?
Most projects follow a clear path: scoping → schema and workflow design → build → QA and traceability → handover and training. You get a working system plus documentation, not just recommendations.
11) How long does it take?
It depends on volume, complexity, and standards requirements. Smaller “engine” builds can be relatively fast, while full evidence and reporting systems take longer due to design, QA, and review loops. The scoping call is where we lock the timeline and delivery milestones.
12) Are you a fit for small “misc support” retainers or brand design work?
Not usually. I am a best fit when the problem is data-to-evidence-to-reporting under real delivery pressure, and you need a system your team can run repeatedly.
Trusted on high-stakes reporting and delivery work
Selected partners and projects across research, policy, operations, and digital.
When inputs are messy, reporting becomes slow, risky, and inconsistent
Evidence lives in PDFs, folders, inboxes, and spreadsheets with no single source of truth
Different analysts code and summarise differently, so reviews drag and rework piles up
It is hard to defend findings because the evidence-to-claim trail is unclear
What I deliver instead
A clean, structured evidence base (with a data dictionary and validation rules)
Faster synthesis with review guardrails and traceability back to source
Reporting packs and draft outputs aligned to your required standards and templates
What you can expect
Structured data capture tools (forms and calculators as needed)
Clean, standardised database with a documented data dictionary
AI-assisted categorisation and synthesis with review guardrails
Insight interface (Custom GPT) to query and reuse the evidence base
Standards-aligned reporting outputs (tables, summaries, draft sections)
Governance built in: QA checks, versioning, source traceability
Handover pack: SOP + training for your team
Access control and confidentiality approach (work within your environment where possible)
Service options (choose the layer you need)
Capture & Automation Engine
Capture → compute → route (automated).
Best for
Teams who need structured intake and automated follow-up.
Outcomes
Clean inputs from day one
Instant scoring/categorisation
Automated routing into tools and comms.
Evidence, Insight & Reporting Engine
Raw inputs → structured evidence → reporting-ready outputs.
Best for
Teams drowning in documents, submissions, or messy datasets.
Outcomes
Faster synthesis
Consistent categorisation
Traceable, defensible insights.
Insight Copilot
A Custom GPT over your evidence base for self-serve answers.
Best for
Teams that need rapid answers without re-reading everything.
Outcomes
Faster internal insights
Better reuse across workstreams
Less dependence on one analyst.
Report Writer System
Standards-aligned drafting workflow with a human review loop.
Best for
Research and policy teams with tight reporting cycles and heavy review pressure.
Outcomes
Faster drafts
Consistent structure
Clear evidence-to-claim linkage.
Best for larger projects: Full Evidence & Reporting System
A complete workflow that runs Capture → Compute → Analyse → Report, designed for traceability, audit trail, and handover-ready documentation.
Outcomes
Capture & Automation Engine + Evidence, Insight & Reporting Engine
Optional Insight Copilot + Report Writer layer depending on scope
How it works
1) Scoping (objectives, standards, constraints, stakeholders)
2) System design (data schema, taxonomy, workflow map)
3) Build (capture tools, database, automations, AI workflows)
4) QA + traceability (checks, versioning, audit notes)
5) Handover (SOP + training + recommended next steps)
Real World results
Short case snapshots showing outcomes and how we got there.
Short snapshots of what changed and how. Real outcomes, not just activity.
TheFutureMe (TFM), a systems-first wellness score for real life
LGWP26 Case Study: Turning 270 Submissions into a Traceable Evidence Base
08 Sept 2025
UNICEF AI Case Study: Child Poverty Study (Zambia)
08 Sept 2025
Crafting a Digital Brand: A Case Study on the Launch of Craft Cask
20 Jun 2025
Frequently Asked Questions
1) What do you mean by “Delivery Intelligence Systems”?
I build practical, end-to-end workflows that turn messy inputs into structured evidence and standards-aligned reporting outputs. That usually includes a capture layer, an analysis-ready database, QA checks, traceability back to source, and a handover pack your team can run without me.
2) Can you build an ETL pipeline to turn messy inputs into an analysis-ready database?
Yes. I design the schema first, then build an ETL flow that cleans, standardises, validates, and loads your data into a format your team can actually use. The goal is repeatable processing, not a once-off “data dump”.
3) Do you do intelligent document processing (IDP) or PDF extraction?
Yes, when it makes sense for the project. I can extract structured fields from documents, normalise them, and route them into your evidence base with source references so reviewers can trace any finding back to the original material.
4) Can you create a codebook or coding framework so multiple analysts code consistently?
Yes. I help you define a defensible taxonomy (themes, sub-themes, tags, definitions, inclusion rules, exclusions, and examples). This reduces reviewer friction and makes synthesis far more consistent across analysts and workstreams.
5) Can you build a qualitative evidence workflow that is traceable and “audit-ready”?
That is the point of the system. I build in evidence-to-claim traceability, versioning, QA checks, and clear documentation so your outputs can stand up to scrutiny from senior reviewers, funders, or oversight bodies.
6) Do you build RAG or “knowledge base chatbots” over internal documents?
Yes. I can build an Insight Copilot (Custom GPT-style assistant) grounded in your curated evidence base, with guardrails so it is useful for internal queries without inventing answers. It is designed for reuse across teams, not novelty.
7) Can the system produce donor-aligned or standards-aligned reporting packs?
Yes. I align outputs to your required templates and standards (for example: MEL or M&E reporting formats, policy briefs, annexures, evidence tables, and structured summaries). The aim is to reduce drafting time and rework, while keeping the underlying evidence trail intact.
8) What tools do you build this in?
It depends on your environment and constraints. I typically work “spreadsheet-first” where useful, then move into more robust database and workflow tooling when scale demands it. Where possible, I build within your existing stack to reduce friction and handover risk.
9) How do you handle confidentiality and access control?
I treat your evidence base as sensitive. Access is limited to named team members, and I prefer working inside your environment (or with secure shared workspaces) where feasible. I also document what data is stored where, and how it is governed.
10) What does a typical engagement look like?
Most projects follow a clear path: scoping → schema and workflow design → build → QA and traceability → handover and training. You get a working system plus documentation, not just recommendations.
11) How long does it take?
It depends on volume, complexity, and standards requirements. Smaller “engine” builds can be relatively fast, while full evidence and reporting systems take longer due to design, QA, and review loops. The scoping call is where we lock the timeline and delivery milestones.
12) Are you a fit for small “misc support” retainers or brand design work?
Not usually. I am a best fit when the problem is data-to-evidence-to-reporting under real delivery pressure, and you need a system your team can run repeatedly.
Trusted on high-stakes reporting and delivery work
Selected partners and projects across research, policy, operations, and digital.
When inputs are messy, reporting becomes slow, risky, and inconsistent
Evidence lives in PDFs, folders, inboxes, and spreadsheets with no single source of truth
Different analysts code and summarise differently, so reviews drag and rework piles up
It is hard to defend findings because the evidence-to-claim trail is unclear
What I deliver instead
A clean, structured evidence base (with a data dictionary and validation rules)
Faster synthesis with review guardrails and traceability back to source
Reporting packs and draft outputs aligned to your required standards and templates
What you can expect
Structured data capture tools (forms and calculators as needed)
Clean, standardised database with a documented data dictionary
AI-assisted categorisation and synthesis with review guardrails
Insight interface (Custom GPT) to query and reuse the evidence base
Standards-aligned reporting outputs (tables, summaries, draft sections)
Governance built in: QA checks, versioning, source traceability
Handover pack: SOP + training for your team
Access control and confidentiality approach (work within your environment where possible)
Service options (choose the layer you need)
Capture & Automation Engine
Capture → compute → route (automated).
Best for
Teams who need structured intake and automated follow-up.
Outcomes
Clean inputs from day one
Instant scoring/categorisation
Automated routing into tools and comms.
Evidence, Insight & Reporting Engine
Raw inputs → structured evidence → reporting-ready outputs.
Best for
Teams drowning in documents, submissions, or messy datasets.
Outcomes
Faster synthesis
Consistent categorisation
Traceable, defensible insights.
Insight Copilot
A Custom GPT over your evidence base for self-serve answers.
Best for
Teams that need rapid answers without re-reading everything.
Outcomes
Faster internal insights
Better reuse across workstreams
Less dependence on one analyst.
Report Writer System
Standards-aligned drafting workflow with a human review loop.
Best for
Research and policy teams with tight reporting cycles and heavy review pressure.
Outcomes
Faster drafts
Consistent structure
Clear evidence-to-claim linkage.
Best for larger projects: Full Evidence & Reporting System
A complete workflow that runs Capture → Compute → Analyse → Report, designed for traceability, audit trail, and handover-ready documentation.
Outcomes
Capture & Automation Engine + Evidence, Insight & Reporting Engine
Optional Insight Copilot + Report Writer layer depending on scope
How it works
1) Scoping (objectives, standards, constraints, stakeholders)
2) System design (data schema, taxonomy, workflow map)
3) Build (capture tools, database, automations, AI workflows)
4) QA + traceability (checks, versioning, audit notes)
5) Handover (SOP + training + recommended next steps)
Real World results
Short case snapshots showing outcomes and how we got there.
Short snapshots of what changed and how. Real outcomes, not just activity.
TheFutureMe (TFM), a systems-first wellness score for real life
LGWP26 Case Study: Turning 270 Submissions into a Traceable Evidence Base
08 Sept 2025
UNICEF AI Case Study: Child Poverty Study (Zambia)
08 Sept 2025
Crafting a Digital Brand: A Case Study on the Launch of Craft Cask
20 Jun 2025
Frequently Asked Questions
1) What do you mean by “Delivery Intelligence Systems”?
I build practical, end-to-end workflows that turn messy inputs into structured evidence and standards-aligned reporting outputs. That usually includes a capture layer, an analysis-ready database, QA checks, traceability back to source, and a handover pack your team can run without me.
2) Can you build an ETL pipeline to turn messy inputs into an analysis-ready database?
Yes. I design the schema first, then build an ETL flow that cleans, standardises, validates, and loads your data into a format your team can actually use. The goal is repeatable processing, not a once-off “data dump”.
3) Do you do intelligent document processing (IDP) or PDF extraction?
Yes, when it makes sense for the project. I can extract structured fields from documents, normalise them, and route them into your evidence base with source references so reviewers can trace any finding back to the original material.
4) Can you create a codebook or coding framework so multiple analysts code consistently?
Yes. I help you define a defensible taxonomy (themes, sub-themes, tags, definitions, inclusion rules, exclusions, and examples). This reduces reviewer friction and makes synthesis far more consistent across analysts and workstreams.
5) Can you build a qualitative evidence workflow that is traceable and “audit-ready”?
That is the point of the system. I build in evidence-to-claim traceability, versioning, QA checks, and clear documentation so your outputs can stand up to scrutiny from senior reviewers, funders, or oversight bodies.
6) Do you build RAG or “knowledge base chatbots” over internal documents?
Yes. I can build an Insight Copilot (Custom GPT-style assistant) grounded in your curated evidence base, with guardrails so it is useful for internal queries without inventing answers. It is designed for reuse across teams, not novelty.
7) Can the system produce donor-aligned or standards-aligned reporting packs?
Yes. I align outputs to your required templates and standards (for example: MEL or M&E reporting formats, policy briefs, annexures, evidence tables, and structured summaries). The aim is to reduce drafting time and rework, while keeping the underlying evidence trail intact.
8) What tools do you build this in?
It depends on your environment and constraints. I typically work “spreadsheet-first” where useful, then move into more robust database and workflow tooling when scale demands it. Where possible, I build within your existing stack to reduce friction and handover risk.
9) How do you handle confidentiality and access control?
I treat your evidence base as sensitive. Access is limited to named team members, and I prefer working inside your environment (or with secure shared workspaces) where feasible. I also document what data is stored where, and how it is governed.
10) What does a typical engagement look like?
Most projects follow a clear path: scoping → schema and workflow design → build → QA and traceability → handover and training. You get a working system plus documentation, not just recommendations.
11) How long does it take?
It depends on volume, complexity, and standards requirements. Smaller “engine” builds can be relatively fast, while full evidence and reporting systems take longer due to design, QA, and review loops. The scoping call is where we lock the timeline and delivery milestones.
12) Are you a fit for small “misc support” retainers or brand design work?
Not usually. I am a best fit when the problem is data-to-evidence-to-reporting under real delivery pressure, and you need a system your team can run repeatedly.
Book a 20-minute scoping call with Romanos
Book a 20-minute scoping call to map your reporting requirements, data reality, and delivery risks. You’ll leave with a recommended scope (Capture Engine, Evidence & Reporting Engine, or full system) and next steps.
Helping agencies, consultancies, and delivery teams turn raw inputs into structured evidence and reporting-ready outputs.
Book a 20-minute scoping call with Romanos
Book a 20-minute scoping call to map your reporting requirements, data reality, and delivery risks. You’ll leave with a recommended scope (Capture Engine, Evidence & Reporting Engine, or full system) and next steps.
Helping agencies, consultancies, and delivery teams turn raw inputs into structured evidence and reporting-ready outputs.
Book a 20-minute scoping call with Romanos
Book a 20-minute scoping call to map your reporting requirements, data reality, and delivery risks. You’ll leave with a recommended scope (Capture Engine, Evidence & Reporting Engine, or full system) and next steps.
Helping agencies, consultancies, and delivery teams turn raw inputs into structured evidence and reporting-ready outputs.
Kickstart a project today!
Kickstart a project today!