Document systems, data platforms & AI quality · Ireland and the EU

Turn legacy documents into structured content you can trust

Dropseeker converts large, inconsistent document sets into clean, structured formats and checks every page automatically. Where AI helps, its output is tested against the source and reviewed by a person before anything is published.

Discuss a document project See a worked example

WordPDFDITAHTML→AsciiDocMarkdownXMLJSON

Worked example

What a converted page looks like

A section of a legacy product manual, converted to AsciiDoc and run through automated checks. The checks catch what a quick read misses, and anything uncertain goes to a person rather than being fixed silently.

Before

manual_v3_FINAL.docx

3.2 Installing The Agent

NOTE - you must login as admin first

1) Download the package (see section 4.7)
2) Run   install.sh  --port 8443
3)  Restart the service.
   a. On Linux use systemctl
   b. On windows use the Services panel

Tip: Log-in again after restarting.

After

installing-the-agent.adoc

[[install-agent]]
=== Install the agent

NOTE: Log in as an administrator first.

. Download the package.
  See <<download-package>>.
. Run the installer:
+
[source,shell]
----
./install.sh --port 8443
----
. Restart the service:
** Linux: `systemctl restart agent`
** Windows: the *Services* panel

TIP: Log in again after restarting.

Checks

6 run · 1 for review

passHeading levels consistent
passSteps converted to an ordered list
passCommand preserved exactlyText inside code blocks matches the source byte for byte.
fixedTerminology: "login", "Log-in"Standardised to "log in" using the style guide.
reviewCross-reference "section 4.7"Section 4.7 was renumbered. Linked by title instead; confirm the target.
passNo content droppedEvery source sentence maps to the output.

Illustrative example written for this page. It contains no client or employer material.

Services

What we deliver

A small discovery or pilot, a defined conversion project, or a specialist contribution to a larger programme.

Core service

Document transformation & AI quality

Large, inconsistent document sets become clean, structured content that can be searched, reused and published automatically. Every conversion is checked, and every AI-assisted step is tested and reviewed.

Converting legacy documents to AsciiDoc, Markdown, HTML or XML
Rule-based checks for structure, terminology, links and completeness
AI-generated summaries, descriptions and keywords, checked against the source
Docs-as-code publishing pipelines with automated quality gates
Regression tests so later changes or model updates cannot quietly lower quality

Supporting services

Integration & migration

Connecting converted content to the systems that use it.

REST APIs designed with OpenAPI
Repeatable migration and enrichment pipelines
Monitoring and auditable validation

Data-platform implementation

Configuring proven open-source or commercial platforms before writing custom code.

Metadata ingestion, search and data-quality workflows
Authentication, APIs and system integrations
Containerised delivery with Docker and Kubernetes

Discovery & pilots

Testing the approach on your material before a full build.

Map the current process and review points
Convert a representative sample
Measure accuracy, time saved and cost

Data platforms

Implementation around proven software

Not every requirement needs a new product. Dropseeker can configure an established platform, connect it to existing systems and deliver the integration, migration and quality controls around it.

A practical delivery layer

From scattered sources to governed, usable data

For data catalogues, knowledge platforms and work-management systems, the value is often in reliable ingestion, metadata, permissions and adoption—not another custom interface.

metadatalineagesearchOpenAPIquality rulesSSOKubernetes

SourcesDocuments, APIs, databases and existing business systems

PlatformConfigured catalogue, workflow or knowledge platform

ControlsValidation, access, monitoring and documented handover

Approach

How a project runs

Scope, deliverables and acceptance criteria are agreed before work starts. Each stage can be commissioned on its own.

Discovery
Scope & success criteria
We map the documents, the process and the constraints, and agree how success will be measured.
Pilot
Evidence on real material
A sample converted and checked, with accuracy and time measured and its limits stated plainly.
Build
Demonstrated increments
Delivery in stages, each reviewed against the agreed acceptance criteria.
Handover
Agreed per project
Documentation, training and support so your team can run the pipeline without us.

Principles

How we use AI

Checked against the source

AI output is compared with the original document. Claims that cannot be traced back are flagged, not published.

People approve what matters

Uncertain results go to a reviewer with the evidence beside them. Nothing consequential is changed silently.

Tested like code

Quality checks run on every change, so a new model or prompt cannot lower quality without anyone noticing.

Your data stays yours

Where documents are processed, which models see them and how long anything is kept are agreed in writing first.

Working with us

A clear scope for the right project

Tell us what needs to change and how you handle it today.

For buyers

Share a requirement, a tender reference or a sample of the current documents. We will assess fit and propose a scope with deliverables, assumptions and responsibilities. Public bodies should use their own procurement route and tender documents.

For platform vendors & delivery partners

Dropseeker is a small, senior practice, and most work is delivered directly by its director. We can provide EU-based implementation capacity for configuration, ingestion, migration, API integration, validation and technical handover, with responsibilities agreed in advance.

About

The engineer who scopes it builds it

Colin Bennett is a senior Irish software engineer with more than a decade of experience in structured-document workflows, APIs, integration services, data pipelines and cloud-native systems.

He started Dropseeker to do one thing well: turn messy document estates into structured content, with automation and AI that can be measured, tested and trusted.

BEng Software Engineering, Atlantic Technological University · BEng Computer & Electronic Engineering, Technological University of the Shannon

Python · Go · Java · AsciiDoc · Markdown · OpenAPI · FastAPI · GitLab CI · GitHub Actions · Docker · Kubernetes · BigQuery

github.com/colinben

Contact

Tell us about the documents

Send a short description, a tender reference, or the number and type of documents involved. Please don't send confidential material until we have agreed how it will be handled.

colin@dropseeker.com

Company details

Dropseeker Ltd, registered in Ireland

CRO 671573

Phone: +353 86 051 5136

Privacy

Contact information

If you contact Dropseeker, we use the information you provide only to respond to your enquiry and assess the requested work. Please don't send confidential or sensitive material before suitable handling arrangements are agreed. You may request access, correction or deletion by emailing colin@dropseeker.com.