Services
The engineering behind useful AI.
01Data and content engineering
Get the information your work depends on in order.
Business information sits in documents, shared drives, repositories, business applications, and databases. It is often duplicated, out of date, or hard to match up. We connect, clean, or move those sources so your team, or a later workflow, can rely on them.
For example
An organization needs to move years of content off an aging document platform. Each file carries metadata, permissions, and version history that people still use. We map each field to the new platform, migrate in batches, reconcile counts and samples, and report anything that could not move cleanly.
What we do
- Take stock of the sources, their owners, access, and known quality problems.
- Agree the mappings: which field goes where, and what counts as a match.
- Build the integration, migration, or data preparation pipeline.
- Run it on representative samples first, then at full scale.
- Reconcile the results and record every exception that needs a decision.
How you know it worked
- Counts and samples reconcile between source and target.
- Mapped fields, metadata, and permissions behave as agreed.
- Data is as current as the task needs it to be.
- Unresolved exceptions are listed, each with an owner.
What you receive
Connected or migrated sources, prepared outputs, a reconciliation and exception report, and operating notes your team can maintain.
This fits when
Your team gathers the same information by hand, keeps finding records that don't match, or has to move content without losing what makes it useful.
02AI workflow implementation
Turn one repeated task into a working workflow.
Many teams have tried AI tools on their own. Moving from one person's experiment to work a whole team relies on takes more: the right data, connections to your systems, clear review steps, and a way to check quality. We build that around one defined task.
For example
At month-end, a finance team matches supplier invoices against purchase orders and delivery records. A workflow can gather the documents, pull out the key fields, match them, and prepare a list of mismatches with the evidence attached. The team decides each exception. The workflow does not approve payments.
What we do
- Map the task as it runs today: inputs, steps, decisions, and outputs.
- Agree what the workflow may do on its own and what needs a person.
- Connect the sources and applications it needs.
- Build the retrieval, analysis, and output steps.
- Test on agreed examples, including the hard cases, then deploy and hand over.
How you know it worked
- Outputs meet the quality bar agreed on real examples.
- Known failure cases are written down, not hidden.
- Any action that changes a system stays within agreed limits.
- Review effort and running cost are measured and acceptable.
- A second cycle runs on new inputs without starting over.
What you receive
A working workflow, the evaluation results behind it, and documentation of its inputs, outputs, limits, running requirements, and ownership.
This fits when
Your team has a repeated task and accessible inputs, but turning an individual AI experiment into shared, reviewed work needs integration and engineering.
03Managed operation
Give live systems a clear owner.
Once a workflow is live, things change. A source system is upgraded, a field is renamed, a model is updated, or usage grows. Without a clear owner, fixes depend on whoever has time. DDYCorp can take on that responsibility for an agreed set of systems.
For example
A contract-renewal workflow reads dates from a shared drive and a CRM. The CRM renames a field, and some renewals quietly drop off the list. Under managed operation, monitoring flags the change, the integration is fixed, and results are rechecked before the next reminder run.
What we do
- Monitor the agreed systems within agreed coverage hours.
- Handle incidents and route them to the right person.
- Maintain integrations, pipelines, and application dependencies.
- Recheck quality after changes, before they reach users.
- Review usage, running cost, and output quality on a regular schedule.
How you know it worked
- Incidents are logged, handled, and reported as agreed.
- Changes are checked before release.
- Escalation paths and owners are named on both sides.
- Service reports arrive on the agreed schedule.
What you receive
A written operating scope: systems covered, responsibilities, support hours, reporting, escalation, and how changes are made.
This fits when
An application or workflow is useful, but keeping it running depends on ad hoc effort or unclear ownership.
Experience across the systems behind the workflow
Our delivery capabilities span enterprise content management, content migration, custom applications, data integration, analytics, cloud engineering, and technical support.
- Enterprise content management
- Content migration
- Custom applications
- Data integration
- Analytics
- Cloud engineering
- Technical support
Much of the effort in an AI project sits in these systems: finding the data, connecting it, and keeping it accurate. We choose the capabilities each job needs and agree the scope with you.
How we work
How an engagement takes shape
Every project starts from the task and the result it should produce. Nothing is scaled up until it passes the checks agreed at the start. Scope, responsibilities, and commercial terms are agreed per engagement.
Step 01
Define the result.
Name the process owner, how the task runs today, the output you need, and any constraints. If the work is still uncertain, we start with a scoped assessment that ends in an implementation plan.
Step 02
Agree what will be delivered.
Write down the sources, integrations, acceptance criteria, responsibilities, and commercial scope. Software, infrastructure, and ongoing operation are priced separately from the build.
Step 03
Build and test.
Build against representative inputs. Review results, exceptions, review effort, and running cost with the people who will use the workflow. Fix what falls short before rolling it out.
Step 04
Agree who owns it.
Hand over to your team with documentation, or have DDYCorp run it under an agreed scope. Agree how future changes are tested and when a next workflow is worth adding.
FAQ
Common questions
- How is this different from buying an AI tool?
- A tool gives you capabilities. We take responsibility for one task working end to end: the data it needs, the connections to your systems, the review steps, the checks, and who runs it afterward. We can build on tools you already own.
- Do we need to use a DDYCorp product?
- No. An engagement can use your existing platforms, third-party software, custom development, or DDYCorp technology where it fits. The choice follows the task and your operating requirements.
- Can we start with data or content work?
- Yes. Integration, migration, and data preparation can be standalone projects. They can also lay the groundwork for a later AI workflow, but they don't have to.
- Where do people stay involved?
- Wherever judgment matters. We agree up front which steps a workflow may complete on its own and which need a person to review or approve, and we build that boundary in.
- Can you work alongside our internal team?
- Yes. During scoping we agree who does what: implementation decisions, access, acceptance, and who runs the system afterward.
- How do you handle deployment and data access?
- We agree the environment, data flows, permissions, and any model or service dependencies as part of the scope. The options depend on your systems and the products involved.
- What engagement models are available?
- A scoped assessment, an implementation with agreed milestones, or ongoing support. Where requirements will change as you go, we can agree a time-and-materials arrangement with clear priorities and reporting.
- How long does an implementation take?
- It depends on access to sources, integration needs, how much testing is required, and where the system will run. We give you a delivery plan once the scope is agreed.
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