AI data, evaluation and implementation under one accountable delivery model.
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Connected Delivery brings YPAI’s AI Data & Evaluation and AI Implementation capabilities into one accountable engagement when the system, the evidence and the operating workflow cannot be separated.
Define the outcome once. Connect the necessary workstreams. Test the complete result against one acceptance model.
An AI system can fail even when every supplier appears to have completed its own task.
The application may be built, but the retrieval data is incomplete. The speech model may perform well on a benchmark, but fail with the languages, accents and noise conditions the product actually encounters. The evaluation team may identify a problem, but the result never reaches the system backlog. A collection supplier may deliver accepted files while the implementation team discovers that the metadata, rights or output structure cannot support production use.
These are not isolated supplier failures. They are failures in the connection between:
Connected Delivery treats those dependencies as one engagement.
The Data & Evaluation workstream can cover:
The Implementation workstream can cover:
Each service line can be purchased independently.
Connected Delivery is used when the dependencies between them materially affect whether the final outcome will work.
A product may need new speech, video, image, text, sensor or expert-generated data before the application can work under its intended conditions.
YPAI can connect the collection and evaluation operation directly to the implementation requirement, so the accepted data unit, metadata, file structure and quality process are designed for the system that will consume them.
The problem may not require more features. It may require:
Connected Delivery links diagnosis, evaluation, remediation and implementation rather than ending with a report.
Some AI outcomes cannot be accepted through automated metrics alone. They may require:
YPAI can design the review operation and the system workflow together, so human judgment is part of the operating model rather than an external spreadsheet or manual exception.
The files may exist without sufficient clarity on:
Connected Delivery can begin with data and rights diligence, identify what is usable, define what must be repaired or collected, and connect the resulting assets to the implementation.
One vendor owns the data. Another owns the model. A third owns the application. The customer is left to reconcile interfaces, acceptance rules, failure definitions and change requests.
Connected Delivery creates one coordinated scope for the dependencies that cross those boundaries.
YPAI can own the complete connected workstream or operate alongside your existing model provider, cloud environment, systems integrator, data supplier or internal engineering team.
The first question is not: Which model should we use?
What must the complete workflow do, under which conditions, and what evidence will prove that it is ready?
The engagement starts by defining:
This creates one operating definition for both service lines.
YPAI maps the workflow before proposing the solution.
That includes the current process, system boundaries, decision points, users, data sources, failure modes, controls and required production outcome.
The gap may sit in:
The purpose is to distinguish what must be built from what must be collected, evaluated, repaired or governed.
The engagement establishes how the complete outcome will be judged. Depending on scope, acceptance may include:
The same acceptance model guides both workstreams.
Data & Evaluation and Implementation may run sequentially, in parallel or through repeated evaluation and improvement cycles.
The engagement is structured around the dependency, not around forcing every project into one fixed process.
A system is not accepted merely because its components run individually. YPAI tests the connected result against:
Findings feed back into the data, evaluation method, application or workflow as required.
The final package may include the system, data assets, evaluation material, controls, documentation and operating records needed by the agreed scope.
Production monitoring and improvement can continue as a separate managed workstream where required.
The customer needs a production system, but the required data or knowledge base is incomplete. The work may combine data sourcing or collection, rights review, annotation or preparation, evaluation-set creation, application development, integration and acceptance testing.
The customer has built an AI system but lacks reliable evidence that it is ready. The work may combine workflow analysis, evaluation design, benchmark or regression-set creation, expert review, failure analysis, application changes and monitoring.
The customer knows performance is weak but does not yet know where the defect sits. The work may combine sample analysis, error taxonomy, automated and human evaluation, data-gap diagnosis, prompt or system changes, new data or labels and repeated regression testing.
The customer needs to validate the complete operating model before committing to scale. The pilot may test data availability, participant or expert mobilisation, system integration, reviewer workflows, quality controls, acceptance, rights, unit economics and operational ownership.
Production is a separate decision after pilot review and acceptance.
A credible AI engagement should leave more than a demonstration and a presentation. Depending on scope, delivery artefacts may include:
These artefacts make the engagement reviewable by engineering, product, procurement, security and governance stakeholders.
They also reduce reliance on claims that cannot be independently inspected.
Connected Delivery describes the commercial engagement: AI Data & Evaluation and AI Implementation coordinated around one outcome.
Controlled Delivery is the operating discipline underneath that engagement.
Depending on the project, it can include:
Controlled Delivery is not a third service line. It is the control layer used to make either service line, or a connected engagement across both, inspectable and accountable.
Connected Delivery does not require the customer to replace its existing technology estate. YPAI can work with:
The architecture is selected for the workflow, control requirements and operating environment.
The engagement may cover the complete implementation or a defined connected workstream inside a larger engagement.
A useful pilot does more than demonstrate that a model can generate an answer. It should test the assumptions that matter to the operating outcome.
Pilot scope, commercial terms, responsibilities and acceptance are agreed with the customer and documented for the engagement.
You do not need to know in advance whether the answer is more data, a different evaluation method, a new agent, a RAG system, an integration or a combination. A useful first brief explains:
YPAI can turn that into a connected scope, workstream plan and acceptance model.
Bring us the outcome. We will connect the work required to deliver it.
No. YPAI has two independently purchasable service lines:
Connected Delivery is an engagement model used when work from both service lines must be coordinated around one outcome.
No. Many projects need only Data & Evaluation or only Implementation.
Connected Delivery is appropriate where a material dependency crosses the two workstreams, such as when an application requires new data, a deployed system needs a custom evaluation operation, or evaluation findings must feed directly into implementation.
Connected Delivery is the coordinated commercial engagement.
Controlled Delivery is the underlying operating discipline covering scope, acceptance, rights, security, quality, auditability, change control and handover.
Yes. YPAI can own the complete connected scope or work alongside internal engineering, a cloud provider, a systems integrator, a model provider, a data supplier or another specialist vendor.
Responsibilities and interfaces are defined during scoping.
The engagement can begin by validating the available data, knowledge sources, rights, coverage, quality and suitability for the intended application.
The result may be to use the existing assets, repair them, create an evaluation set, source missing material or collect only the identified gaps.
Connected Delivery can focus on evaluation, data remediation, human review, monitoring and targeted system improvement.
It does not require rebuilding an application that already meets the requirement.
Success is defined through project-specific acceptance criteria.
These may include functional performance, evaluation results, task completion, human-review outcomes, data quality, integration behaviour, operational controls, latency, exception handling or handover requirements.
Success criteria are defined per system.
Pricing depends on the scoped workstreams and accepted outputs. Relevant factors may include:
Commercial units and acceptance are defined together during scoping.
Yes. A customer-specific pilot can test the most material delivery assumptions before production scale.
Commercial terms, responsibilities, acceptance and the production decision are defined for the engagement.
YPAI supports project-specific data governance, documentation, provenance, DPA, review, retention, deletion, transfer and operational-control requirements.
The applicable obligations depend on the system, data, intended use, parties and legal framework.
Those obligations are scoped into the engagement when they apply.
Depending on scope, YPAI can support:
Ongoing support is scoped separately from the initial delivery.