Services
Concrete Data & AI services with explicit scope and limits.
These offerings cover RAG Reliability Audit, data-pipeline work and applied ML or analytics. Each one states the problem types, typical deliverables, fit criteria and what is out of scope.
Scope depends on the available data, the current system, the decision that matters and the evidence needed to judge the work.
RAG Reliability Audit
Audit an existing RAG system or prototype to find stale, conflicting or insufficient evidence, measure false-answer and deferral behaviour, design answer/defer criteria and produce prioritised practical recommendations.
Problems this can address
- A RAG prototype or production candidate answers when evidence is stale, conflicting or insufficient.
- Retrieval and answer quality are not measured with explicit precision, coverage and deferral criteria.
- Teams lack a representative evaluation set or a clear answer/abstain policy.
- False answers are observed anecdotally without a structured failure taxonomy.
- Model, retrieval and document-lifecycle issues are difficult to separate.
Typical deliverables
- RAG Reliability Audit of the current retrieval and answer path.
- Evidence-conflict, staleness and sufficiency findings.
- False-answer and deferral analysis.
- Abstention-policy recommendations.
- Evaluation dataset and metric design where needed.
- Prioritised remediation and limited-pilot recommendations.
- Technical documentation for engineering and product stakeholders.
Good fit
- There is an existing RAG prototype, corpus or evaluation objective.
- Representative examples, documents, logs or test questions can be shared through an approved process.
- The team can define when the system should answer, clarify or abstain.
- Limitations and trade-offs can be discussed openly.
Not a good fit
- Guaranteed answer quality or correctness is expected without representative data.
- The request is only to add an LLM without a defined user problem.
- Confidential source material cannot be accessed through an approved process.
- A compliance certification, legal sign-off or security audit is required.
- A managed production SaaS or public API is expected as the immediate deliverable.
Published synthetic study
The RAG Reliability Study documents a pre-registered answer/defer gate on a frozen synthetic holdout, with explicit precision, coverage, false-answer metrics and limitations. Results apply only to that benchmark and do not claim production validation.
Professional capability
Experience designing and evaluating RAG, LLM and multimodal-model workflows in professional settings. Employer, client and internal evaluation artefacts remain confidential.
Data pipeline modernisation and quality
Migrate or improve analytical workflows with reproducible transformations, explicit validation, data-quality controls and practical handover.
Problems this can address
- A recurring SQL, SAS or ETL process is slow or difficult to maintain.
- Data-quality issues are detected late or handled manually.
- Legacy logic is poorly documented and difficult to reconcile.
- PySpark or Databricks adoption lacks a controlled migration plan.
Typical deliverables
- Current-state technical audit.
- Migration and reconciliation plan.
- PySpark or Databricks workflow implementation.
- Reusable parameterised transformation components.
- Automated data-quality and validation controls.
- Operational documentation and handover guidance.
Good fit
- A concrete workflow and expected outputs already exist.
- Legacy and target results can be compared.
- Data access and technical ownership are available.
- The organisation accepts a staged migration with validation.
Not a good fit
- A full platform replacement is expected without discovery.
- No owner can explain the existing business rules.
- Source data cannot be accessed or profiled.
- Identical performance gains are expected from another case study.
Published case study
The analytical pipeline modernisation case study documents verified migration, reusable PySpark components, automated quality controls and the reported change from weeks to approximately two hours.
Applied ML, NLP and decision analytics
Develop analytical or machine-learning workflows that connect a defined decision problem with data preparation, evaluation and usable outputs.
Problems this can address
- A prediction, prioritisation, segmentation or automation problem lacks a structured baseline.
- Manual document or analytical review is difficult to scale.
- Business metrics and model outputs are disconnected.
- Stakeholders need decision-ready analysis rather than an isolated notebook.
Typical deliverables
- Problem framing and baseline definition.
- Exploratory analysis and data diagnostics.
- Predictive modelling, NLP or segmentation workflow.
- Evaluation design and error analysis.
- Decision-oriented outputs or reporting.
- Technical documentation and stakeholder communication material.
Good fit
- A concrete decision or operational workflow can be described.
- Representative historical or labelled data exists.
- A baseline and evaluation criterion can be agreed.
- The result can be reviewed by technical and business stakeholders.
Not a good fit
- A model is requested before the decision problem is defined.
- Guaranteed prediction performance is required.
- The available data cannot legally or ethically support the proposed use.
- A dashboard alone is expected to resolve unclear data ownership or quality.
Professional capability
Experience across classification, regression, clustering, NLP, analytics, reporting automation and stakeholder-facing delivery in professional settings. Organisation-specific artefacts remain confidential.
Ways an engagement can start
- 01
Focused diagnostic
A bounded review of the current problem, data, system and evidence, ending with findings and a prioritised next-step plan.
- 02
Fixed-scope implementation
A defined build or migration increment with agreed inputs, outputs, acceptance criteria, validation and handover.
- 03
Embedded external support
Part-time or fixed-term contribution inside an existing Data, AI, analytics or product team with explicit ownership and communication boundaries.
Useful inputs before a first conversation
- The current workflow, system or prototype.
- The decision or process that needs to improve.
- Available data, documents, logs or examples.
- Existing baseline, metrics or expected outputs.
- Technical, security and confidentiality constraints.
- Stakeholders who will evaluate or operate the result.
What this portfolio does not promise
- Guaranteed model or business outcomes.
- Production readiness without technical and operational assessment.
- Security or compliance certification.
- Identical results across different data, systems or organisations.
- A tool-first implementation without a defined problem and evaluation approach.
Start with the problem and the evidence available.
A useful first message explains what exists today, what needs to improve, who will use the result and which constraints matter.