01
Conflicting sources
Customer, product or operational data sits across applications, spreadsheets and databases. Nobody can explain which version a report or AI answer should trust.
WHY AI / DATA FOUNDATIONS
Make scattered data a foundation you can build on.
Your systems disagree, reports need manual repair, and AI cannot rely on the data it retrieves. We assess the gaps, design the architecture, and build the integrations, pipelines and controls your team needs.
Discuss your data foundationsWHO THIS HELPS
When the problem starts before the model.
02
Fragile pipelines
Manual exports and undocumented transformations break when a source changes. Freshness, failed loads and ownership are hard to see.
03
AI blocked by data
A useful AI workflow needs permissioned, traceable information. Retrieval quality, access boundaries or missing source ownership prevent a responsible rollout.
WHAT YOU GET
Working data flows, with the decisions behind them.
01
Source and gap assessment
A source inventory, current data-flow map and prioritized gaps in quality, access, freshness and ownership. We relate each gap to the business workflow it blocks.
02
Target architecture
Integration boundaries, storage and processing choices, architecture diagrams and decision records. ETL, warehouse or lakehouse choices follow the actual workload and constraints.
03
Scoped implementation
Agreed connectors, ingestion and transformation pipelines, orchestration and quality checks. The build scope names the sources and workflows included, rather than promising a company-wide migration.
04
Quality and governance
Validation rules, lineage, access and retention decisions, named data owners, and a way to detect failures or stale data. Controls are agreed with the people responsible for the data.
05
AI readiness
For the selected use case: preparation and permission boundaries for retrieval or model inputs, plus explicit gaps that still prevent reliable use. Clean data alone does not prove an AI system is ready.
06
Handover
Pipeline documentation, operating guidance, an acceptance checklist and remaining work with owners. Your team can inspect what was built and how to maintain it.
ASSESS / DESIGN / BUILD
Start narrow. Make the path repeatable.
01
Assess the real workflow
Work with the business owner and data team to trace a representative record from source to use. Inspect access, sample quality and failure points before selecting a new platform.
02
Design for your constraints
Agree the target architecture, implementation boundary, owners and acceptance checks. Document trade-offs around residency, latency, cost and the systems you already operate.
03
Build and verify
Implement the agreed data flows in increments. Check source-to-target reconciliation, missing and duplicate records, schema changes, access rules and failed-load recovery before handover.
Example acceptance checks to agree together: a failed load is visible to its owner; a source record can be traced through transformations; unauthorized records stay outside retrieval; a pipeline can be rerun without silently duplicating records.
EVIDENCE AND BOUNDARIES
Inspect the method before choosing the engagement.
The P.R.O.D. framework describes Reliable Data alongside operational control and delivery architecture. Read how a demo becomes a system for the wider production context, and Aleksander's background for who leads the work. These are methodology and experience references, not a promise of your project's outcome.
The engagement covers the agreed assessment, design and build. It does not automatically include replacing every business application, unlimited historical cleanup, formal security testing or 24/7 operations. Source access, domain decisions and client-side approvals need named owners.
A focused outcome fits a project. For recurring architecture and hands-on capacity, explore Fractional Data & AI.
BUYER QUESTIONS
What to clarify before starting.
Do we need a new data platform?
Not necessarily. Assessment should establish what your current systems can support and where they fail. A new platform is a design decision, not the default deliverable.
Can you implement, or only advise?
Both. Assess, design and build are available. The proposal explicitly separates review and architecture outputs from the connectors, pipelines and controls to implement.
What do you need from our team?
A business owner, access to the agreed source systems or representative samples, someone who can explain domain rules, and technical counterparts for deployment and handover. Access limitations are recorded in the scope.
How long does it take?
Timing follows the number of sources, data condition, access and build scope. We agree milestones after scoping; this page does not promise a fixed duration for an unknown migration.
NEXT STEP
Start with the problem you need to solve.
Bring the process that is blocked, the systems involved, and an example of unreliable data. On the 30-minute data readiness fit call, we identify whether assessment, architecture or a scoped build is the useful first step.