Data, AI & Technology
Data and AI only work when the foundations are real: reliable data, clear objectives, measurable validation, and engineering that can survive production constraints. Many teams accumulate data faster than they accumulate clarity. Dashboards grow, models multiply, and clarity decreases. We turn data and technology into decision infrastructure: modeling, pipelines, architecture, and evaluation. AI is introduced selectively, only when it reduces complexity instead of adding opacity.
Problem signals
- Dashboards exist, but nobody trusts the numbers.
- Manual data work is consuming the team (spreadsheets, rework, fragile scripts).
- AI pilots look promising but fail to integrate, scale, or stay safe.
- Decision-makers want speed, but the data and systems cannot support it.
Intervention
- Diagnostic-driven work: start with data readiness, risks, and decision context.
- Architecture and pipelines that reduce complexity before adding more AI.
- Measured validation: clear metrics, evaluation plans, and safe integration patterns.
Observable deliverables
- Data and pipeline diagnostic (quality, data lineage, ownership, failure points).
- Reference architecture and implementation plan (cloud or on-prem).
- Prototype pipeline/model, plus evaluation and validation summary.
- Handover-ready documentation (runbooks, monitoring notes, governance basics).
Engagement model (high level)
Phase 1: Diagnostic - audit data, systems, and decision needs. Deliver: readiness report, risk map, and a pilotable execution plan.
Phase 2: Build / Pilot - implement a small but end-to-end prototype (pipeline, model, or analytics) and validate against constraints. Deliver: prototype, metrics, and pivot points.
Phase 3: Scale / Handover - harden and operationalize. Deliver: production-ready system, monitoring notes, and runbooks/training.
Good fit if
- Your AI or analytics works in demos but fails under real variance
- You need a rigorous path from experimentation to production
- You want applied research that results in deployable artifacts
- You need AI that respects engineering constraints and safety.
Not a fit if
- You’re looking for a one-size-fits-all architecture.
- You need guaranteed outcomes without access to data and stakeholder time.
- You want automation without ownership, monitoring, and governance.