A senior-led AI & data consultancy — Est. 2026

Fix the data. Unlock the AI.

Most AI initiatives stall in the same place: tangled data, undocumented workflows, and dashboards nobody trusts. We fix that layer first — then build AI systems your teams actually use.

Engagements
Senior-led
No juniors on the work.
Deliverables
Working code
Not slides, not pilots.
Book a consultation
01 The Problem

Most organizations don't have an AI problem.
They have a foundation problem.

/01

Fragmented systems

Your data is scattered across Jira, GitHub, Slack, drives, and a graveyard of spreadsheets. There is no single source of truth — and everyone on the team already knows it.

/02

Untrusted outputs

AI built on noisy data produces noisy answers. Teams stop trusting the system. Then they stop using it.

/03

Reactive decisions

Dashboards report what already happened. Nobody can answer why — let alone what to do next.

Fig. 01 — the foundation problem, drawn out
Fragmented data sources versus a unified data foundation. A two-panel schematic. The left side shows scattered source systems with tangled connections. The right side shows the same sources flowing into a unified data foundation, then into AI. — BEFORE what teams have JIRA SLACK GITHUB DRIVE SHEETS spread // inconsistent // untrusted — AFTER what we build JIRA SLACK GITHUB DRIVE SHEETS DATA FOUNDATION AI integrated // standardized // AI-ready

AI doesn't fix this. It amplifies it.

02 Services

Foundation, build, sustain
— in that order.

— 01 / Foundation

Data Foundation

We integrate, standardize, and clean the data underneath your business — so AI has something solid to stand on.

  • Source integration
  • Schema standardization
  • Quality & lineage
— 02 / Build

AI Enablement

Internal copilots, automated triage, decision support. We wire AI into the work people actually do — not the work the slide deck describes.

  • Internal copilots
  • Workflow automation
  • Custom evaluation
— 03 / Sustain

Governance & Training

Safe, confident, useful AI — backed by clear policies, real evaluation, and the team training that turns pilots into permanent practice.

  • Policy & risk frameworks
  • Team training
  • Adoption tracking
03 Process

Built for fast value —
and long durability.

i.

Assess

We audit your data, workflows, and AI readiness. We surface what's broken — and what's leverageable. Honest, not flattering.

— Weeks 1 – 3
ii.

Build

We integrate sources, standardize metrics, and ship the AI systems that run on top of them. Code, not concepts.

— Weeks 4 – 12
iii.

Deploy

We integrate into the real workflow and train your teams. Demos don't count. Usage does.

— Ongoing
04 Who We Are

Senior-led. Outcomes-first.
Allergic to hype.

We've watched dozens of AI initiatives fail — and almost never for the reason people blame. The model wasn't the problem. The data was a mess, the workflows were undocumented, and nobody asked the people who'd actually use the system what they needed.

Our work is unglamorous on purpose. We fix the foundations first. We ship code, not decks. And we measure ourselves on adoption — not pilots, not slideware.

05 Selected Case

From data fragmentation
to actionable insight.

— Engagement Brief

Turning a week of manual reconciliation into one working AI surface.

Context
  • Teams losing hours to reconciliation
  • Decisions blocked downstream
Challenge
  • Data spread across systems
  • Inconsistent metrics
  • Heavy "data janitoring"
Approach
  • Integrated heterogeneous sources
  • Standardized key metrics
  • Layered AI on structured data
Outcome
  • Manual effort cut sharply
  • Trust in metrics restored
  • Decisions, not reports
Start here

An AI Readiness
& Data Audit.

You leave with a roadmap, working prototypes, and a short list of things you should stop doing.

  • Data quality assessment
  • AI opportunity mapping
  • Risk & governance review
  • Working prototypes
contact@datatruthai.net →