/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.
01
The Problem
Most organizations don't have an AI problem.
They have a foundation problem.
/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.
AI doesn't fix this. It amplifies it.
02
The Missing Layer
Most AI work skips the
part that actually matters.
Brilliant engineers ship technically impressive systems that nobody adopts. Not because the tech is wrong — because nobody mapped how the work actually gets done, and nobody brought the people along.
AI doesn't replace process design. It depends on it. And no model — however capable — fixes a workflow that nobody documented, owned, or agreed on.
That's the layer we live in. People and process, sitting between the engineering team and the business — and we'll be doing this work for decades.
03
Services
Data, AI, and the
people layer.
— 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 / Process & People
Process & Adoption
The layer most AI work skips. We map real workflows, harmonize them across teams, and train the humans who'll use the system — because nothing else matters if nobody adopts it.
- Workflow mapping
- Cross-team process design
- Training & change management
- Governance & risk frameworks
04
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.
ii.
Build
We integrate sources, harmonize processes across teams, and ship the systems that run on top of them — alongside the people who'll use them. Engineering with the business, not at it.
iii.
Deploy
We integrate into the real workflow and train your teams. Demos don't count. Usage does.
05
Who's Behind This
Senior-led, by design.
I'm a researcher and educator. For more than a decade, I've studied the layer this site is built around — the human side of how software, data, and AI work actually succeed or fail inside organizations.
My research explores how to curate data from heterogeneous sources and integrate it in ways that are both insightful and traceable — work that sits at the intersection of Software Engineering and Computer-Supported Cooperative Work. Over 150 publications. Leadership at the field's major conferences. A PhD from the University of São Paulo, a visiting year at UC Irvine, and an academic home at Northern Arizona University.
The gap I document in research is the same gap I keep watching organizations stumble into. Brilliant engineers, real business problems, and almost no one minding the process and the people in between. The technology gets the spotlight. The unglamorous middle is where the work actually lives or dies.
DataTruth AI grew out of that observation. I led an NSF I-Corps team through structured customer discovery — engineering managers, consultants, CTOs — and the same pattern surfaced again and again: brilliant teams stuck doing janitorial work on their own data, insights that never reach decisions, dashboards nobody trusts.
That's the work I do here. Senior-led engagements from someone who has spent a career on the human side of building things — and refuses to leave it to chance.
— Igor
06
How We Work
Three ways to start
working together.
— 01
Audit & Roadmap
A focused diagnostic of your data, workflows, and AI readiness.
- Where the foundations are weak
- Where AI will actually help
- What to do — and what to stop
— 02
Build Engagement
End-to-end delivery of a working system, integrated into how your teams already work.
- Data integration & harmonization
- AI systems on solid foundations
- Process design & team training
— 03
Advisory
Senior support for the people inside your organization doing this work.
- Architecture & governance review
- Hiring & team structure
- Strategy for in-flight initiatives
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