Digion

Applied AI engineering · Digion Inc.

Most AI systems can do anything. The ones worth building can't.

I build production AI for teams working under real constraints — data that isn't allowed to move, decisions someone can be made to justify, outputs that end up in front of an auditor, a regulator, or opposing counsel. The engineering is the easy half.

Where constraints attach to an AI pipeline A four-stage pipeline — data, model, decision, people — with the regulatory and contractual constraints that bind at each stage listed beneath it. What you are building Data Model Decision People affected Where the rules attach Residency and retention Business associate agreements Consent and minimization Training and reuse terms Provenance and versioning Subcontractor exposure Bias and adverse impact Explainability of outcome Logging and reproducibility Notice and disclosure Recourse and appeal Liability when it's wrong
Every consequential system is defined by what it isn't allowed to do. Knowing where the constraint attaches — and building so it holds without crippling the thing — is most of the work.

Engagements

Four ways this usually starts.

Fixed scope where the work is knowable, retained where it isn't.

Build

AI engineering

End-to-end delivery on production systems: retrieval over messy internal corpora, agent workflows that actually complete, fine-tuning and evaluation harnesses, and the unglamorous integration work that decides whether any of it survives contact with real users. Most engagements start here and grow.

Contain

Private and self-hosted inference

Open models running inside your own environment when the data can't leave it — or when the API bill has outgrown the convenience. Replaces third-party inference in systems already in production, without a rewrite.

Assess

Governance, audit, and exposure review

A fixed-fee review of where your AI systems meet the rules that bind them: vendor terms and what they actually permit, data exposure, model documentation, and adverse-impact testing where decisions affect people. Delivered as findings you can hand to counsel or a diligence process.

Own

Fractional AI lead

Senior technical ownership for teams shipping AI without a senior machine learning voice in the room. Architecture, hands-on build, vendor decisions, and the risk calls that come with them — part-time, on defined scope rather than headcount.

Selected work

Systems where getting it wrong had a name attached.

Client names available on request.

Employment technology

Independent bias auditing of automated employment decision tools

Scope
Adverse-impact audits of hiring and promotion systems under New York City Local Law 144.
Constraint
Statutory methodology, auditor independence, publicly posted results.
Outcome
Audit practice run under the Paritas name; available for independent engagements.

Hospice & home care

Clinical documentation platform built to a HIPAA-adjacent standard from the first commit

Scope
Product MVP with AI-assisted documentation for care teams.
Constraint
Patient records in scope from day one; no third-party model exposure permitted.
Outcome
Shipped with the data-handling posture settled before it got expensive to change.

Behavioral health

Self-hosted models under a cloud business associate agreement

Scope
Matching and intake tooling for clinical placement consultants.
Constraint
Sensitive intake material that could not reach a commercial endpoint.
Outcome
Full stack on managed infrastructure with inference inside the covered boundary.

Dermatology

Production AI features in a clinician-facing product

Scope
Ongoing engineering on a live product used in practice settings.
Constraint
Clinical accuracy expectations alongside patient data handling.
Outcome
Continuing engagement across build and architecture.

Who you'd be working with

One senior engineer, not an agency bench.

Digion is Tyler Horan. I spent a decade as a software engineer before finishing a doctorate in computational social science, and I've spent the years since building AI systems for organizations where the output has consequences for somebody.

That combination is the point. Most people who can put a model into production have never read the agreement governing the data going into it, and most people fluent in that language have never shipped anything. The problems worth paying for live where those meet, and they don't get solved by one group writing findings for another.

I work directly with founders and engineering leads — no account managers, no handoff to a junior team. Agent-assisted workflows are part of how I deliver, disclosed to every client, and accountability for what ships is mine either way.

Doctorate
Computational social science, The New School for Social Research
Teaching
Lecturer, Data Analytics and Computational Social Science, UMass Amherst
Governance
ForHumanity — independent AI audit and assurance
Board
Director, Creative Capital Foundation
Writing
Three books under contract on technology, work, and attention

Start here

Tell me what your system isn't allowed to do.

First conversation is thirty minutes and mostly questions about your stack and your obligations. If it isn't a fit, I'll say so and point you somewhere better.

tyler@digion.cloud