Applied AI engineering / co-founder

Acelera

Applied AI systems built around real workflows, deterministic controls, tool calls, human approval, evaluation, and explicit failure recovery.

Illustration of a track that branches at a guard gate and a human review checkpoint — IllustrationAI-generated illustration: a bounded pilot with explicit gates and human review.

At a glance

Period: January 2026 to present; independent side project
Role: Co-founder; applied AI engineering lead
Team: Acelera team working with client stakeholders
Users: Operations and business teams evaluating AI-assisted workflows
Status: Active AI implementation studio; Clara Voice is a tested pilot prototype, not a production deployment

SystemClara Voice
ControlTools + deterministic rules
ReviewHuman escalation
Testsautomated test sources

My part

Mapped operational workflows and built TypeScript and Python LLM orchestration with tool calls, deterministic rules, human approval, evaluation, and failure recovery.

Tradeoff

Let the model interpret natural language while deterministic software controls availability, required evidence, consent, allowed actions, and escalation.

Evidence

Clara Voice includes test fixtures and validation code. Client work remains NDA-safe and is described as discovery, prototype, and pilot handoff rather than production deployment.

Sanitized engagement model

Scoping one observable constraint

Stakeholder interviews and current handoffs become a bounded AI pilot. The model interprets natural language, tools expose allowed capabilities, deterministic rules control decisions, and failures escalate to human review.

Workflow signals / sanitized

Repeated status entry across tools

Exception ownership not visible at handoff

Pattern condensed from stakeholder interviews

Current handoff

01Request arrivesInbox

02Context rebuiltObserved delay

03Owner assignedSpreadsheet

04Decision sharedChat

Pilot boundaryPreserve context at handoff 02

Owner: Operations lead
Signal: Time to assign
First release: One workflow

Client details removed. This is the engagement model, not a client artifact.

The brief behind the brief

Several engagements began with a request to use AI. I interviewed the people doing the work, mapped the handoffs and failure cost, and reduced the request to one workflow with an observable success signal.

Illustration of a funnel narrowing many loose shapes into one cube — IllustrationAI-generated illustration: discovery narrows a vague request into one constraint.

Clara Voice: language is probabilistic, actions are not

Clara Voice interprets natural-language appointment requests. Tool calls expose availability and workflow actions, while deterministic rules control required documents, consent, allowed transitions, and when the system must stop.

Failure handling belongs in the product

Provider failure, missing documents, unavailable slots, and clinical-risk signals route to explicit fallbacks or human review. The prototype includes test sources across orchestration, validation, and failure paths.

A public product, not a client claim

Brand Kit Generator turns an 8-stage interview into a decision tool. Each stage has a gate for rejecting vague positioning, voice, or visual choices before producing reusable brand outputs. The methodology and source are public.

Illustration of eight gated stepping stones leading to a stack of swatches — IllustrationAI-generated illustration: eight interview stages with gates before the brand outputs.

Implementation stack

TypeScript and Python orchestration connect LLM APIs, tool calls, voice providers such as ElevenLabs and Twilio, deterministic policy checks, evaluation fixtures, and human escalation paths.

Claim boundary

The healthcare work progressed from stakeholder interviews through a tested prototype and defined pilot handoff. This case does not claim a production deployment.

Project linksacelera.agency Brand Kit Generator