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TKOSolutions
Todd Kovalsky · Founder & Principal

I turn complex operations into work that gets done.

Clear decisions. Working systems. One accountable principal.

I bring 20+ years across operations, product, and healthcare transformation to two kinds of work: making a healthcare mandate executable, and building an owned client pipeline with governed AI. I map the decisions, design the controls, and deliver the agreed scope. You work with me from the first conversation through handoff.

Discuss Your ProjectInspect My Work

20+ Years

Operations & Product Experience

RachelOS

Single-Operator Proof

Human Review

Before AI Outreach

Single Principal

No Delegated Associates

Work you can inspect

Name the owner. Show the handoff. Record the evidence.

A green status report does not show what happens when a rule is missing, a vendor handoff fails, or a client asks something the record cannot answer.

I follow the work across team and system boundaries. I identify the missing rule, handoff, or owner, then turn the finding into a specific delivery plan. Every proposed change has an acceptance test and a way to check whether it helped.

My background spans regulated operations, product ownership, and healthcare transformation. RachelOS shows how I turn that operating detail into working rules and screens.

The Core Thesis

Model calls are cheap. The surrounding decision system is the work:

Model Calls Are Commodities

A useful model draft needs recorded facts, visible gaps, a permitted action, and an accountable reviewer. Deterministic rules control workflow state; model output remains proposed prose.

Fail-Closed Human Approval Gate

RachelOS routes AI-assisted outreach through review of the exact message. Its model wrapper records attempts and classifies failures. These controls make review and failure handling explicit; they do not guarantee factual accuracy.

Choose the work you need

Healthcare readiness and owned demand

Two bounded engagements. Clear outputs, pricing, and ownership before work starts.

Commercial Engine A$15,000 fixed · 3–4 weeks

Enterprise Transformation & Readiness Sprint

For Health Plans, Payers & Regulated Healthcare Organizations

For leaders facing CMS-0057-F, state prior-authorization mandates, or core platform transitions. Trace one approved obligation through the affected teams and systems, then identify the missing controls, owners, dependencies, and release evidence.

The 9-Part Implementation Chain

Policy → Obligation → Capability → Workflow → System → Control → Owner → Evidence → Outcome

  • Obligation Register & Cross-System Impact Map
  • Gap, Dependency, and Accountable Ownership Matrix
  • Executable Implementation Backlog with traceable acceptance criteria
  • A sponsor readout and baseline plan; inspect the synthetic lab before engaging
Explore the Readiness SprintRun Decision Lab Trace →
Commercial Engine B$7,500–$15,000 build · fixed scope
$1,500–$3,000 per month optional

Owned Demand & Governed AI Engine

For Wealth Advisory (RIAs), Boutique Practices & High-Consideration Firms

For wealth advisory firms, luxury services, and boutique practices that rely on third-party leads and scattered follow-up. Build useful buyer content, inquiry capture, and an accountable next step on channels you control.

The Governed Operating Model

Buyer Education → Permission-Based Inquiry → Relationship Context → Human-Reviewed Action

  • Buyer education that answers research questions before an inquiry
  • Recorded relationship context with known facts and open questions
  • A daily action view with accountable next steps
  • Exact-message human review for AI-assisted outreach
Explore Private Client OSInspect RachelOS Proof →

Living Technical Proof

Inspect the RachelOS workflow

RachelOS is an independent, single-operator system. These snapshots show interface behavior. Its code backs separate controls for human review and recorded model attempts; neither establishes healthcare deployment or client results.

Own the path from buyer research to inquiry

Useful content gives buyers a reason to come to you. The operating system carries their question into the next conversation.

  1. 01Answer a real buyer question
  2. 02Capture an inquiry with permission
  3. 03Keep context, ownership, and the next step together
Explore the owned-demand build

The model prepares. The operator decides.

RachelOS moved away from autonomous AI nurture after documented factual and quality failures. The current pattern puts exact-message review around AI-assisted outreach.

  1. 01Keep recorded facts separate from proposed prose
  2. 02Record model attempts and classify failures
  3. 03Review the exact message before AI-assisted outreach
Inspect the controls
Loading walkthrough…
Walkthrough: Automated walkthrough of TKO's educational prototype. Rules and identifiers are synthetic; no PHI or clinical determination. Use the interactive lab to change the facts yourself.
Run the interactive decision lab
Operator Review

Known context and open questions

Operator Review

This snapshot shows the operator reviewing relationship context and missing information. Exact-message approval is a separate, code-backed control.

The Canonical Priority Queue

Computed next-best action

The Canonical Priority Queue

A daily action view groups work by the next step and makes missing qualification information visible. This screenshot does not prove a measured outcome.

Read the Full RachelOS Case Study & Technical Breakdown

Execution Rigor

The 9-Link Implementation Chain

The method used to reconstruct operational truth across complex payer transformations:

Policy
Obligation
Capability
Workflow
System
Control
Owner
Evidence
Outcome

One principal · Priced up front

Bring the work that needs an accountable owner.

Bring one regulatory deadline under pressure or one high-consideration pipeline losing momentum. Work directly with Todd Kovalsky.

Discuss Your ProjectScope and Pricing

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