Lanciter proof hub

Chris Clark

Enterprise AI Deployment Strategy | ERP Workflow Transformation | Governed Delivery

Enterprise deployment leader with 30 years delivering PeopleSoft and ERP programs in complex public-sector environments. I now apply the same discipline to Codex and OpenClaw workflows, operational systems, validation, adoption, and controlled handoff.

30 yearsEnterprise ERP delivery
300+Coordinated onboarding and integration tasks
WaterlooHonours Mathematics, Operations Research + Statistics
Codex + OpenClawGoverned workflow implementation

Selected deployment proof

Evidence from enterprise delivery and working systems.

PeopleSoft is the lead domain. The supporting work shows the same delivery discipline across persistent automation, secure mobile access, product prototypes, portals, and commerce.

PeopleSoft + Codex Workflow Deployment Brief

A sanitized defect-to-technical-spec workflow showing how Codex can support ERP delivery without bypassing delivery-lead review. The pattern starts from approved source evidence, moves through a review workbook, generates bounded technical-spec output, and keeps open questions visible.

Sanitized workflow proof. It demonstrates the operating method without implying live use or exposing client material.

PeopleSoft Codex workflow Sanitized demo Governed delivery
1

Workbook-first review

Confirmed facts, missing evidence, reviewer notes, confidence, and approval fields are separated before document generation.

2

Generated technical-spec output

Draft specs are generated only from reviewed workbook rows, with open questions preserved instead of invented.

3

Validation checks

Checklist steps verify inputs, workbook review, document quality, manifest coverage, and delivery gate readiness.

4

Manifest and evidence tracking

Generated files are tied back to source rows, review status, validation status, and unresolved delivery risks.

5

Human approval before artifacts

The delivery lead approves the workbook and generated documents before anything becomes client-facing.

Persistent operational system

Property decision pipeline and Mission Control

A nightly workflow ingests source events, preserves lineage in SQLite, enriches and scores candidates, generates reports, and exposes dashboard state while keeping human veto separate from machine ranking.

  • Verified against more than 2,200 active records
  • Deterministic ingestion, monitoring, and recovery paths
  • Human feedback and overrides remain explicit

Secure operational delivery

Timesheet Ops and native iPhone companion

A private FastAPI and SQLite workflow extended to a native SwiftUI companion through an authenticated, tailnet-only mobile API with authoritative server totals and protected offline cache.

  • Gate 1 read-only path proven on a physical device
  • Pairing, offline cache, service restart, and recovery verified
  • Mobile writes, deletion, submission, invoicing, and email remain excluded
Full-stack product build

HoldingsOS Portfolio Intelligence Prototype

HoldingsOS by Lanciter is a local-first rental portfolio intelligence prototype for small landlords. It connects property identity, ownership context, rent and lease details, cash-flow analysis, refinance scenarios, and documentation in an auditable operating view while keeping private data local and public claims inside prototype and private-discovery boundaries.

  • Canonical property model and linked operating records
  • Cash-flow and refinance analysis tied to property identity
  • Tests, browser checks, documentation, and privacy boundaries
Client platform delivery

Health and Performance Operations Portal Build

Codex-assisted implementation support for a premium health and performance coaching business, focused on client onboarding, coach/admin workflows, secure access boundaries, and delivery handoff. The client is not named from this site.

  • Client, coach, and admin workflow surfaces
  • Onboarding, diagnostics, media, and task visibility
  • Role-based access, RLS patterns, and guarded handoff
Commerce implementation proof

Shopify Storefront Implementation and QA

Staged storefront cleanup and QA work for a Shopify commerce experience, with emphasis on customer buying paths, theme-safe changes, screenshot evidence, mobile/desktop review, and publish readiness.

  • Navigation, collection, product, and cart-path review
  • Draft theme changes separated from live publish decisions
  • Desktop and mobile evidence captured before handoff

How I work

Governed deployment, not one-off prompting.

Each workflow starts with bounded evidence and ends with reviewable output, explicit approval, operating documentation, and a recovery path.

Bounded inputs
SRCApproved evidence
CTXWorkflow context
CHKAcceptance criteria
OPSOperating constraints
Bounded discoveryInspect approved sources.
Evidence and lineagePreserve source-to-output trace.
Validation and evaluationApply checks and review criteria.
Human decisionReview, decide, authorize.
Controlled handoffMove approved work to its destination.
Runbooks and recoveryDocument operations and failure paths.
Reviewable outputs
Evidence-backed artifacts
Explicit decisions
Controlled system handoff
Operating runbook
The method stays bounded by source evidence, acceptance checks, human decisions, and documented operating controls.
1

Bounded discovery

Agents inspect approved evidence, templates, reports, and workflow context to organize what is known and what still needs review.

2

Evidence and lineage

Outputs, references, manifests, screenshots, and decisions are packaged so the path from source to artifact can be reviewed.

3

Validation and evaluation

Checklists, scripts, and review rules catch gaps early and keep unsupported assumptions out of generated artifacts.

4

Human decision

People review evidence, fill gaps, authorize generated output, and own the final delivery judgment.

5

Controlled handoff

Approved work moves to the right documents, teams, folders, environments, and operating processes with open risks still visible.

6

Runbooks and recovery

Document operating steps, outcomes, and recovery paths so the workflow is easier to run, support, and improve.

Principles: / Human in control / Evidence first / Validate early / Clear handoffs / Bounded claims

Deployment capabilities

Capabilities for enterprise AI deployment.

Workflow discovery and mapping

Turn business outcomes, source evidence, stakeholder constraints, and current-state work into a bounded deployment problem. Separate confirmed facts, open questions, dependencies, and decisions before implementation begins.

Deployment planning and sequencing

Translate complex work into milestones, workstreams, acceptance criteria, test moves, operating rhythms, and readiness gates. Keep scope and sequencing tradeoffs visible to customer, product, engineering, security, and operations teams.

Enterprise integration and technical coordination

Work across ERP architecture, PeopleTools, SOAP and REST APIs, SQL, Python, web systems, infrastructure, and production support. Coordinate specialists without losing the end-to-end workflow or customer outcome.

Validation, evaluation, and evidence

Define quality criteria before the run, then verify outputs through scripts, checklists, browser review, manifests, and source lineage. Preserve failures and unresolved questions instead of smoothing them out of the handoff.

Governance, adoption, and operational readiness

Design approval points, access boundaries, human decisions, recovery paths, and production-readiness evidence into the workflow. Treat adoption and operating ownership as delivery requirements, not post-launch cleanup.

Reusable skills, runbooks, and internal tooling

Convert successful delivery patterns into reusable skills, runbooks, diagnostics, dashboards, and controlled automations. Each implementation should leave the next deployment easier to explain, evaluate, operate, and improve.

Credentials and working context

Quantitative foundation. Enterprise operating judgment.

Education
Honours Bachelor of Mathematics, Operations Research (Statistics Minor), University of Waterloo
Languages
English and French, full professional fluency
Security
Government of Canada Secret (Level II) security clearance

Discuss enterprise AI deployment.

For deployment, Codex, enterprise workflow, recruiting, or referral conversations, send a short note. I can share the current resume and sanitized proof packet directly.