Portfolio

Workflow Systems I Build

Typical AI systems for operational bottlenecks. Details are generalized to respect client confidentiality.

Internal systems / software delivery

AI Software Delivery Workflow

Internal operations

Problem

Software delivery still depended on manual coordination across planning, task assignment, development, QA, deployment, and review. Too much flow control lived in the operator’s head, and each handoff required active oversight.

System

A private AI workflow built around a central orchestration layer. A lead agent receives a project brief, generates a plan, creates tasks on an internal board, and routes work to a development agent. Completed tasks move to a QA agent for validation. Approved work is passed to a DevOps agent, which provisions a dedicated environment, configures infrastructure, and deploys the result for review on a local host.

Result

Turned a fragmented multi-step delivery process into a repeatable AI-driven workflow with clear handoffs across planning, development, QA, and deployment. The system has been used to build multiple internal products, including an intelligence platform, an internal project board, and the current website. In practice, this workflow can take a spec or task brief in the evening and return a review-ready build or feature iteration by the next morning.

Slack intake · AI orchestration · internal task board · local Git repo · homelab deployment · VM provisioning · local review environments

Mission Control board used to coordinate task flow across the AI delivery system.

Used to build internal products including Radar, Mission Control, and the current website.

Internal Knowledge System

Retrieval

Document-heavy operations team

Problem

A 20-person team spent hours each week searching SOPs, project files, and scattered documentation for answers that already existed somewhere. Knowledge lived in shared drives, inboxes, and people's heads.

System

Private retrieval system indexed to internal documentation — surfaces answers with source references through a simple internal interface.

Result

Shift from manual lookup to fast retrieval. Senior team members freed from routine operational questions.

Inbound Lead Triage

Automation

Owner-led professional services firm

Problem

Inquiries arrived through multiple channels — forms, email, referrals. The founder personally reviewed each one, extracted details, and decided which to pursue. This consumed significant time daily.

System

Automated capture from all channels, extraction of qualification criteria, fit scoring against ideal client profile, and structured routing.

Result

Reduced manual screening before review. Founder sees a prioritized, pre-qualified list instead of raw inquiries.

Weekly Reporting Pipeline

Pipeline

Operations-heavy small business

Problem

Weekly reporting required pulling data from four systems, combining it into spreadsheets, and writing narrative summaries. One person spent most of a day each week on this.

System

Automated data assembly from existing sources, structured report generation with narrative summaries in the team's existing format.

Result

Moved reporting effort from assembly to review. Team focuses on interpreting data rather than collecting it.

Client Communication Drafting

Generation

Service business with high client volume

Problem

Account managers spent a large portion of their day drafting routine client updates, follow-ups, and responses to common questions. Quality and tone varied across the team.

System

Context-aware drafting system based on project status, communication history, and tone guidelines. Staff review and send — the AI handles the first draft.

Result

More consistent communication quality across the team. Account managers reclaimed time for relationship work.

Document Processing Pipeline

Extraction

Research-intensive consultancy

Problem

Analysts reviewed lengthy documents — contracts, regulatory filings, reports — to extract specific data points. The process was manual, slow, and inconsistent under time pressure.

System

Extraction pipeline that processes documents against defined schemas, pulls relevant sections, and outputs structured comparisons for analyst review.

Result

Reduced manual extraction effort. Analysts spend time on interpretation and recommendations rather than data entry.

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