kaiellenson.

The problem

High-ticket sales don't happen in a single message. They depend on context, timing, the right offer, and knowing when to follow up. We built a system that connects those decisions to action.

What we built

An end-to-end engine that reads conversation history, selects from real offers and prices, sends authorized messages and offers, and schedules the next move. It runs routine sales conversations without per-message approval, with explicit exceptions handed to a human.

More than a prompt

The model makes the decision. The system carries it through: offer selection, delivery, follow-up timing, and a record of what actually happened. Conversation, commercial logic, and execution operate as one connected workflow.

The engineering underneath

Deciding and sending are separate steps. Before an action goes out, the system checks its permissions, fresh conversation state, the selected offer, and its price. Each conversation is processed in order, with delivery receipts and an operational history. An uncertain delivery is recorded as uncertain, so it isn't blindly sent twice.

How we test it

A separate replay environment evaluates decisions without sending live messages. Versioned test cases compare candidate releases against a baseline, and release checks flag regressions before rollout. Each decision keeps the version and input context that produced it.

The problem

Running a business means connecting work across departments: who's responsible, what's happening, and how it ties back to the numbers.

What we built

A custom operations platform bringing together account records, team management, training roadmaps, financial tracking, shared files, and analytics.

The engineering underneath

Teams and departments have role-based access. Shared records connect the workflows, while background processing handles work outside the browser. Typed APIs connect the interface to the database, with object storage for shared files.

The problem

AI generation takes time. Conversations need live updates. Media access and usage records need to stay consistent across both.

What we built

A platform combining real-time conversations, AI responses, image and video generation, media libraries, and a credit-based usage ledger.

The engineering underneath

Generation runs in the background while the interface shows progress. Successful results connect to stored media and usage records. Private media uses signed access, conversation reads are scoped to the user, and balance changes have a transaction history.

The problem

A routine code push and a destructive history rewrite can look similar at a glance. Teams need enough context to know which changes deserve attention.

What we built

A GitHub integration that watches repository events, compares commit history, checks selected code and dependency changes, and sends severity-based alerts to the team.

The engineering underneath

Related events are correlated over time, including branch deletion followed by recreation. Duplicate alerts are suppressed, but new evidence can trigger a fresh warning. Delivery state is recorded only after an alert is accepted. The scope is targeted change monitoring, not a claim to catch every security issue.

The problem

Finding a page is only the start. Results need to be checked for relevance, corroborated, and organized into work that someone can act on.

What we built

A background pipeline for discovering candidate pages, classifying them, scoring evidence, and routing findings into review and follow-up tasks.

The engineering underneath

Inexpensive checks run before deeper page extraction. Network-heavy work has bounded concurrency, and each decision records the evidence used at the time. Organization context travels with each job; findings and follow-up work remain distinct stages.