What gets built here

Most of what gets called AI automation is a chain of ordinary steps with one model call somewhere in the middle. The work is in the other ninety percent: retries when a third-party API times out, idempotency so a replayed webhook doesn't double-charge anyone, and a dead-letter queue you can actually open on a Tuesday morning and understand. Builds run on n8n or Make when the team wants to own and edit them after handover, and on Temporal when the workflow has to survive a process dying halfway through.

An agent earns the name when it chooses its own path: it reads the situation, picks a tool, checks its own result, and escalates when confidence drops. That autonomy is both the entire value and the entire risk, so every build ships with a bounded tool surface, a written escalation rule, and an eval suite that runs before deploys rather than a vibe check after them. Model choice follows the task — Claude, GPT, or an open-weight model — not the logo on the invoice.

Retrieval quality is a ranking problem before it is a model problem. In practice that means chunking that respects how the document is actually structured, hybrid keyword-plus-vector search rather than embeddings alone, a reranking pass, and citations rendered back to the source so a reader can check an answer instead of trusting it. The corpus lives in pgvector or Pinecone depending on whether you already run Postgres.

The unglamorous layer every other service on this page stands on. Ingestion into a warehouse, webhook receivers that verify signatures and survive replays, and event streams that keep two systems agreeing about the same customer record. Usually Airbyte for ingestion, dbt for the transforms, and Postgres as the place the truth lives.

Tier-1 deflection that customers do not resent. The assistant answers from your real help centre and resolved-ticket history, says plainly when it is not sure, and hands to a human with the full conversation attached so nobody has to start over. Confidence thresholds are tuned against your own historical tickets, which means the handoff line is set by evidence rather than by a default someone left in the config.

Enrichment, routing and sequencing built around your actual ICP definition rather than a generic template. Everything is written back to HubSpot or Salesforce as structured fields your reps already filter and report on, so the automation shows up in the pipeline review instead of in a side tool that nobody opens after month two.

A pipeline rather than a prompt box: a structured brief in, a house-style pass, a human approval gate, and publication through the CMS you already use. Every asset carries provenance — which brief, which model, which reviewer signed it off — because the first question a legal or brand team asks about generated content is where it came from.

An audit of what you have, an automation roadmap ranked by time-saved-per-dollar, or a build-versus-buy review before you commit a quarter to the wrong path. The deliverable is a written document you own outright and can hand to another vendor. A meaningful share of these end in a recommendation not to build the thing at all, which is the point of paying for the opinion.

Swift and SwiftUI, shipped to the App Store. This predates the AI work rather than sitting beside it as a side offering — fourteen years of mobile engineering means accessibility and Dynamic Type from the first screen, Core ML running on-device where privacy demands the data never leaves the phone, and a clean Objective-C interop layer when the codebase is older than Swift.