Put AI to Work
Let software read documents, answer questions and sort requests for your team.
Who it's for
- Founders whose product works and who want one AI feature in it, not an AI rewrite.
- Operations leads who want paperwork, search or triage handled by software.
- CTOs with a small team who need a senior hand on the first AI feature.
Problems it solves
- You have tried AI in a demo, but it is not safe to put in front of customers.
- Nobody can say what it will cost to run each month.
- The answers are sometimes wrong and there is no way to measure how often.
- The feature needs to respect who is allowed to see what.
What you get
- One AI feature, working inside your existing product.
- Limits on spend and usage, so cost stays predictable.
- A set of test cases that shows how good the answers are, and tells you when they get worse.
- A fallback for when the AI provider is slow or down.
- Notes your team can use to run and change it.
How an engagement runs
- 01We agree on one feature and what "good enough" means.
- 02I study your product and data, and design where the feature fits.
- 03I build it behind a switch, with guardrails and tests.
- 04We check it against real examples and tune it.
- 05I deploy it and hand over the notes.
In plain words
Your team spends hours reading, copying and sorting. I add AI to the tools you already use so that work happens on its own, safely and at a cost you can predict.
Typical examples are reading documents and pulling out the fields you need, a chat that answers from your own knowledge base, or sorting incoming requests. I keep the scope tight.
Under the hood
Stack
- Laravel, Node.js or your existing backend
- Provider-agnostic model routing with fallbacks
- Structured output with schema validation
- Queues for slow calls, rate limits per user
- Evaluation cases run in CI
Decisions
- The model sits behind a small interface, so it is designed to make swapping providers a small change.
- Output is validated against a schema before it reaches your users or database.
- Retrieval respects the same permissions as the rest of your app.
- Every call logs cost and outcome, so spend is visible from day one.
Related case files
- Lyra: an AI agent hub that ships pull requestsA central hub that runs AI coding agents in isolated workspaces, checks their work, and delivers changes as pull requests for people to merge.
- Loom: coding agents on a Mac, routed and supervisedA local macOS app that coordinates coding agents in separate Git worktrees, picks a model for each role, and can be controlled from a phone.
- Office Suite: five connected tools, one identityAn internal office suite for a software company. HR, projects, docs, a virtual office and a Mac meetings app, all sharing one sign-in.