What I do, how engagements run, what your first weeks look like, and how your data is handled — in one place you can click through, and save as a PDF whenever you want one.
Sage Ideas is a one-operator AI engineering & quality studio. I build the AI features you want — and the automated proof they work. Everything here is real and clickable; where a number is estimated rather than measured, it says so.
A production SaaS platform — cockpit, admin ops, Stripe billing, real market-data engines, and a public track-record page that renders real output.
Open the live track record → livelearning platformA live learning product — lesson player, AI tutor on a real model, interactive labs, Stripe billing, and verifiable certificates.
Open the academy →Teams shipping an AI feature to real users who need it reliable — and provably so — before it embarrasses them in front of a customer.
A weekend prototype with no users, or a pure research problem with no path to production. Come back when it has to be trustworthy.
Three service lines. Tap what's true for you and I'll highlight where I'd start — then open a track to see what's included, what you get, and the tools behind it.
Most AI features ship without a way to say whether they're getting better or worse. I build the measurement first — a suite of representative cases with known-good answers — then wire scoring (LLM-as-judge plus deterministic checks) so "is it working?" becomes a number you can watch on every change. The same suite becomes a CI gate: if a prompt tweak or model upgrade drops citation coverage, the merge goes red before it reaches a customer.
Your metrics, thresholds, and pass/fail lines are defined with you — this is a shape, not a claim.
A flaky suite is worse than no suite — when red is noise, the team learns to ignore it, and the real regression sails through with the false alarms. I diagnose the flake sources (shared state, timing, ordering), install a flake protocol (quarantine lane, retry policy, isolation fixes), and get the suite fast and trustworthy again — rebuild is the last resort, not the first. Then it gates your merges so red means red.
The work that quietly eats a week is usually a chain of manual steps between systems that don't talk to each other. I map the workflow, automate the boring middle with RAG, agents, or pipelines (n8n/Make or code), wire it into the tools you already use, and — because it's AI touching real work — put monitoring and evals around it so you find out it drifted before your customer does.
Every engagement is scoped and quoted in writing after a short call — fixed scope, never open-ended hours. Fixed prices for the common paths, and a custom quote for larger builds. Tap a phase on the rail (or below) to see what happens, what you get, and the proof it produces.
A fixed-scope look at the AI feature you're unsure about: where it breaks, what "working" should actually mean, and what it would take to prove it. You leave with a plan whether or not we continue.
A tightly-scoped build or eval-harness sprint — enough to move one real thing from "we think it works" to "here's the evidence."
The full feature plus its proof: the AI, the eval/test harness, the CI gates, and documentation your team can operate without me.
If you'd rather it be run than handed off — monitoring, regression coverage, and iteration on a simple retainer.
Milestone 2 · Build — eval harness + CI gate DELIVERED
Deliverables: a 120-case eval suite in your repo · faithfulness + citation gate wired to CI · a scored dashboard · a one-page runbook. You review it in the portal and click Approve — nothing advances without your sign-off.
Signed on? Here's exactly what the first two weeks look like, a checklist to get us moving, and how your private client portal works.
We align on scope, success criteria, and access. You get your portal link.
I get into the code, map the real problem, and share early signal so there are no surprises.
A working slice plus the evidence it behaves — reviewed together in the portal.
Milestones land in the portal; you approve, we keep moving. Async by default, calls when useful.
A live timeline of what's being built, with status on each step — no "how's it going?" emails needed.
When a milestone is delivered, you approve it right in the portal. Nothing moves without your sign-off.
A direct thread with me about your project — I'm notified by email the moment you send.
Deposit and balance handled securely via Stripe, with a printable receipt and full payment history.
What we're building, and the specific, measurable definition of "working" we'll hold it to.
The repo, environments, and accounts I'll need — least-privilege, your providers, revoked at handoff.
How we'll communicate, how often, and who the decision-maker is on your side.
The one thing we'll deliver and prove first — so you see real signal fast.
A plain-English brief on how your code, credentials, and data are handled. If you need something more formal, an NDA and the data terms in the agreement cover it — tap a topic to expand.
How a build engagement is scoped: objective, phased plan, deliverables, acceptance criteria.
The master services agreement that governs the working relationship and IP.
The Operate phase: what's covered, response targets, monitoring, and reporting.
Confidentiality both ways, so we can talk specifics before we start.
Illustrative samples, not legal advice — real terms are written per engagement and reviewed by your counsel.
Full security & data-handling page → — the version to hand your security reviewer.
This is a plain-English summary, not the contract. The signed agreement and any NDA are the binding terms.