Method,
encoded

Consistent analysis in hours, not weeks.

Your proprietary method, in code you own. For research, insights, and strategy firms.

Issues

Give Claude or ChatGPT a stack of documents, interviews, or open-ended responses and it gets you about halfway. Where it stops depends on your firm. It’s usually one or more of the following.

Assumed experience: Your method assumes a trained person at every step. A tool can read the document, but lacks critical context to make an informed decision.
Inconsistent calls: Two senior people run the same method and land in different places, which happens wherever the method leaves room for judgment.
Handed off manually: The steps live in different tools, in different shapes. Someone reshapes the output at every handoff, and the third report comes out shaped differently from the first.
One study at a time: One study is a conversation. Forty is a process, and a chat window has none: no schema, no checkpoints, no way to pick up where it stopped. Cobbled-together automation isn't much better.
Nothing checks it: It's right often enough that a person has to read all of it to catch the times it isn't.
It lives in one person's chat: Your best analyst's prompt is real and it works well enough... for them. It isn't integrated into your systems, and it doesn't run without them.

Case Study

I built a solution for a research firm in that exact situation. Their method was written for their analysts, who were running into limitations in their sessions with ChatGPT. I turned the method, all of it, into a pipeline: every step from raw transcript to finished report, the reshaping between steps done by the pipeline instead of a person, in the structure they’d always used. Made quantitative where it could be measured and kept qualitative where their judgment belonged, with their team reviewing at fixed points along the way, checked against their own standard for what “right” looks like.

What it was

Who: A qualitative research firm, interview-based
Before: Weeks per study, by hand
After: Hours per study, automated
Scale: Hundreds of transcripts at a time
The people: Senior analysts review at checkpoints instead of reading every line.

How it was built

Steps: Thirteen steps, each one a checkpoint. An interrupted run resumes where it stopped.
Schema: The firm's own vocabulary as typed fields, so the format can't drift.
Clustering: Themes grouped by meaning, with the number of groups chosen per study rather than typed in.
Rubric: One written definition of success, versioned six times on their own examples, with checks in code and a judge model separate from the generator.
Test set: The client's own edits, about 130 themes across nine studies.
Delivery: Into their spreadsheet, on their cloud, with the steps their team still does by hand mapped and built around.

Couldn't AI build this?

AI can write great code. I use it in my workflow. What it can’t do is learn the method from your team, judge whether a result is right, or stand behind it.

Distilling your team's method: Sitting with your senior people until every call they make is written as a step, including the ones nobody thought to mention. A tool can't interview your team. On the research firm project it mattered as much as the code did, and it took a person in the room.
Deciding what right looks like: A tool hands you something that runs. Whether it matches what your best person would have produced is a different question, and answering it takes someone who knows your method and how these systems fail. On the research firm project that meant a separate judge model, calibrated against the client's own examples.
Someone who answers for it: Models change. Your inputs change shape. A prompt that worked in March drifts by June. Owned, tuned, and documented by a person you can call, not a session you closed.

What you get

Your method, encoded: Every step of your analysis, from raw input to findings to the report, including the calls your people make without thinking, built as a pipeline a person can inspect. Not a generic tagger. The logic is yours.
Your deliverable, in its intended shape: Output lands in the format your clients already get, the spreadsheet, the deck, the report, with nobody reformatting between steps. Nobody on your team learns a new tool.
A checkpoint at every step: An analyst can open any stage, see what the system decided and why, and correct it. Review points sit where your team wants them. Senior judgment is still the last word. It just isn't the first pass.
Your data stays yours: It runs on your accounts. Your material never sits in someone else's product, which matters when the client has a data clause.
You own the code: No seats, no vendor holding your method. If we stop working together you keep everything, documented.
Measured before it ships: We agree what a correct result looks like on your real material before anything gets built, and the build is scored against it. On the research firm project, the client's own edits became the test set.

How it starts

01

A conversation

About where it stops working for you.
02

A paid week, $5,000

If there's something there, I sit with the people who make the calls, map one deliverable end to end, and hand you a written spec and a fixed price for the build. If you don't proceed, you keep the spec.
03

The build

Fixed-price, set from the week's spec. I can't price it before I've seen your method, and anyone who claims they can is either guessing or selling a template. What moves the number is how many steps your method has and how many handoffs it crosses.
04

Kept tuned

Models change and your inputs change shape. A monthly retainer keeps it running and documented, and scopes the next workflow when you're ready.

Get in touch

Say where it stops working for you. That's the whole first conversation.