Dacard.ai

I design how work gets done when agents do the production.

DACARD.AI

The AI operating model · Darren Card

02 / The problem
◆ WHAT KEEPS HAPPENING

The pilot works. Then nothing changes.

Someone demos an agent. It works. Six weeks later the team is still running the same weekly cycle, the same status meeting, the same review queue, and the agent is a browser tab two people use.

The tools landed. The way the work runs didn't move. That's the gap I close.

03 / What I do
◆ THE OFFER

I design how work gets done when agents do the production.

Then I build the measurement that tells you whether it worked: evals on what the agents produce, unit economics on what it costs to produce. Twenty years of B2B SaaS product and technology leadership, pointed at one problem.

AI operating model Evals and unit economics Fixed scope
04 / Where it applies
◆ TWO DOMAINS, ONE METHOD

Software teams, and companies that don't build software.

The operating model is the same problem in both. One builds a product with agents. The other runs a business with them. The instruments don't change.

Software product leadership
A product and engineering org where agents now write most of the code. The work is deciding what to build, keeping quality legible as models change, and knowing what the output costs. Buyer: CTO, CPO, or VP Product.
Internal company operations
A firm that sells expertise, not software. Estimating, reporting, field data, proposals. The work is turning senior judgment into agents that draft, and leaving the sign-off with a person. Buyer: COO, managing partner, or owner.

Not a fit either way: teams where headcount reduction is the goal, or where the ask is a strategy document with no instrumentation attached. This work removes drudgery and installs measurement.

05 / What the work looks like
◆ FOUR PATTERNS I KEEP MEETING

Concrete, because the category talk isn't useful.

Two from each domain. These are shapes, not client stories: the specifics differ every time, the shapes rarely do.

The expert stuck in a spreadsheet
One person estimates better than everyone else, and it lives in their head and a workbook. We turn their judgment into an agent that drafts the estimate, and leave the sign-off with a human. Turnaround drops, and the expert moves from writing every estimate to reviewing every estimate.
Nobody owns the AI bill
Spend climbs every month and no one can say what it bought. We put a cost per unit of work on each agent task and set a budget at the task level. It usually turns out two workflows are most of the bill, and one of them isn't worth running.
The model changed and nobody noticed
A provider ships an upgrade, output quality moves, and the first signal is a customer complaint. We build a graded set that runs on every prompt and model change, so a regression shows up as a number the same week instead of a story next quarter.
Everyone's shipping, nobody's deciding
Output tripled and the roadmap didn't get better. We rebuild the weekly cycle around the decisions that actually need a person, and hand the drafting, the synthesis, and the status reporting to agents on a schedule.
06 / How you'll know it worked
◆ THE MEASUREMENT LAYER

Two instruments almost nobody installs.

This is what separates an operating model from a reorg, and it's a fair thing to ask every other candidate about.

01
Evals on the output.
A graded set that runs on every prompt change and every model change. Quality becomes a number that moves instead of a feeling that drifts. Without it, an upgrade and a regression look identical from the outside.
02
Unit economics on the production.
Cost per unit of work the agents produce, tracked against the margin it protects. Open-ended generation stays open-ended cost until somebody puts a budget on the task.
03
Decision quality, not velocity.
Time-to-decision, and the gap between customer signal and customer outcome. Story points measured how fast the old model ran. They don't tell you whether the new one is pointed anywhere.
07 / Engagements
◆ HOW TO START

Start with three days. Decide from there.

Fixed scope carries its number here. Everything else gets priced on the call, once the scope is real. No qualifying gates, no pitch deck round.

08 / Proof
◆ I HAVE DONE THIS

A one-year engagement, zero to launched.

Lexful.ai · Founding CPTO, one-year 0-to-1 contract · SOC 2 at launch · $3M pre-seed · Launched Feb 2026
What the engagement covered
Product and technical architecture built AI-native rather than AI-added, with conversational search and retrieval at the core. Hired the product and engineering team from zero. Stood up the AI-assisted workflow across coding, QA, and support. SOC 2 compliance achieved at launch, not after it.
What it shipped into
A market with five incumbent platforms and a $100M+ ARR leader. Launched with paying customers on day one. I supported the $3M pre-seed alongside the build, which is the version of this work where the operating model and the fundraise have to hold each other up.

Twenty years before that: VP Product and Technology at Cognota (category creation, SMB-to-enterprise pivot, co-led a $5.5M Series A). Director of Product at Elastic Path and Allocadia, scaling ARR from $5M to $20M. Operating partner at Top Down Ventures. Eight verticals. Internal-operations references available on request.

09 / Get in touch
◆ AVAILABLE

Start with the three-day diagnostic.

Or bring the harder question directly. Thirty minutes, rate on the table, no deck round. If you are hiring for a full-time seat rather than buying a project, say so and we will talk about that instead.

Book 30 min → darren@darrencard.com Read the whitepaper

calendly.com/darren-darrencard/30min · dacard.ai/consulting