Getting AI product decisions wrong early is expensive. Getting them right early is everything.
We work as the embedded AI team for early-stage software companies — product architecture, engineering velocity, and the go-to-market operations a five-person company can't staff.
AI product development isn't regular software development. Strategy, infrastructure, prompt design, evaluation, and data architecture all have to work together, and most teams are solving that combination for the first time. Decisions made in month two — how you structure your data, how you evaluate output quality, where the model sits in the product — are expensive to reverse in month twelve.
The second problem is arithmetic. A small team needs a marketing function, a data function, and a sales operations function, and can afford none of them. So founders do all three badly, at night.
And the third is speed. Your advantage over an incumbent is that you can ship this week. That advantage disappears the moment your team is buried in operational work.
Three places it pays off first.
Product architecture
The decisions that are cheap now and expensive later.
Engineering velocity
Shipping at a pace that justifies being small.
The functions you can't hire
Marketing, sales ops, support, and reporting, run by workflows instead of headcount.
The workflows we build.
Not a menu you buy off. It's the work we've actually built in this industry — start with the one costing you the most hours.
AI product foundations
- Specification documents for core product logic — onboarding, scoring, matching, generation
- Prompt architecture and versioning
- Evaluation harnesses, so quality is measured instead of vibed
- Data model and retrieval design
- Natural language search and query parsing
- Scoring and matching algorithm design
Engineering velocity
- AI-assisted development on the backend
- Code review and test generation
- Migration and refactoring work
- Documentation generated from the codebase
Go-to-market
- Ideal customer profile scoring and lead sourcing, written into your CRM with attribution so you can see what converts
- Outbound sequencing and personalization at volume
- Competitive and pricing research
- Sales collateral and demo materials
Product operations
- User feedback synthesis across channels into themes
- Roadmap triage and prioritization inputs
- Changelog, release notes, and documentation
- Support deflection — docs and self-serve answers built from real tickets
Company operations
- The internal stack set up once, properly — email, docs, CRM, reporting — instead of rebuilt every quarter
- Recurring reporting and metrics
- Hiring and onboarding materials
Fundraising
- Investor update drafting from your actual metrics
- Data room preparation and organization
- Metrics reporting and cohort analysis
The product decision and the customer conversation. We'll also tell you when a channel isn't working rather than optimizing something that shouldn't exist — we've told clients to stop chasing a segment, and told others that their existing list was a better asset than any new outbound motion.
What working together looks like.
We embed. That means sprint reviews, not status reports — and it means we'll do the unglamorous work: setting up the stack, running the campaign, building the trade show materials. For early-stage companies we've structured performance deals, commission arrangements, and equity partnerships where a standard retainer didn't fit.
Research
We map how work actually moves through your business — not the org chart, the real path — and quantify where the hours go.
Build
We build inside the tools you already own. No new platform, no custom system that breaks when the technology changes, no vendor lock-in. Everything we build belongs to you.
Train
We teach your team to run and extend what we built, so the next workflow doesn't require us.
What this industry always asks.
"We can't afford an AI team."
Most of our startup clients aren't on a standard retainer. And a well-chosen internal stack for a small team runs closer to $100/month than $10,000 — we'll tell you what to buy before we sell you anything.
"We already use AI to code."
Most teams do. The gap is usually architecture and evaluation, not autocomplete: how the product's AI actually works, and how you know when it's getting worse.
Tell us what's blocking the roadmap.
Thirty minutes, no pitch. If we can't help, we'll say so.
Let's build your
AI advantage.
Book a free 30-minute strategy session. We'll look at your workflows, find the biggest opportunities, and show you exactly what AI can do for your team.
Book Your Free Strategy Session