Our delivery methodology, our trained operators, and industry-leading AI coding agents as the execution engine. We embed with your team, run the spec-to-merge pipeline end-to-end, and ship working software at a fraction of the cost of a traditional engineering team — without giving up the discipline that makes software actually work in production.
A capable AI coding agent paired with a disciplined methodology — and an operator who knows how to drive both — ships features at a rate that makes traditional team sizing look obsolete. The leverage isn't in the model. It's in the harness around the model: the spec format, the review gates, the stack profiles, the conventions encoded in our project context file, the slash commands that make repeated work disappear.
We've built that harness. We bring it to you, install it on your repo (greenfield or brownfield), and run the delivery process for you — or alongside your existing team — until you're shipping at AI-native speed and AI-native cost.
AI-Native Development is for businesses that have looked at the new economics and decided they don't want to keep paying 2018-era engineering rates for 2026-era work. Three shapes of engagement we see most often:
You have an engineering line item that's grown into the seven figures and isn't shipping at the rate the cost implies. We come in with the kit and operators, take over delivery for one or more product lines, and your engineering spend drops to a fraction of what it was — without features stalling or quality slipping.
You have a vision for a platform but don't want to spend a year and a small fortune assembling a team to build it. We scaffold with our methodology kit on day one, run the spec-driven pipeline, and ship a real product in weeks instead of quarters. You stay focused on the business; we own delivery.
You have a working codebase that's slow to extend, hard to onboard into, and expensive to maintain. We read the code, draft a project context file reflecting your existing conventions, and start shipping features against your codebase using the same engine — without a rewrite, without a migration project, without breaking what works.
The methodology is the IP. The operators are how it gets executed. AI coding agents are the engine. Each piece is necessary; none of them is sufficient on its own — which is why "we bought a coding agent" doesn't get most teams where they want to go.
A complete delivery system installed on your repo: a base project context file with stack-specific overlays, our four spec templates (generic, API, web, mobile), four core slash commands (/draft-spec, /spec-to-feature, /add-tests, /review-pr), an opinionated agent settings profile with safe-by-default permissions, and a CI workflow that gates merges on tests and spec linkage. Updated continuously as we learn what works on real engagements.
We maintain a library of stack profiles — Next.js, FastAPI, Django, Rails, Expo, T3, Refine, and growing — each a tested combination of conventions, settings, CI workflow, and spec template variant. Pick a profile from the library, compose two for fullstack work, or tell us your stack and we'll author a new profile. Profile-driven setup means a new engagement is configured in hours, not weeks.
AI coding agents in unskilled hands produce impressive demos and disappointing production code. The leverage is in operators who've shipped real features through this pipeline before — who know when to split work into subagents, when to commit, when to push back on a spec, when to run an eval, when an AI suggestion is wrong. Every engagement gets a named operator and a backup, full-time on your work.
No code without a spec. No merge without a review. The lifecycle: requirement → drafted spec → human approval → the agent implements + tests + opens PR → CI gates → human review → merge → preview deploy. Every gate is deliberate. The discipline is what separates AI-shipped software from AI-cowboy software.
MCP connections to your Linear (or Jira / Asana / GitHub Issues), your GitHub repo, your Slack, your Postgres, your internal APIs — wired so the agent reads tickets directly, opens PRs, posts status, queries data. We've already done this integration on prior engagements; on yours it's configuration, not engineering.
"AI shipped it" is not an excuse for cutting corners. Every feature ships with tests (unit, integration, end-to-end where appropriate). AI-touching code ships with evals — a regression suite that proves the agent is still doing the right thing at 3 AM. Observability and cost monitoring are wired in from day one.
A service that walks into many different clients with many different stacks needs an architecture, not a checklist. Ours separates what we standardize (the methodology) from what stays flexible (your stack, your tooling, your conventions).
Lives in our private repo. Versioned and tagged. Every engagement is delivered with a specific kit version, so you always know which release of our system shipped your code. The project context file base, spec templates, slash commands, settings profile, and CI workflows that are universal across clients.
Picked or composed from our profile library and applied on top of the base. Stack-specific conventions, permitted commands, CI configuration, and spec template variants. This is where Next.js engagements get Next.js patterns and FastAPI engagements get FastAPI patterns — without re-deriving them every time.
Lives in your repo. Your specs, your PRs, your code, your data. Yours, fully — we license you the kit during the engagement; everything we produce on top of your codebase is yours, full stop. No lock-in, no proprietary platform you have to keep paying for to read your own code.
A typical feature flowing through the pipeline. Every step has an owner and a gate. The gates are what make this work — not the model, not the prompt, not the framework.
The operator runs /draft-spec against the ticket. Output: a structured spec file (user stories, acceptance criteria, data model, API contracts, edge cases, test plan) committed to /specs/<feature>.md. The spec is the contract.
The most consequential gate in the pipeline. You read the spec, edit it, push back on it, approve it. Most agent-built systems collapse here — they generate code from fuzzy intent and produce fuzzy output. We don't.
/spec-to-feature reads the spec + your project context file, scaffolds files, implements the feature, writes tests, runs them locally, fixes anything that breaks. The operator monitors, intervenes when needed, splits into subagents for independent modules.
/add-tests ensures coverage of the acceptance criteria. For AI-touching code, the eval suite runs against the new behavior. CI must be green before a PR is opened. No exceptions.
PR description references the spec, summarizes changes, lists what was tested. Preview deploy fires automatically. The operator runs /review-pr for an automated first-pass review, then your engineer (or ours, if you've outsourced review too) does the human review.
This is where "AI shipped it" stops being scary. The diff is small enough to review (because the spec was scoped well). The tests prove behavior. The preview deploy proves it works. Merge, deploy, done. Most features go ticket-to-merged in 1–3 days.
Whether you're starting from a blank repo or walking us into a 200,000-line codebase you inherited, the delivery process is the same after week one. The first week is what differs.
You have a vision. We have the kit. The first PR ships within days, not months.
We read first. We talk to your engineers. We pick a low-blast-radius pilot. The first PR is our marketing — and it lands.
Concrete shifts our clients see in the first 60–90 days. Specifics depend on the engagement, but the shape is consistent.
Plenty of vendors are pitching AI-driven engineering right now. The ones that miss tend to miss in the same places. Here's what we got right.
The single biggest failure mode in AI-built software is fuzzy specs producing fuzzy code. Our pipeline refuses to start coding without an approved spec. The spec format is opinionated — testable acceptance criteria, edge cases pre-thought, data model spelled out — because that's what makes the implementation crisp.
Spec approved by a human. PR reviewed by a human. Merge approved by a human. Each gate is small and fast because the work between gates is small and fast. Gates aren't bureaucracy when they're proportional to the change.
Generation-from-zero is where AI coding agents waste the most tokens and produce the most weirdness. We lean on battle-tested starters per stack and let the agent customize from there. Less novelty, more leverage.
The kit makes the process repeatable. The operator makes the process succeed. We don't hand you a kit and tell you to run it yourself — that's a different SKU we don't sell. We bring the operator and the kit together, because together is what works.
The agent reads your code the way a senior engineer would — grep, glob, follow imports, read the actual file. Not a vector index that goes stale every commit. The result: fewer hallucinations, fewer "the AI made up an API that doesn't exist," fewer surprises.
Every engagement makes the kit better. New stack profiles, sharper slash commands, edge cases discovered on real production work. You're not getting v1 of an internal tool — you're getting whatever version we're shipping today, plus everything we learned from the last engagement.
You don't take our word for it — you read the spec before code is written, review the PR before it's merged, watch tests and evals run in CI, and see preview deploys before features go to production. The discipline is mechanical, not vibes-based. The single best sales pitch we have is showing this on a real feature in a discovery call.
Yes. We have profiles for the most common stacks (Next.js, FastAPI, Django, Rails, Expo, T3, Refine, and growing). For anything we don't already have a profile for, we author one as part of onboarding — usually a day or two of work, then it's reusable for your future engagements and ours.
You own everything in your repo — code, specs, tests, configurations, documentation. Full stop. We retain ownership of the methodology kit (the templates, slash commands, stack profiles, playbooks); during the engagement you have a license to use it, and at the end you can keep using the version installed on your repo. No lock-in, no platform fees to read your own code, no surprise.
Our agent runtime runs in environments we configure with sandboxing, managed settings, and explicit permission rules. For regulated environments (healthcare, finance, defense), we offer on-prem proxies and audit-trail configurations — sales cycle is longer for those engagements, so flag it early. SOC 2, HIPAA, and financial-services postures are supported on an engagement-specific basis.
No, and that's deliberate. The leverage is in the methodology, the operators, and the discipline — not the framework. Building a custom agent framework from scratch is a multi-year R&D investment that doesn't make the output any smarter; the underlying frontier models are the same regardless of which harness wraps them. We compose battle-tested industry tooling with our methodology layer on top, which is why we ship on day one instead of spending a year building infrastructure.
Outcome-based, not seat-based. Engagements are scoped against a defined set of features or a defined throughput — "ship the platform described in this PRD by date X" or "deliver N features per month at quality bar Y." Pricing reflects the outcome, and lands as a fraction of equivalent engineering headcount cost. Month-to-month with no long-term lock-in; we'd rather earn the next month than trap you.
Most engagements have your engineers in the loop on review — that's good for everyone. Some engagements have your engineers in the loop on implementation too, pairing with our operators. Some have you fully outsourcing delivery. All three models work. Which one fits depends on what you have in-house and what you're trying to learn from the engagement.
AI Workflows is about building AI features inside your product. AI-Native Development is about using AI to build software for you. Different audience, different outcome. Some clients buy both — we use AI-Native Development to ship the platform, and AI Workflows to put AI features inside the platform we're shipping.
A 30-minute discovery call gets you a clear scope, a stack-profile recommendation, and a candid read on whether AI-Native Development is the right fit for your situation. Greenfield or brownfield, replacement or augmentation — same conversation.