Development
Delivery Models
For enterprise product leads and founders who need a product shipped, not just engineering capacity. Our two-person senior team combines product strategy, business analysis, design, full-stack development, QA, and DevOps, supported by specialized AI agents across the delivery lifecycle.
“Their team handled the challenge professionally and found appropriate workarounds.”
“The group goes above and beyond to accommodate our demands.”
“The overall service was exceptional.”
“Their communication, work ethic, and desire to give a positive outcome for their client are impressive.”
“They deliver what they promise, and I can’t say that about other companies I’ve worked with.”
BAs traditionally scan briefs, transcripts, and scattered docs by hand. The ba-workflow plugin ingests every input in parallel and produces a structured summary with citations back to the source.
~80 working hours becomes ~8.
Output:
5 commands, ~15 parallel agents, human review at every stage.
Designers traditionally cycle through manual iterations of the design system, screens, and accessibility audit. The design-implementer plugin runs a 7-phase generative UI pipeline straight in the design tool.
~48 working hours becomes ~5.
Output:
7 pipeline phases, 100+ MCP API calls.
Devs traditionally lose days on project setup, codebase audit, architecture choices, and security review before writing a single feature. The ticket-implementer plugin scans the codebase first, plans the work in execution waves, and ships a full-stack feature from ticket to merged PR.
~24 working hours becomes ~3.
4 commands, 4 validation gates, wave-based parallel execution.
QA traditionally writes tests against the spec rather than the code. The qa-web-helper plugin cross-references ACs with the live codebase and prescribes how to close each gap.
~16 working hours becomes ~1.5.
Output:
4 phases, parallel browser agents, zero manual clicks.
Founders With a Clear Vision and a Real Budget
You know the problem, the user, and what v1 needs to do. We move you from vision to production in a quarter.
Mid-Sized Businesses Building an MVP or Internal Tool
Fixed scope, fixed timeline, fixed price — without the overhead of a full team.
Teams That Want a Production-Ready v1, Not a Sandbox Prototype
Code, tests, infrastructure, docs, and design assets — all delivered clean so any team can pick it up and keep going
Schedule a consultation to see whether Product Development 2.0 fits your product and your timeline.
Two Experts, Full Product Coverage
Two senior specialists cover six to eight traditional roles — PM, BA, design, dev, QA, DevOps. No coordination layer, no handover tax.
AI Across the Entire Workflow
AI runs inside every phase — discovery, design, code, QA, deployment. That is what makes the x5–x10 compression real, not theoretical.
A Codebase Built for Whatever Comes Next
Code, docs, architecture, and design files delivered as a clean package. Nothing locked inside one person’s head.
Fixed-Price Commitment on the Agreed v1
Scope locked in week one, fixed price from there. Budget certainty with a leaner, faster team.
Your Vision Isn’t Validated Yet
You have a direction, but not a clear picture of the v1. A discovery or prototyping sprint comes first — Product Development 2.0 is the next step once the vision is set.
The Scope Can’t Be Defined Upfront
If requirements will only emerge mid-engagement, the model breaks. Product Development 2.0 needs a signed-off scope before phase 1 — that’s what lets the two-person pod move 2–3x faster.
You Need Augmentation, Not Delivery
If you want engineers plugged into your existing team and processes, this isn’t the right shape. Product Development 2.0 is a full-delivery pod — for augmentation, One Man Army or Team Extension fits better.
A two-seat team only works when both seats are senior, both are deeply AI-fluent, and the engagement is structured to make the timeline compression real.
We’ve delivered over 230 projects. These stories show how our custom software development company solves immediate problems while building a technical foundation that scales for years.
The engagement is built to lock direction fast and ship within a quarter. Here is how a typical AI-powered product development project moves from first call to production v1.
We start with a short conversation to understand the product, the users, the constraints, and what v1 needs to do. We do not require a detailed specification, but we do need a clear vision of the product you want to ship.
In the first weeks, the team works with you to lock the v1 scope, produce the prototype, and align on architecture. Once the scope is set, the engagement runs on a fixed price and a fixed timeline.
AI-assisted prototyping, architecture, and scaffolding move the product to roughly 50% maturity in the first weeks. This is where most of your involvement sits.
The main scope is implemented. Development, QA, and DevOps run in the same seat, supported by AI across the loop. Your involvement drops to milestone reviews.
Edge cases, performance, security, and production readiness. The product ships.
At the end of the engagement, you decide what happens next: extend with the same team, hand the codebase to an internal team you build, transition to a Dedicated Team for ongoing scale-up, or move to another provider. The codebase is documented from day one, so none of these paths requires a rebuild.
Both deliver a production-ready v1 with full responsibility on our side. Our product development service uses a two-seat AI-powered team instead of a full delivery group, which compresses the timeline from four to six months down to two to three, and reshapes the budget around two senior specialists instead of a full team. It is the right fit when you have a clear vision, a defined v1 in mind, and want to ship fast. Classic Product Development is the right fit when the scope is large, the team needs to work in parallel breadth, or the engagement runs longer than a single v1.
First senior specialist who can take a product vision and turn it into a working prototype, a defined scope, and a clear delivery, covering project management, business analysis, design, and prototyping. The second seat covers full-stack development, QA, and DevOps, a senior engineer who ships the product, tests it, and runs it in production. Both are AI-fluent across the full workflow.
It means the same v1 that takes four to six months with a classic team ships in two to three months with this model. The compression comes from three places: AI integrated across every phase of the workflow, two senior specialists each absorbing three to four traditional roles, and a front-loaded scope that removes the long discovery and coordination tail. The boost is biggest at the start (around x10 for prototyping and foundation), drops to roughly x4 during the core build, and settles to around x2 during polish and hardening.
Yes, for the agreed v1 scope. The first weeks of the engagement are spent locking the scope, producing the prototype, and aligning on architecture. Once that is set, the engagement runs on a fixed price and a fixed timeline. If you decide to expand the scope after v1, that is a separate conversation.
Custom product development services assume you can describe the v1 in concrete terms, what it does, for whom, and what success looks like. You do not need a detailed specification or finished designs; the team produces those in the first weeks. But if the core product question is still open, a discovery engagement is the right starting point.
A production-ready v1, a working prototype, design assets, working code, technical documentation, and a reproducible infrastructure setup. Everything is structured so your internal team, our Dedicated Team setup, or another provider can pick it up without a rebuild.
Most v1 engagements run between two and three months from kickoff to production. The exact timeline depends on the complexity of the scope locked in the first weeks. Clients commonly extend the engagement after v1 to add the next round of features, hand the codebase to an internal team they hire, or transition into a Dedicated Team setup with us.
Typically, within two weeks of confirmation. Because there are only two seats to onboard and a predefined engagement structure, the time from decision to first day of work is significantly shorter than assembling a full team.
You decide. Common paths include extending the engagement to build the next round of features, handing the codebase to an internal team, transitioning into our Dedicated Team model for ongoing scale-up, or moving to a different provider. Because the codebase is documented from day one, none of these paths require rebuilding the foundation.
