AI PRODUCT ENGINEERING

AI product development services, Sprint 0 to shipped product.

Ship the AI product your roadmap commits to. Sprint 0 lands a scope you can defend in two to four weeks. The build runs with a lean team in weekly sprints, billed against acceptance criteria rather than clocked time.

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  • Groundlight
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  • Notary
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WHAT YOU WALK OUT WITH

An AI product development company that ships product, not staff hours.

You walk out with a production-grade product against a scope you can defend. Sprint 0 turns your thesis into engineering scope you can build against. The build runs that scope to shipped software, with billing tied to acceptance instead of clocked time.

  • A scope you can defend, before code starts

    Two to four calendar weeks of senior engineering and product time, working alongside your team. You walk out with a closed scope and acceptance criteria, sized user stories, repository scaffolding and infrastructure-as-code groundwork, a foundational UI and design system, and a milestone plan with the first two milestones defined in depth. If you take the deliverable elsewhere, it still holds value: another vendor or your in-house team can execute against it directly.

  • A lean team scoped to the work

    Two to four people on the engagement: full-time engineers paired with the fractional roles your build actually needs (delivery manager, product manager, QA, designer). Lean by design, set in Sprint 0 against the work rather than against a target head count. Adding people above what the scope warrants slows shipping and adds coordination cost on your side.

  • A build you can model runway against

    Weekly sprints with end-of-sprint demos. Milestones map to the epics from Sprint 0, with at least two milestones a month. Billing fires on milestone acceptance, not on clocked time. Scope changes become new milestones with their own acceptance criteria and billing, not absorbed into existing ones. The schedule and the billing are the same artifact, so you can model runway against either.

HOW IT WORKS

From discovery call to shipped product, in four steps.

The engagement starts with a 30-minute discovery call. A senior engineer walks through what you are building, where you are in the build, and what the next two quarters need to look like. The call ends with a Sprint 0 proposal when the fit holds.

When Sprint 0 lands at the end of two to four weeks, both sides sit down for a go or no-go decision. Either side can decline the full build, and the deliverable is intact on either side. When the answer is go, the build engagement runs against the scope and milestones set in Sprint 0. Weekly sprints, weekly demos, billed against acceptance criteria.

WHAT KEEPS THE BUILD ECONOMICS HONEST

Four mechanics that hold the build from Sprint 0 to launch.

Boutique product builds tend to fail at four predictable points: discovery produces a deck instead of engineering scope, the team scales up under scope-creep until economics break, billing decouples from delivery and your runway erodes invisibly, or the AI angle is bolted on after the codebase is already shaped wrong. The four mechanics below are what hold each of those failure points in check.

  • Sprint 0 produces engineering scope, not a deck

    Two to four weeks of working engineering and product time produces six concrete deliverables: a closed scope with acceptance criteria, sized user stories, repository scaffolding and infrastructure-as-code, the foundational design system, a milestone plan with the first two milestones defined in depth, and an engagement-economics summary. If you walk at Sprint 0 close, those six artifacts go with you and another team can build against them.

  • Lean team scoped to the work, never inflated to absorb scope creep

    Two to four people on the engagement: full-time engineers paired with the fractional roles your build actually needs (delivery manager, product manager, QA, designer). Composition is set in Sprint 0 against the work, not against a target head count. When scope genuinely grows, the answer is a new milestone with its own acceptance criteria and its own billing, not more people added to an existing milestone you already priced.

  • Milestone billing tied to acceptance, not to clocked time

    Every milestone has acceptance criteria that were written in Sprint 0 and a billing amount tied to that milestone. Billing fires on your sign-off, not on hours logged. You can model runway against the milestone schedule because the schedule and the billing are the same thing. Scope changes are negotiated as new milestones, not absorbed into existing milestones at the same price.

  • AI fluency in every engineer, by default

    Every engineer placed on the build is fluent in modern AI tooling, LLM APIs, and the agent patterns shipping in production today. AI fluency is the baseline, not an upcharge and not a separate team. Whether your product needs AI on screen or just an engineering team that uses AI tooling in the build loop, you get the same bar.

PRICING

AI MVP development to mature product builds, priced against scope.

Pricing runs in two steps. Sprint 0 is a fixed fee set on the discovery call. The build is sized when Sprint 0 closes, against the scope and team composition you and Leanware land on, not by the hour. Each proposal is its own configuration of the variables below.

SPRINT 0

Scoping with engineering, two to four weeks

Fixed fee

Set on the discovery call

Senior engineer plus senior delivery manager, with a designer engaged fractionally. The fee scales with complexity (number of stakeholders, scope of the discovery, integration points to inventory, depth of the design-system work). The deliverable retains value whether you continue with the build or take it elsewhere.

  • Closed scope with acceptance criteria for the build
  • Sized user stories ordered into a build roadmap
  • Repository scaffolding and infrastructure-as-code groundwork
  • Foundational UI and the design system the build extends
  • Milestone plan, first two milestones defined in depth
  • Engagement-economics summary: team, cadence, billing schedule, total cost

BUILD ENGAGEMENT

Milestone-billed product build

Sized to your scope

Set when Sprint 0 closes

The full build runs against the scope and milestone plan from Sprint 0. The number reflects the complexity Sprint 0 surfaced: deeper scope, more integration points, or a larger team composition land at the higher end. No setup fee on top of Sprint 0, no hourly markup, no separate "AI specialist" charge.

  • Two to four people: full-time engineers plus fractional roles as scoped
  • Delivery manager, product manager, QA, designer as the work warrants
  • Weekly sprints with end-of-sprint demos
  • At least two milestones per month, mapped to Sprint 0 epics
  • Billing fires on milestone acceptance, not on clocked time
  • Scope changes are new milestones, not absorbed into existing ones

WHAT A BUILD PROPOSAL CARRIES

The variables on every build engagement.

Each proposal commits to a precise configuration along the axes below, set in Sprint 0.

Sprint 0 fee and timeline
Fixed fee, two to four calendar weeks. Set at signing based on complexity.
Build team composition
Two to four people on the engagement: full-time engineers paired with fractional delivery, product, QA, and design as the work warrants.
Milestone count and schedule
At least two milestones per month, mapped to Sprint 0 epics. Total count set against scope.
Per-milestone billing
Each milestone has acceptance criteria from Sprint 0 and a billing amount tied to that milestone.
Total build engagement cost
Sum of milestone billings, set in the Sprint 0 economics summary before the build kicks off.
Sprint cadence and demo schedule
Weekly sprints. End-of-sprint demos with your team. Acceptance sign-off triggers the next milestone billing.

IS THIS YOU

Who this engagement typically works for.

If one of these describes your quarter, the engagement is sized for you. Runway and stage are part of the conversation on the discovery call, not a filter we apply before it.

  • Technical founder shipping the first production version

    You have validated a thesis and you are ready to ship the first production-grade version. You can prototype yourself, but the architecture, the AI orchestration, and the design system need to land right from day one so the next year of building runs against them cleanly. Sprint 0 closes the scope. The build ships the product. You stay close to the technical decisions; the team carries the execution.

  • Non-technical founder with a co-founder gap

    You have a product-market thesis but the CTO search is still running. You want a team that can ship the next major version while the search goes, and hand the codebase to the eventual CTO cleanly. The senior delivery manager in the engagement is your single point of contact, doing the work a technical co-founder would otherwise be doing.

  • Company building an AI product for internal or external use

    A company commissioning a new AI product, either an internal tool that re-shapes a workflow your team runs every day or an external-facing AI feature in your customer offering. Engineering is not your core function, the product is a defined deliverable rather than a permanent practice, and you want a team that ships it and hands it back to you to operate.

  • PM with a finished PRD and a scoped deliverable

    You have a finished PRD and exec air cover to engage an external partner. The acceptance criteria are already written; what you need is a team that converts your PRD into engineering scope and ships against the milestones, without needing PM hand-holding on every decision. Sprint 0 closes around your PRD, not a blank thesis.

HOW THIS IS DIFFERENT

Not staff augmentation. Not enterprise consulting. Not a self-serve AI build tool.

Four alternatives cover most of the market for AI product builds. Each wins on a specific shape of need. AI Product Engineering is built for teams who want a fixed-scope engagement with engineering credibility and AI fluency at boutique scale, priced against milestones you can verify rather than against hourly time.

Comparison AI Product Engineering Staff-aug marketplaces Larger product studios Offshore project shops Self-serve AI build tools
Engagement structure Sprint 0 scoping plus a milestone-billed build. One fixed-scope engagement. Per-engineer hourly placement. You own the deliverable. Sales-led discovery, then a multi-team build proposal. Fixed-price build off a brief, no working discovery. You drive the tool. No team, no engagement.
Scope before the build Two to four weeks of working engineering and product time, six concrete deliverables. You write the scope. The marketplace places engineers against it. A discovery deck and a SOW, typically one to two weeks. A brief from you, then a fixed-price quote. Often missing acceptance criteria. You scope it yourself in the tool as you go.
Team shape Two to four people: full-time engineers paired with the fractional roles the build actually needs. Lean by design. One engineer at a time. You assemble the team. Five to fifteen across multiple practice areas. Partner ladder. Variable. Often shared across clients. You are the team.
AI fluency Baseline for every engineer. AI tooling in the build loop comes standard. Per-individual. You filter for it. Practice-level, often priced as a premium specialism. Variable. Often retrofit onto a non-AI process. AI is the tool itself, not the engineering around it.
Pricing shape Sprint 0 fixed fee, build milestone-billed against acceptance. Hourly markup per engineer. Time-and-materials or fixed bid, priced for $200M+ enterprise budgets. Hourly or fixed-price. Cost-led positioning. Per-seat subscription. Production-grade work is your problem.
Best fit Startups shipping a new product, or companies commissioning a defined AI product for internal or external use, where milestone-billed economics fit the budget shape. Teams who want point-and-shoot capacity and will own the build management. Multi-team initiatives at enterprise budgets. Cost-led builds where price beats engineering quality. Founders prototyping solo before the codebase needs production-grade architecture.
Where it loses Teams wanting hourly billing. Multi-month enterprise procurement. Engagements that need point-and-shoot staffing rather than a fixed-scope build. Sustained team stability. Partnership layer. Lean-team scope sizes that do not fit enterprise budgets. Engineering quality. Codebase extensibility after the agency exits. Production-grade architecture. Integration with existing systems. Codebases that real engineering teams will extend.

Two things make this engagement different in kind. Sprint 0 is real engineering work, so the scope the build runs against is the kind of artifact another team could build against if you walked. And milestone billing is tied to acceptance criteria set in Sprint 0, not to hours logged, so the schedule and the billing are the same thing. Both pieces hold the build economics where most boutique builds fail.

SELECTED WORK

Product builds shipped under the milestone-billed structure.

AI Product Engineering ships product against a milestone plan set in Sprint 0. The cases below are the published anchors today. As more closed engagements clear the case-study template, the strongest of them will land in this section and the placeholders will retire.

PRD generation reduced from hours to minutes for PMs using the tool

Leanware

An AI agent built by Leanware engineers that generates structured product requirement documents from a short feature or product brief. Internal proof point of how Leanware applies AI to document automation and product-management workflow.

AI Product Engineering Read case study
AI analysis of GitHub commit history paired with task estimates running in beta

Leanware

An AI-powered internal tool built by Leanware engineers to measure how accurate task time-estimates were against the actual commit history. CodiQ analyzes GitHub commits with OpenAI and surfaces over- and under-estimation patterns for the team.

AI Product Engineering Read case study
Event-driven serverless trading platform in production, executing rules-based strategies

2Moon Capital LLC

A rules-based automated trading platform built for 2Moon Capital. Event-driven serverless architecture executes the firm's trading strategies via Interactive Brokers, with paper-trading environments alongside the real-money execution path. Not ML-driven.

AI Product Engineering Read case study
View all case studies

CLIENT VOICE

From the founders who ran Sprint 0 and shipped the build.

Leanware built our AI-powered fitness assistant from concept to launch across iOS and Android. They turned our concept into a comprehensive, intelligent platform we can actually take to market, with the safety filtering and conversation design that AI fitness products need to ship responsibly.

GF

GloFlow founder

Founder, GloFlow

See all client reviews

FREQUENTLY ASKED

Questions we hear on the discovery call.

Most of these come up before the Sprint 0 proposal is on the table. The answers below match what actually happens once Sprint 0 starts and once the build engagement runs.

  • What does Sprint 0 produce and how is it priced?
    Sprint 0 is a fixed-fee scoping engagement set on the discovery call based on complexity (number of stakeholders, scope of the discovery, integration points to inventory, depth of the design-system work). It runs two to four calendar weeks and produces six concrete deliverables: a closed scope with acceptance criteria for the build, sized user stories ordered into a build roadmap, repository scaffolding and infrastructure-as-code groundwork, the foundational UI and design system the build will extend, a milestone plan with the first two milestones defined in depth, and an engagement-economics summary covering team size, sprint cadence, milestone billing schedule, and total estimated build cost.
  • What happens at the end of Sprint 0?
    A go or no-go decision, structured as a working session rather than a sales close. Either side can walk with the Sprint 0 deliverable intact and no further obligation. You walk when scope shifted, when internal alignment changed during the two to four weeks, or when you want to take the deliverable in-house. Leanware walks when scope outgrew your runway, when readiness is weaker than the call suggested, or when technical risk surfaced during scaffolding would break the proposed economics. The go or no-go is not a sales failure when the answer is no. It is the engagement working as designed.
  • How does milestone billing actually work?
    Each milestone has acceptance criteria that were written in Sprint 0 and a billing amount tied to that milestone. The build runs in weekly sprints with end-of-sprint demos. Milestones map to those sprints and the epics defined in Sprint 0, with at least two milestones per month. When a milestone meets its acceptance criteria, you sign off. Billing fires on your sign-off, not on hours logged. If scope changes mid-build, the change becomes a new milestone with its own acceptance criteria and its own billing. The existing milestone keeps its price and its acceptance bar. You can model runway against the milestone schedule because the schedule and the billing are the same artifact.
  • Why a two-to-four-person team and not a bigger one?
    Two to four people is the standard composition because adding people above what the scope warrants rarely improves shipping velocity and adds coordination cost on your side. The mix is full-time engineers paired with the fractional roles your build actually needs (delivery manager, product manager, QA, designer). The composition is set in the Sprint 0 economics summary and committed to in the proposal. When scope genuinely grows, the answer is a new milestone with its own acceptance criteria and its own billing, not more head count on an existing milestone you already priced.
  • How is this different from staff augmentation or hiring through Toptal or Andela?
    Staff-augmentation marketplaces place engineers. AI Product Engineering ships products. Sometimes people start the conversation thinking they want staff aug and realize partway in that what they actually need is a team that owns the deliverable. The Sprint 0 plus milestone-billed structure is the fixed-scope version of that realization. When point-and-shoot staffing is honestly the right answer, the conversation routes to the Dedicated AI Engineering Teams line.
  • How is this different from product engineering services from larger firms like HCLTech, IBM, or PwC?
    Larger product engineering services firms are sized for enterprise procurement: multi-team initiatives, multi-month discovery, partner-and-associate staffing models, and budgets that start above what a startup carries. AI Product Engineering is the boutique alternative. The same scoping-then-milestone-billed structure, but with a lean team and engagement sizes that match a 12 to 24 month runway.
  • How is this different from Bolt, v0, Cursor, or Replit Agent?
    Self-serve AI build tools are real for prototyping and for early-stage builds when the founder is building solo. They become the wrong tool when the build needs production-grade architecture, when the codebase has to be extended by a real engineering team, or when the AI-tool output needs to integrate with existing systems. Some of the strongest qualified leads on this line arrive after hitting that wall on a full-self-serve build, because they have already learned what the tool cannot do. AI Product Engineering is the next step when the tool has run its course, not a competitor to it for the prototype.
  • Who owns the code and the IP?
    You. The master engagement agreement assigns full ownership of the codebase, the design assets, and any AI prompts or fine-tuned artifacts produced during the engagement to you. Leanware retains rights to reusable infrastructure patterns, internal tooling, and engineering practices that are not specific to your product. The Sprint 0 deliverable is yours from delivery, whether you continue with the build or not.
  • Should I hire in-house instead?
    Sometimes hiring in-house is the right answer and the Sprint 0 deliverable will say so when it is. The engagements where AI Product Engineering is the better fit are the ones where the build needs to ship on a milestone schedule the board is expecting, where hiring at the right bar would not close on that timeline, or where the AI fluency in the placed team is a meaningful capability gap the in-house plan would not solve.
  • How fast can we start?
    Sprint 0 typically kicks off within one to two weeks of signing. The discovery call runs 30 minutes and is scheduled inside a week. The build engagement starts immediately at Sprint 0 close on a go decision, since the team composition and milestone plan are already set in the Sprint 0 economics summary.
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Talk to someone who gets your business. And can build the solution.

We walk through what you are building, decide together whether Sprint 0 or Dedicated AI Engineering Teams is the right shape, and leave you with a concrete next step.

Tell us what you are building and where you are in the build. Someone who understands the business and the engineering will walk through it with you, decide together whether Sprint 0 or Dedicated AI Engineering Teams is the right shape, and leave you with a concrete next step.

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