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KRASTOR

Services · Product & Venture Engineering

We don't just fix the business. We help you build the next one.

Sometimes the constraint isn't an inefficiency. It's a missing product or a new revenue line you can't staff for. We design and ship AI-native products and new business lines, the same way we built our own.
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What's included

The product engagement, named.

From thesis to launch to ongoing evolution. Every consulting build is R&D for product. We see the same patterns across engagements and design products that capitalize on them.

01

AI-Native Product Design

From concept to architecture: we design the product for the model-and-data moat that makes it defensible, not just the feature set that makes it usable.
02

MVP to Launch

Real software, shipped. Not a prototype handed off to a dev shop. A working product built on a modern, model-agnostic stack, deployed and live.
03

Productize an Internal Capability

Turn a process you built into a product you sell. If you've developed an internal workflow or intelligence layer that solves a real problem, we help you package and launch it.
04

New Revenue-Line Engineering

Design and build the systems behind a new business line: pricing, fulfillment, customer experience, and the attribution that proves it from day one.
05

Technical Co-Founder Engagement

For founders who need a technical partner, not a vendor. We bring the architecture decisions, the build, and the strategic judgment, and stay embedded as the product evolves.
06

Go-to-Market Infrastructure

The acquisition, onboarding, activation, and attribution infrastructure behind the launch. Designed and instrumented before the first user arrives, so performance is measurable from day one and conversion compounds instead of churning.
07

Ongoing Product Evolution

The product compounds. Better models, more usage data, and smarter workflows make it more valuable over time, and we stay embedded to run that evolution.
08

Product Discovery & Validation

Problem framing, user research synthesis, and assumption testing before a line of code is written, so the build starts from a validated problem, not a hypothesis dressed up as a requirement.
09

Data & AI Moat Design

The data flywheel, model-training loops, and intelligence layers that make the product harder to replicate over time, designed into the architecture from the start, not added after the product gains traction.
10

Pricing & Packaging

Monetization architecture designed around value delivery: tier structure, usage-based components, and the pricing logic that aligns what customers pay with what the product actually does for them.
11

Analytics & Instrumentation

Event tracking, funnel instrumentation, and product analytics built into the stack from day one, so every product decision after launch has behavioral data behind it.
12

Iteration & Roadmap Management

The prioritization framework, the feedback loops, and the cadence that keeps the product moving toward the outcomes that matter, managed by the embedded seat, not left to chance after delivery.
13

Fundraising-Ready Architecture

Technical documentation, system diagrams, and the architectural narrative investors ask for, produced as a natural output of the build, not assembled in a rush before a diligence call.

How it works

Shape, build, evolve.

Three stages. Each one has a gate: you don't commit to the build until the thesis and architecture are right, and the product doesn't stop improving after launch.

Step 1

Shape

The product thesis, the moat, and the architecture. What makes it defensible? What's the data flywheel? What does the model need to compound with usage? These questions get answered before a line of code is written.

Step 2

Build

MVP to launch on a modern, model-agnostic stack. Real software, deployed. Not a proof-of-concept. A product your first customers can actually use.

Step 3

Evolve

Better models and more usage make it smarter. We stay embedded to run the evolution: tuning the intelligence layer, expanding the feature set, and compounding the moat.

In practice

We built our own. Every engagement is R&D for the next one.

Krastor is building its own AI-native product from the patterns we observed across client engagements. Every consulting build taught us something about how organizations capture, route, and act on institutional knowledge. That pattern became a product.

The stack is model-agnostic by design. Claude, GPT, Gemini, open-weight Llama and Mistral variants. The architecture doesn't bet on one model family because the landscape moves faster than any single bet. Products we build inherit that posture.

See what we are building →
Concept → launch
The full arc: thesis, architecture, build, and deployment
Model-agnostic
By design: the product doesn't bet on one model family as the landscape evolves
Building our own
An AI-native product in development, built from consulting patterns
Equity considered
For the right venture: revenue-share or equity alongside fees when it aligns both sides

Pricing logic

Priced on the build and the relationship, not hours.

Frameworks, not rigid SKUs. Every product engagement is shaped by the scope of the build, the depth of the embedded relationship, and whether the venture structure warrants an equity conversation.

Product discovery & architecture

The product thesis, the moat design, the data architecture, and the build plan. The gate before the MVP commitment, scoped by the complexity of the intelligence layer. Exact numbers are sized to your venture and put in writing before you commit.

Scoped in the diagnostic

MVP build

Scoped by feature set, model integration depth, and launch infrastructure. Priced on the build, not the hours. Scope is defined and priced once before work begins.

Fixed for the build

Equity / revenue-share

For the right venture, we'll consider revenue-share or equity alongside fees. Structure depends on stage, scope, and the length of the embedded relationship.

Considered for the right venture

Questions

Straight answers.

Are you an agency that builds apps?

No. Agencies scope features, build them, and invoice. We engineer products with a data and intelligence moat, built to be defensible, not just functional, and we stay embedded as the product evolves. The difference shows up in what you have after year one.

Can you really build something fundable?

We are building our own: an AI-native product designed from the patterns observed across client engagements, in active development now. The same model, applied to your product, for your venture.

Do you take equity?

For the right venture, yes. We'll consider revenue-share or equity alongside fees. The structure depends on the stage, the size of the build, and how embedded we'll be in the long run. We don't take equity on every engagement; we take it when it aligns both sides for the long game.

Continue

AI Architecture

The intelligence layer every product is built on.

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Growth & Acquisition

The acquisition engine that fills the pipeline after launch.

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Embedded Partnership

The ongoing seat that runs product evolution with you.

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Engagement starts here

Have a product or a new revenue line you can't staff for?

Thirty minutes. We map your operation, name what's actually slowing it down, and tell you what we'd do if we were running it. You get a written stack assessment after the call, whether you hire us or not.

Book a Diagnostic Take the AI Readiness Assessment

Not limited to what's listed. Every engagement starts by assessing what your business actually needs, and we build whatever it requires.