The roadmap is a list of opinions.
What it costsMonths spent building features nobody uses.
What changesDiscovery with real users, and a roadmap tied to what they actually need.
Agentic OS · SignalAI Transformation · AI Product Engineering
AI-native products, from first prototype to scale.
For enterprises building AI products, customer apps and internal platforms. We help you decide what to build, prove it with real users, and build it with AI inside from the start. Then we run and improve it with you, or hand it to your team.
From idea to scale
- DiscoverInterviews and MVP definition
- StrategyRoadmap and go-to-market
- BuildMVP and SaaS products
- MobileNative and cross-platform apps
- PodsDedicated engineering teams
- MeasureProduct analytics and ROI
Code in your repositories, from the first commit
Written for
CPOs, CTOs and digital leaders building AI products, customer apps and internal platforms
The question you came with
Is this product worth building, and how do we build it with AI inside from the start?
Part of Enterprise AI Transformation
Products with AI inside. New products and apps built with agents and AI in the core workflows, under the same evaluation and controls.
See the whole programmeWhat is slowing your product down
If two of these are true, the rest of this page is about you.
AI was bolted on after launch.
What it costsA chatbot in the corner, and no real change to the product.
What changesAI designed into the core workflows of the product, with evaluation from day one.
Agentic OS · AgentsHiring engineers takes too long.
What it costsThe roadmap slips quarter after quarter.
What changesA dedicated pod that works in your tools and rituals, scaled up or down as needed.
Agentic OS · FlowNobody knows which features earn their keep.
What it costsInvestment follows the loudest voice.
What changesProduct analytics on funnels, cohorts and usage, reviewed in every planning cycle.
Agentic OS · PulseFrom idea to scale
Each stage ends with something you can test, fund or stop.
- Stage 01 · Discover
Prove the problem
Interviews and data show whether the problem is real, who has it and what they would pay to solve it.
- Stage 02 · Prototype
Test before you build
A clickable or working prototype, tested with real users, including the AI parts.
- Stage 03 · Build
Ship the MVP
The smallest product that delivers the core value, released in weekly increments.
- Stage 04 · Scale
Measure and grow
Analytics, performance and a team sized to the roadmap, run by us or handed to yours.
What we build
The capability on the left, what your team would notice on the right.
- Discovery
Product discovery and prototyping
User interviews, problem framing, MVP definition and clickable prototypes tested before code.
- Strategy
Product strategy and roadmap
Positioning, pricing logic, roadmap and go-to-market plan for a new product or a major release.
- Build
MVP and SaaS product build
Web and SaaS products on modern stacks such as Next.js, MERN and Laravel, with AI features built in.
- Mobile
Mobile app development
Native and cross-platform apps for customers, field teams and partners.
- Teams
Dedicated engineering pods
A product team that works in your tools, standups and planning, scaled up or down with the roadmap.
- Improve
UX research and product analytics
Journeys tested with users, and Mixpanel, Amplitude or GA4 funnels showing what works.
What stays yours
We build and hand over
- Source code in your repositories
- Designs, prototypes and research
- Architecture and runbooks
You keep and control
- Your product and its IP
- Your cloud and app store accounts
- Admin access, in your name
How an engagement runs
The same four steps whatever we build. The first one is small, fixed-fee and useful on its own.
Start with a fixed-scope offer
A fixed scope, a set timeline and a deliverable you keep. The quickest way to start.
Assess
AI Product Sprint
A tested prototype and a build plan, so you can decide whether to build
- Time
- 4 to 6 weeks
- Price
- Fixed fee, agreed on a call
- For
- CPOs, CTOs and digital leaders
The problem. A product idea, or an AI feature, has support but no evidence that users want it or that the AI can do the job.
What happens, week by week
- Week 1Frame the problem
Interviews with target users and a look at the data, to agree the problem and who has it.
- Weeks 2 to 3Prototype
A clickable prototype, plus a working proof of the AI part on real data.
- Week 4Test with users
Real users try the prototype, and the AI output is scored against what good looks like.
- Weeks 5 to 6Decide and plan
Findings, a go or no-go recommendation, and an MVP scope, architecture and estimate.
You get
- User research findings
- A tested prototype and AI proof of concept
- Go or no-go recommendation
- MVP scope, architecture and estimate
Who signs off
The founder or product owner, with the CTO for architecture
What we need from you
Access to target users, sample data, and a decision-maker each week
Not sure where to start? Begin with the AI Transformation Review: two to three weeks, $1,500, credited to month one.
Questions
Who owns the code?
You do, from the first commit. Everything lives in your repositories and accounts.
Can you work as part of our team?
Yes. A dedicated pod works in your tools, standups and planning, and you can scale it up or down.
Do you build AI features into products?
Yes. AI is designed into the core workflows, with evaluation and guardrails, not added as a chat window at the end.
Where should we start?
With the AI Product Sprint: a few weeks of discovery and a working prototype, so you can decide whether to build.
Keep going
Where AI Product Engineering fits your industry, what to read next, and the tools involved.
Industries we do this in
Playbooks
Tools we review for this
AI transformation for the agentic engine era
Start with a fixed-fee AI transformation review. We map your systems, data and teams against the six layers of Pegasus Agentic OS and name the first workflows to hand to agents. You keep the findings whether or not you work with us.