Your AI product should be live. It is not.
Altitude Group takes your defined business problem and turns it into a working agentic product in 21 days. Fully project managed. Fixed scope. Fixed price.
Built by an AWS veteran who has shipped 200+ AI and web products.
We are selective by design. Limited sprints available each quarter.
Scope & Spec
Decompose the workflow. Fix the spec.
Build
Agentic build against real data, not demos.
Harden & Ship
QA, launch live, handover.
Products built inside the studio. AI-native product ideas turned into useful software.
AWS PartnerYou have a problem that AI should solve. You have not shipped it yet.
The tools exist. The data often exists. What is missing is the operating discipline to take it from idea to live product without it going wrong.
Every founder and operator we talk to runs into the same wall:
The pressured founder
Roadmap bigger than the team. Customer commitments slipping. Priorities change before anything ships.
The would-be hirer
A defined project that needs a developer. Months of interviewing. The project still is not live.
The domain expert
Deep knowledge, no senior technical lead. Dependent on whoever they hire first. Cannot scope or de-risk the build.
The overwhelmed AI explorer
Has looked at every tool. Run internal experiments. Nothing has reached production.
Wrong person. Wrong tool. Wrong scope. A token bill that cost $40k and delivered nothing.
Agentic product development still needs product management and project management. That is the gap. That is what we fill.
20% of the cost of a mid-tier developer. A working product at the end.
A single mid-tier developer hire runs $180,000 to $300,000 fully loaded per year, before one feature ships. Recruiting takes months. The project that justified the hire keeps slipping.
The 21 Day Sprint ships first. You hire later, into proof, not into hope.
The deal
From $30,000.
Fixed price.
21 days.
A live product.
From problem to live product in 21 days.
Three disciplines married into one delivery: product management, project management, and AI-native execution.
You bring the problem. We bring the operating discipline to take it live.
Scope and Spec
- Decompose the workflow into decisions, data, actions, and outputs
- Find the leverage point where AI creates the most value
- Define the spec, the guardrails, and what we will not build
- Agree the success measure before any code is written
Build
- Agentic development against the agreed spec
- Real data and real tools, not a demo
- Structured outputs and human review points
- Daily progress, controlled token spend
Harden and Ship
- Integration, testing, and QA gates
- Launch the product live
- Handover, documentation, and feedback loops
- Final delivery report
What is included
Product management
- — Turn the business problem into a clear spec
- — Decide what to build and what to leave out
- — Keep the work tied to outcomes, not demos
Project management
- — Fixed scope, fixed timeline, fixed end date
- — Review and QA gates
- — Control of scope, spend, and quality
AI-native execution
- — Agentic build using best-in-class tools
- — Real integrations and data pipelines
- — Human review where trust and risk matter
How we reduce your risk
- Fixed scope agreed in Week 1, so you know exactly what ships
- Fixed timeline, so the project cannot drift for months
- Fixed price, so spend cannot blow out
- Weekly reporting, so you always know where it stands
What a 21 Day Sprint produces.
Blackline Intelligence is an automated government performance intelligence system. It ingests every major Australian public sector data source daily, maps what it finds against accountability benchmarks, and surfaces anomalies before any human analyst would notice them.
The problem
Australian government performance data is public but inaccessible. Contracts awarded without open competition. Budgets growing without KPIs. FOI requests stonewalled. Audit findings that keep recurring. No one was watching all of it at once.
The sprint
- Week 1
Defined the full detection model
Eight official data sources. Seventeen signal categories. Mapped the detection logic for procurement risk, governance gaps, delivery failure, and fiscal exposure. Agreed the schema and the success measure.
- Week 2
Built the system
Ingestion pipeline, detection engine, signal classifier, and findings generator. Real government data from day one. No synthetic demos.
- Week 3
QA and live deployment
Audit trail validation, confidence flagging, and live deployment. Blackline went live monitoring the full Commonwealth procurement record.
Key numbers
- Official sources monitored8
- Signal categories17
- Total signals detected274,659
- Findings generated13,461
- Dollar exposure identified$164.6B across 7,546 contracts
- Build time21 days
- StatusLive, running daily
A data-rich domain problem with no technical lead became a live, daily-running intelligence product in one sprint.
Three ways to work with the studio.
Product Strategy Sprint
You have a strong product opportunity, but the scope, architecture, or launch path needs to be made clear before a build.
- Product thesis
- Buyer or user definition
- Problem and use case map
- Product scope
- Technical approach
- AI and agent workflow outline
- Data and integration requirements
- Launch plan
- Build estimate
21 Day Sprint
FeaturedYou have a defined problem. You need it live, not in six months.
- Full product and project management across every day
- Clear spec agreed in Week 1, before any code is written
- Agentic build with real data and real integrations
- Fixed scope, fixed price, fixed timeline
- Weekly delivery reports
- Live product handover with documentation
Product Studio Retainer
You have a product in market or launching soon and need ongoing improvement without hiring a full AI product team.
- Roadmap support
- Feature design
- AI workflow updates
- Bug fixes
- QA and release support
- Usage review
- Performance improvement
- Product reporting
We are selective because good AI products need real judgment.
Good fit
- You have a clear user or buyer.
- You have domain insight or proprietary expertise.
- AI or agents are central to the product.
- The product has a business case.
- A founder, operator, or executive owns decisions.
- You have budget to build.
Poor fit
- You want a cheap app build.
- You want a generic chatbot.
- You have no clear user.
- You have no internal owner.
- You want equity-only development.
- You need broad transformation advice instead of a product.
From product opportunity to working software.
Clarify the judgment.
We identify the expertise, decision, workflow, or market insight the product needs to make usable.
Shape the product.
We define the user, use case, product scope, experience, data needs, AI workflows, and launch path.
Build the software.
We design and build the full-stack product, including AI workflows, agentic systems, integrations, admin flows, and review loops where needed.
Launch with control.
We test, QA, release, monitor, and keep human approval where the product carries risk.
Improve after use.
We learn from usage, update workflows, add features, and improve product performance after launch.
Built by a software operator with 200+ production web applications shipped.
Altitude Group is led by Nick Holmes a Court, a software developer and consultant who has brought more than 200 web applications into production for enterprise, startup, and founder-led teams.
Experience
Recent product work
- Blackline IntelligenceAutomated government performance intelligence for Australia.
- gtm-osAI GTM operator for seed to Series A SaaS and AI startups.
- ExitProofTax residency exit planning software for Australian high earners.
- LadderOSAgentic product development and operations for Ladder Inc.
- JudgementAIExpert judgment turned into AI diagnostics and lead qualification.
Not an AI automation agency. Not a traditional dev shop.
AI automation agency
Usually automates narrow workflows.
Traditional dev shop
Usually sells human delivery capacity.
AI strategy consultant
Usually advises but does not operate.
Altitude Group
Builds AI and agentic products with a disciplined studio operating model, then launches and improves them after release.
Have an AI product worth building?
Bring us the problem. We will pressure test it, confirm it fits a 21 day sprint, and agree the outcome. If it is not a fit, we will tell you.