Custom app development that starts small and proves value
A first release your team can actually test
- Scoped to the workflow
- Testable release in weeks
- Certainty where it matters
A focused application built around one real user, one valuable task, and the system states needed to deliver it reliably — not a feature backlog dressed up as a strategy.
- Scoped around the workflow, not a wish list
- A testable first release in weeks, not quarters
- AI where it helps, deterministic logic where it must be certain
- US & UK clients
- London studio, US-based team
- Human-reviewed automation
- Scoped before build
- Staged implementation
- 24-hour team coverage
App projects get over-scoped before the goal is set
Feature lists grow before the workflow is defined. Dashboards, notifications, payments, messaging and reporting each sound useful on their own, so each one gets added — while the question that actually decides the budget, what has to work first, goes unanswered. Screens nobody asked for absorb the money. Edge cases multiply faster than anyone can test them. By the time it ships, no one can say whether it solved the problem, because the problem was never pinned down. Then the real cost lands: the release can’t be extended, so version two is a rebuild.
- Scope balloons before the core workflow is agreed
- Launch happens with no way to test whether it worked
- Every future change means renegotiating the whole roadmap
- Staff still retype what the app just collected
- No one can say which system holds the true record
What a disciplined first release gets you
A working product built around a real user and a real task — reliable enough to trust, small enough to ship fast, and structured so the next release extends it instead of rebuilding it. Approvals, failures, empty states and permissions are handled, not discovered in production. Records have an owner, so the app writes back into the systems your team already runs. Once it’s live, completion rates and abandoned states decide what gets built next.
A testable first release
A coherent product that can be used and evaluated, not a feature demonstration.
Lower decision risk
Critical assumptions are surfaced before the most expensive build work.
Operational readiness
Permissions, monitoring, fallbacks and ownership are included in the product model.
What you receive
Product discovery, UX, data structure and engineering treated as one system — not four separate hand-offs. The user and the job, the state model behind the screens, the roles and sources of truth, the integrations checked against real API limits, and the release testing that has to pass. Locale is designed in rather than patched later: UK postcodes and US ZIP codes, both date orders, both currencies, both time zones.
One user, one valuable task
Anchor the first release to a specific person and outcome.
States before screens
Define approvals, errors, empty states and completion before polishing the interface.
Data with ownership
Map records, roles and sources of truth before integrations multiply.
A roadmap earned by use
Expand the product from evidence, not the original wish list.
A release built to reduce risk
We define the states your system must handle before we design a single screen — what happens when data is missing, when an action fails, when two users collide. The critical route is prototyped with acceptance criteria attached while change is still cheap, and open questions get written down rather than hidden behind a polished design. Where AI is involved it gets approved inputs, approved outputs and a fallback, with human review kept on any decision that carries real cost. That discipline is what makes a first release trustworthy enough to build on. Each release ships with the evidence that it works, not just the screens.
Feature-led build
Treats the backlog as the strategy: scope is a list of screens, and the hard questions — states, permissions, ownership — wait until they are expensive.
Silverstone AI first-release model
Makes the user, workflow, system states and evidence explicit first, so the release ships smaller, proves something, and can be extended rather than rebuilt.
Proof, not promises
Representative figures observed across Silverstone AI delivery — evidence of what a disciplined build has achieved.
Results vary by scope, data quality, implementation and operating environment.
How delivery works
From workflow to a testable product decision — without the scope creep. Discovery ends in a written first-release definition and the evidence that would justify it. The prototype carries acceptance criteria you sign off before production code exists. The build implements that scope and nothing else. After launch, the roadmap is set by adoption, errors and abandoned states.
Discover the value
Define the user, task, evidence and constraints.
Prototype the states
Test the route before production scope expands.
Build the first release
Implement the experience, data, permissions and integrations.
Learn from use
Prioritize the roadmap from adoption, friction and commercial evidence.
Define the workflow before the build gets expensive
Questions before a build begins
No. A rough workflow and the problem it needs to solve is enough to start the conversation.
Often, yes. We start by reviewing the current architecture, data and operational risk — a targeted improvement can be the smarter route.
Where it adds real value and can be bounded — for retrieval, classification or guided actions. Deterministic logic wins wherever the rule is already known.
No. We cut unnecessary scope, not reliability, permissions, testing, or the product logic real use depends on.
Custom app builds are scoped and quoted as a defined project fee once discovery has settled the first release. Silverstone AI publishes no fixed app price, because cost follows the workflow, the platforms, the integrations and any data migration involved. Payment is staged: 50% at kickoff, 30% across development milestones, 20% on completion. Third-party software, API, hosting and usage costs are itemized separately before work begins, and the £150$195 per hour senior AI engineer rate applies only where specialist work is explicitly priced hourly.
Timelines follow scope, not a template. The first-release workflow, the platforms, integrations, permissions, design depth, data migration, testing and how quickly your team approves decisions all move the date. Silverstone AI’s published pricing puts the average project at twelve weeks from discovery to deployment, with roughly 15% of effort in discovery and planning, 60% in development and integration, 15% in testing and deployment, and 10% in training and hand-over. Discovery produces the delivery route before anything is committed.
A responsive web application is usually the fastest way to reach users on every device and prove the workflow. A progressive web app adds installability and a limited set of device behaviors. Native iOS and Android delivery is justified by deeper device access, app-store distribution, offline use or performance constraints — not by prestige. Silverstone AI chooses the route from the use case and the commercial model, and will say plainly when a web app is enough.
Custom software is not always the right answer. An existing product may cover most of the requirement at lower risk, and a configured platform can beat owning a codebase you then have to maintain. A custom build earns its place when the workflow or the differentiation is specific enough that compromise costs more than construction. Where the decision is genuinely open, Silverstone AI’s AI and automation consulting runs a structured build-versus-buy assessment before any engineering starts.
A permanent team makes sense when there is a continuous roadmap to keep it busy and the product is central to how the business earns. An agency suits the stage before that, when the scope is still uncertain and hiring against an unproven idea is the larger risk. Silverstone AI is often used to prove the first release and hand over a documented product an internal team can take on, with support and hand-over terms set in the proposal.
Ownership, licensing, source access, third-party dependencies and hand-over terms are defined contractually in the proposal, because the right arrangement depends on the engagement. Silverstone AI’s commercial intent is clarity rather than lock-in: the proposal should state what is being created, what depends on external services you license directly, and exactly what your business can operate after delivery. Ask for that wording in writing before signing — on any build, with any studio.
Integration starts with the data model: which system holds the true record, who is allowed to change it, and what happens when a call fails or arrives twice. From there the app connects to CRM, scheduling, payments, messaging, document storage and analytics through documented APIs and webhooks, with permissions and fallbacks agreed first. Third-party availability, rate limits and licensing are confirmed during discovery rather than assumed. An app that leaves staff retyping has only moved the bottleneck.
Roles and permissions are modeled before screens are designed, so a customer, a staff member and an administrator never share a view by accident. Silverstone AI maps where each record lives, who owns it and how long it is kept, then identifies the obligations that apply — UK GDPR and the Information Commissioner’s Office in the UK, state privacy laws in the US — so your legal advisers review a system they can actually see. US healthcare work is limited to HIPAA-conscious, non-clinical workflows.
Silverstone AI is a London studio with US-based team members, and builds for both markets. The product is built for the market it serves: US or UK spelling, both date orders, currency and tax handling, time zones, postcodes or ZIP codes, and the tools your team already runs, whether that is HubSpot, Salesforce, Stripe or Shopify. Support runs across UK and US business hours, with response times set in the written agreement.
The roadmap is set by real use: completion rates, errors, support requests, abandoned states and the commercial value of the next feature. A request moves forward because it improves the product, not because it appeared on the original list. Ongoing support is a separate retainer, from £350$450 per month for business-hours cover across UK and US time zones, monitoring and scheduled reviews, with response times agreed in writing. Higher tiers add 24/7 priority cover from £1,250$1,600 per month.
Move from idea to a testable product decision
Every quarter spent scoping a feature list is a quarter your competitors spend shipping. The first conversation is exploratory and commits you to nothing.