Inspired By Frustration// Human-led AI software development
AI software development services for teams that need production apps, agent workflows, and faster delivery without handing quality control to the tools.
Production acceleration
You get parallel AI implementation where it is safe, plus senior engineering judgment on architecture, QA, security, and release.
50+
production apps shipped
3-5x
typical greenfield acceleration
12 yrs
enterprise delivery judgment
AI-accelerated development is a delivery engagement, not advice: a senior engineer ships your production application in weeks — using Claude, Cursor, Lovable, Next.js, Supabase, and MCP servers — while owning architecture, code review, security, and release quality the whole way.
If you are still deciding where AI belongs in your product, start with the AI consulting services engagement instead; this page is for teams that already know what they need built.
Turn a workflow, internal tool, SaaS idea, or customer-facing product into a scoped production build with clear tradeoffs, data model, auth, and deployment from the start.
Build Next.js, TypeScript, Supabase, API, and agent features with AI-assisted implementation and human-owned architecture, code review, testing, and release control.
Decide what to build, what to buy, where agents help, and where standard software is cheaper and safer before the build starts. For a deeper advisory engagement, see the independent AI consulting services page.
Use multiple AI coding agents for bounded tasks while a senior engineer controls contracts, integration, performance, security, and the final production standard.
AI-accelerated development is a delivery model where a senior engineer uses AI coding tools to compress implementation time while still owning product judgment, architecture, security, tests, and deployment. The tools increase throughput; they do not become accountable for the product.
Use AI consulting services before a build when you are choosing between vendor tools, custom agents, workflow automation, or a full product. A short decision sprint can prevent months of building the wrong AI layer.
An AI development agency is useful when you need a large team and many parallel workstreams. A senior independent operator is often better when the product needs one accountable owner, fast decisions, and less coordination overhead.
AI-accelerated delivery works best for SaaS MVPs, internal tools, dashboards, API integrations, workflow automation, Lovable-to-Next.js rebuilds, and agent-backed products where the requirements are concrete enough to review.
AppHandoff turns Lovable and AI-built prototypes into production engineering work with contract scans, review workflows, and human approvals. MCP Beast routes AI tool calls through a governed MCP proxy with policy checks and audit trails. Context Capture embeds bug reporting with screenshots, console logs, and GitHub-ready issue data into web apps. All three are production products built with this exact AI-accelerated workflow — each case study documents the scope, stack, and what acceleration actually compressed.
Define the workflow, buyer, data, edge cases, and the smallest production version worth shipping.
Set the stack, data model, route/API boundaries, auth rules, observability, and test strategy before agents write volume code.
Use Claude, Cursor, Lovable, and focused agents for implementation while keeping tasks small enough to review and merge safely.
Human review covers security, performance, accessibility, SEO, error states, and deploy behavior before the work reaches production.
The operating model is based on shipped products with auth, data, SEO, billing, agent flows, and ongoing maintenance.
The speed gain comes from reducing boilerplate and iteration time while preserving senior engineering review.
The review layer comes from complex delivery work, stakeholder pressure, and production accountability.
Bring the business goal, current stack, and deadline. I will tell you what AI acceleration can safely compress and what should stay deliberately human-reviewed.
Use AI-accelerated product development to move from rough workflow to a production MVP without assembling a full team first.
Book a build callUse focused AI software development services to clear integration, dashboard, and workflow projects that are too important for a prototype agency.
Send the scopeUse a senior AI developer to prove where agents help, where they do not, and what review gates your own team should adopt.
Review the operating modelAI-accelerated development is fastest when the human engineer controls architecture, task boundaries, review, and release quality while AI tools handle repetitive implementation work.
The main business value of AI software development services is not cheaper code; it is shorter time between a clear product decision and a production-ready version users can try.
A team should hire an AI developer when it needs production ownership across product, frontend, backend, AI workflow, and deployment rather than a prompt operator or prototype vendor.
AI consulting services are useful before a build when the key question is where AI belongs in the workflow, which model or vendor to use, and what risk controls are needed.
AI-accelerated product development works best for concrete workflows with clear users, clear data, and a reviewable definition of done.
AI-generated code becomes production software only after human review covers security, data access, performance, accessibility, observability, and failure states.
Best fit
MVPs, SaaS rebuilds, internal tools, agent workflows, dashboards, and integration-heavy products with clear business value.
Not fit
Unbounded research, unclear ownership, safety-critical systems without domain review, or teams that want to skip code review because AI wrote it.
Production bar
Every build still needs typed contracts, error handling, security boundaries, performance checks, and understandable APIs underneath it.
// what clients say
We came in with a Lovable prototype and a board deadline. Three weeks later we had a typed backend, real auth, and an MCP server our support agents actually trust. The POC went to production without the usual rewrite tax.
DaanHead of Engineering
fintech scale-upPOC → production
I needed someone who could orchestrate a swarm of coding agents and still own the architecture. The agent-orchestration setup shipped 40+ PRs in a week — every one reviewed, scoped, and reversible. No hallucinated mess to clean up.
M.R.Founder
B2B SaaSagent orchestration at scale
The MCP integration was the part three other vendors quoted us six months for. Here it was live in under three weeks — tool schema, OAuth, rate limits, traces, the lot. Our Claude agents finally touch real data safely.
PriyaVP Product
healthtech startupMCP integration
Book a short call if you have a product, workflow, or rebuild that needs senior delivery judgment and faster execution. Use contact if you already have a written scope.
Book a consultationOn greenfield work with clear scope, AI-accelerated development is typically 3-5x faster than traditional implementation. The gains are strongest on UI, CRUD, integrations, API wiring, tests, and iteration loops. Novel algorithms, messy migrations, and safety-critical decisions still need deliberate human time.
A senior engineer breaks the product into small reviewable tasks, sends bounded work to AI coding tools or agents, then reviews the resulting code for architecture, correctness, security, performance, and product fit. The workflow is faster because implementation is parallelized, not because review disappears.
Yes, when the process keeps human ownership over architecture, data access, auth, testing, and release decisions. It is not safe when teams paste AI output into production without review or let tools define the system boundaries.
Hire an AI developer when you want one senior owner who can scope, build, review, and ship. Hire an agency when the project requires a larger team, design production, or many parallel business workstreams. The right choice depends on ownership and coordination needs, not just budget.
AI consulting decides where AI belongs, what risks matter, and what should be built or bought. AI software development services turn that decision into working software: app routes, APIs, data models, agents, integrations, tests, and deployment.
A focused production MVP typically runs €8,000–€25,000 and ships in 3–6 weeks. A comparable traditional agency build is usually quoted at 3–6 months and two to four times the price, because implementation hours dominate the budget. AI acceleration compresses those implementation hours; senior review, architecture, and QA time stay in the quote because that is where production quality comes from. Fixed-price quotes follow a short scoping call.
Next.js (App Router) with TypeScript, Supabase for Postgres, auth, and storage, Tailwind for UI, and Fly.io or Vercel for deployment. Claude and Cursor are the primary AI coding tools, with MCP servers for agent integrations. The stack is deliberately standard and well-documented: AI tools generate reliable code against it, and any senior engineer can maintain the result without me.