Inventiple builds AI MVPs for funded founders who need real, scalable products — not throwaway prototypes. Every line of code is written or reviewed by a senior engineer averaging 10+ years of production experience. We use Cursor, Claude, and our own internal AI workflows to deliver in 6–8 weeks what most agencies take 4–6 months to ship.
You've raised pre-seed or seed funding. You have 12–18 months of runway, a sharp product hypothesis, and pressure from investors and the market to ship something real, fast. So you go looking for a development partner — and almost every option is bad in a specific way.
Traditional agencies quote 4–6 months and stuff the team with juniors. You pay senior rates for engineers who've never shipped production AI. The architecture they pick (because they don't know any better) won't survive 1,000 users, let alone 100,000. Six months in, you're rewriting their work.
Freelance marketplaces are a coordination nightmare. You spend more time managing three contractors across three time zones than building product. Quality varies wildly. Nobody owns the architecture. When something breaks at 2 AM, nobody answers.
Offshore body shops sell hours, not outcomes. You get a project manager between you and the people writing code, weekly status decks that hide problems, and a team that scales up and down based on their internal margin needs, not yours.
AI specifically makes all of this worse. In 2026, every agency claims AI expertise. Very few have shipped production agentic systems, hardened a RAG pipeline against hallucination, or architected an MCP server that integrates safely with internal data. The gap between "we know LLMs" and "we've made AI features that don't embarrass our client at scale" is enormous.
The result, for most founders: 6 months gone, $200K spent, a product that either doesn't work or is architecturally a dead end. We've inherited dozens of these. We exist because the model is broken.
Inventiple is structured around a single conviction: a small team of senior engineers, equipped with modern AI tooling, ships faster and better than a large team of mid-level developers. Everything about our process follows from that.
Every engineer on your project has 7+ years of production experience. Our average is 10+. We deliberately don't run a junior bench. There are no handoffs to less experienced developers, no "we'll have the seniors review it later," no surprise team swaps mid-project. You meet your team in discovery and they ship your product.
We use Cursor, Claude Code, and our own internal agentic tooling as multipliers on senior judgment. A senior engineer with AI-augmented workflows ships roughly 3x faster than they did in 2023, with tighter code quality because the AI catches obvious classes of error before the PR opens. This is the structural reason our 6–8 week timelines are real, not aspirational.
Week one is architecture: data model, API surface, agent topology, security model, observability, and deployment infrastructure. We deliver a written architecture doc you can show to your CTO or your investors. We don't start writing application code until the foundation is sound, because ripping out bad architectural choices at week six is what kills MVPs.
We quote a single number for the engagement, agreed before kickoff. We don't bill hourly. We don't surprise you with change orders. If we underestimate, that's on us — we eat the cost. This forces us to be honest about scope upfront and aggressive about execution, both of which serve you.
Every Friday you see a live demo of the week's work in your environment. You have direct Slack or Teams access to your engineers — not to a project manager who relays messages. The repository is in your GitHub from day one. You see every commit, every PR, every issue.
Here's exactly how a standard AI MVP engagement runs. We adjust based on scope, but this is the backbone. The numbers in parentheses are typical hours of senior engineering effort.
Two to three working sessions, usually over one calendar week. Output: a written scope document, a fixed-price quote, and an architecture sketch. You're free to walk away here at no cost. (10–15 hours)
Data model, auth, multi-tenancy, deployment infrastructure, observability, repo setup, CI/CD, and the AI infrastructure choices (LLM provider, vector DB, agent framework). End of week: working dev environment, deployable skeleton. (60–80 hours)
Application APIs, business logic, data ingestion, and the non-AI surface area of the product. This is the boring-but-critical layer that makes everything else possible. End of week 3: usable internal product, no UI polish yet. (140–180 hours)
The agents, RAG pipelines, prompt engineering, evaluation harnesses, guardrails, and any MCP integrations. We build for production from day one — including observability, cost tracking, and fallback behavior when models fail. (140–180 hours)
Final design pass, micro-interactions, mobile responsiveness, payment integration if relevant, transactional emails, onboarding flow. End of week: feature-complete product. (60–80 hours)
End-to-end testing, load testing, security review, AI behavior evaluation runs, monitoring setup, alerting, runbook documentation. We deliberately allocate a full week to this because most agencies skip it and you find out in week 10 of production. (50–70 hours)
Production deploy, DNS cutover, monitoring live, documentation review, knowledge transfer to your team. 30 days of free post-launch support included. (30–40 hours)
Most agencies hide their pricing behind a discovery call. We don't. Here's what an AI MVP from Inventiple actually costs in 2026.
Single core AI feature, basic auth, web-only, one integration.
Multi-feature product, real users from day one, 2–3 integrations.
Agentic workflows, MCP servers, compliance (HIPAA/SOC 2), or enterprise customer requirements.
Payment terms: 40% on kickoff, 30% at mid-engagement demo, 30% on production launch. We invoice in USD, EUR, or GBP. We've never had a payment dispute in 5 years; we'd rather under-promise and ship.
We pick boring, proven technology for the layers that need to be reliable, and aggressive new technology for the layers where capability matters more than maturity. This is intentional.
A typical 6–8 week AI MVP from Inventiple ranges from $40,000 to $70,000 fixed price. Complex MVPs with custom agentic workflows, multi-model orchestration, or strict compliance requirements (HIPAA, SOC 2) range from $70,000 to $120,000. For comparison, traditional dev agencies usually quote $150,000–$300,000 for the same scope over 4–6 months. Our cost calculator gives you an instant range based on your specific scope.
A production AI product with: authentication, multi-tenant data model, payment integration, a polished React or Next.js front end, AI features powered by GPT, Claude, or open-source models, a RAG pipeline if knowledge retrieval is needed, an agentic workflow if multi-step automation is needed, observability and guardrails, and deploy infrastructure on AWS, Vercel, or your cloud of choice. What we don't ship in 8 weeks: ML model training from scratch, native mobile apps requiring App Store and Play Store review cycles, or 50-feature kitchen-sink products. We're aggressive about scope, which is why we hit the timeline.
Yes, all three are core to what we ship. Most of our 2026 engagements include at least one agentic component using CrewAI, LangGraph, or custom orchestration. We've shipped Model Context Protocol (MCP) servers that let LLMs access internal databases and APIs securely. RAG pipelines with vector databases (Pinecone, Weaviate, pgvector) and hybrid search are standard in any MVP that needs domain knowledge.
100% senior engineers with an average of 10+ years of production experience. We don't have a junior bench, we don't offshore handoffs, and we don't use overseas subcontractors. Every commit is authored or reviewed by a senior engineer. You meet your team on day one and they stay with the project through delivery.
If discovery surfaces scope that genuinely needs 10–12 weeks, we'll tell you upfront and quote it that way. We won't squeeze a 12-week project into an 8-week timeline. For larger, ongoing engagements, we offer dedicated senior pods on monthly retainer after the MVP ships — typical clients spend $25,000–$60,000 per month for a continued 2–4 person team.
You own 100% of the code, the IP, the AWS or Vercel account, the database, the domain, and all third-party credentials from day one. The repository is in your GitHub or GitLab organization. We're contractors building your asset, not a SaaS holding your product hostage.
Our engineering team is distributed across Bangalore (HQ), Houston, and Frankfurt. This gives you near 24-hour development coverage and full overlap with US Eastern, UK, EU, and APAC business hours. You always have a senior engineer available during your working day.
Yes. Most of our funded-startup clients are in stealth or semi-stealth when we start. We sign mutual NDAs before discovery, and we've never named a client publicly without explicit written approval. Roughly 40% of our case studies remain private at the client's request.
Both. About 60% of our work is greenfield MVPs from a blank repo. The remaining 40% is layering AI features onto an existing product, rebuilding a brittle prototype into something production-ready, or unblocking a stuck internal team. For brownfield work we start with a one-week paid technical audit before scoping the build.
Three options. (1) You take it in-house — we hand off complete documentation, runbooks, and 30 days of free post-launch support to your team. (2) You retain us on a monthly engineering retainer for continued feature work and operations. (3) Hybrid — we stay on for a fixed 90-day post-launch phase while you hire your first engineers, then transition. About half our MVP clients move to option 2 or 3 because shipping speed matters in the first 6 months after launch.