SERVICE · MVP DEVELOPMENT
MVP Development
Turn an idea into a real, shippable, testable product in weeks—not quarters. One unified team carries it from strategy and design through development and deployment, shipping something you can see and use every single week.
From "let's prototype something" to "a real v1 you can actually ship"
Most teams stumble on their first version the same way. Requirements grow as you build and scope quietly spirals out of control. An agency hands back a pretty clickable mockup that turns out to be a throwaway prototype—nothing you can build on. Or design, frontend, backend, and ops live in separate teams, and the handoffs alone burn weeks while every conversation adds technical debt. By the time the product can finally demo, the market window and internal patience are usually gone.
Tenten AI works as Forward Deployed Engineers. We embed our team right next to your workflows and your data—one unified group with product strategy, design, and full-stack engineering under one roof, accountable for shipping from day one. We deliver a working MVP in roughly 6 weeks on average, with a verifiable iteration shipped every week. Not a progress bar—a real version you can click, put in front of real users, and take into a board or investor conversation.
We deliberately keep speed and quality on the same delivery line. Architecture, CI/CD, data models, and access control are built to a durable standard from week one, so the MVP isn't a demo you throw away—it's the first version of your actual product. Scope is driven by testable hypotheses: every feature maps to a business question you want to answer, and anything without a hypothesis behind it doesn't make this round. That's how we keep scope creep out.
Capabilities
01
One unified team, strategy to deployment
The same team owns product strategy, UX design, full-stack build, and cloud deployment—no cross-company, cross-team handoffs. Context and the reasoning behind every decision stay in one group's heads, which is why the team iterates an order of magnitude faster.
02
Verifiable weekly iterations
What ships each week isn't a doc or a status report—it's a deployed, clickable, testable version in a real environment. You and real users can try it and react that same week, collapsing the learning loop to its shortest possible length.
03
Hypothesis-driven scope control
Every feature is tied to an explicit business or user hypothesis with its own acceptance criteria. Anything without a hypothesis behind it gets pushed to the next round—cutting off scope creep at the source rather than fighting it later.
04
A durable engineering foundation, not a throwaway prototype
From day one we stand up CI/CD, strictly typed code, clean data models, and clear access boundaries. The MVP becomes the first version of your real product—no rip-and-rebuild after you find product-market fit.
05
AI-native architecture, ready by default
When the product needs LLM capabilities, we build RAG, Copilot, and agentic workflows on Anthropic, OpenAI, Azure, or AWS—with evals and guardrails designed in, not bolted on after the fact.
06
Delivered inside your cloud and compliance boundary
We can deploy into your VPC and align with SOC 2, GDPR, and your data-governance requirements, so sensitive data never leaves your environment and the MVP can enter regulated use cases from day one.
Use cases
B2B SaaS founder validating PMF
An early founder needs a real, usable product before the raise—something that shows core value to seed investors and first paying customers, not a Figma click-through. We deliver a shippable v1 in roughly 6 weeks on a code foundation you can keep building on.
Internal enterprise AI Copilot
A large enterprise wants an internal Copilot for support, compliance, or sales—RAG-connected to internal knowledge inside their own VPC and compliant with SOC 2 and data governance. We ship a version that works on real tickets and documents, with evals and guardrails built in.
Manufacturing digitization pilot
A manufacturer wants to validate whether a scheduling or quality-traceability workflow will actually get adopted on the floor before committing to a full MES integration. We stand up an MVP that runs on real line data, so the decision rests on shop-floor feedback rather than a slide deck.
Logistics and supply-chain visibility
A logistics operator needs a working visibility dashboard that connects to TMS/WMS and surfaces shipment status and exceptions in real time, to win over internal teams and customers. We build an operable version on real data fast, validating the metrics and workflow before a full rollout.
Compliance-first fintech product
A finance or payments team launching a new feature can't ship without KYC/AML and audit trails. We deliver a verifiable MVP inside the compliance boundary, building risk controls and data governance into the architecture from week one instead of patching them in before launch.
Fast validation of a new module on an existing product
A team with a live product wants to test a module for a new market or segment without slowing down the main roadmap. We deliver it as an independent but integrable build, shipping a usable version each week—and merge it back into the core product once it proves out.
Delivery cadence
WEEK 1
Strategy alignment and hypothesis definition
The embedded team lands, works with you to lock the core hypotheses, success metrics, and minimum scope, and stands up the architecture, data model, and CI/CD skeleton—a running backbone by the end of the week.
WEEK 2–4
Verifiable weekly iterations
Design and development move on the same line, shipping a version deployed to a real environment every week. Feedback from you and real users directly drives the next week's scope.
WEEK 5
Integration, hardening, and load testing
We converge the core flows, complete access control, evals, and guardrails, and validate stability and compliance boundaries under real data and load.
WEEK 6
Launch and handover
We deploy into your cloud or VPC and complete knowledge transfer and documentation—delivering a production v1 you can keep extending, not a prototype that needs rewriting.
~6 weeks
Average MVP delivery
1
Unified team, strategy to deploy
Weekly
Verifiable iteration cadence
FAQ
Will a 6-week build just be a throwaway prototype?
No. From week one we build the architecture, CI/CD, data models, and access control to a durable standard, with strictly typed code. The MVP is the first version of your real product's foundation—after you validate PMF, you build directly on top of it rather than starting over.
How do you control scope and avoid scope creep?
We drive scope by hypothesis: every feature has to map to a business or user question you want to answer, with clear acceptance criteria. Anything without a hypothesis behind it gets pushed to the next iteration. Because we ship something verifiable every week, scope discussions stay grounded in real feedback instead of ever-expanding wishlists.
If you move this fast, how is quality maintained?
The speed comes from a unified team eliminating handoffs, not from skipping engineering discipline. Design, development, and deployment sit on one line with no cross-team handoff losses, and CI/CD, automated tests, and code review are in place from week one. Deploying to a real environment weekly also means issues surface that same week, not at the very end.
We have security and compliance needs—can sensitive data stay in our own environment?
Yes. We support deployment into your VPC and align with SOC 2, GDPR, and similar requirements, so sensitive data never leaves your environment. When KYC/AML or audit trails are involved, we build those constraints into the architecture in week one rather than patching them in before launch.
What if the product needs AI / LLM capabilities?
We're an AI-native team. We build RAG, Copilot, and agentic workflows on Anthropic, OpenAI, Azure, or AWS, with evals and guardrails built in. AI is designed in at the architecture level rather than bolted on, so it stays measurable and tunable after launch.
What happens after the 6 weeks?
At delivery we complete knowledge transfer and documentation, so you can hand off to an internal team or keep the same embedded team driving the next phase. Because the foundation is built to last, neither path gets stuck on handoffs or rewrites.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.