Speed with sound judgment
We prioritize a usable first version with a basic architecture, access controls and agreed acceptance criteria.
Deskrive AI · Accelerated MVP
From an idea to an AI MVP you can put to the test
We build minimum viable products with artificial intelligence designed in from the start: a usable first version to test value with customers or your team. We define the problem, scope and success criteria so you can learn before investing in the next stage.
- A new product or module with AI in its first release.
- A focused scope and a schedule agreed during discovery.
- Validation with real users before expanding development.
Define · Build · Validate
An AI MVP is a product that solves a problem
We start with a business need and define the smallest version that lets you test an idea. We add assistants, document search or automation where they deliver value, with an experience people can use.
We apply the product discipline of Deskrive Labs: focused discovery, defined scope, clear deliverables and a path to connect the MVP to other systems, support it or develop the full product.
AI where it adds value
We use AI to assist, generate, classify, search or automate when it helps test the hypothesis. We choose conventional software when it handles the workflow better.
Validation through evidence
We agree on how to observe usage, completed tasks, response quality and operating costs to decide what comes next.
Control over your product
We hand over the code, instructions and configurations developed for your project. We define accounts, licenses and third party dependencies from the start.
International reach, real operations
We work with startups and established companies that need to validate products for customers or internal teams, with clear scope and responsibilities at every stage.
What an accelerated AI MVP covers
We define the first version’s deliverables together. Final coverage depends on the pilot’s workflows, data and users.
Product discovery
We clarify the problem, users and opportunity for AI. We define the hypothesis and success measures.
First release scope
We prioritize essential workflows and state what is included in the MVP and what belongs in later stages.
Experience design
We design the screens and journeys needed to use the product, including errors, empty states and steps that require human input.
Architecture and providers
We select technologies and existing models for the use case. We agree on initial data, permissions, cost limits and separation between the application and AI layer.
MVP development
We build the interface, backend, user access and agreed AI features: assistants, generation, search or automation.
Initial data and search index
Where needed, we prepare a focused set of documents, FAQs or catalog data. Retrieval augmented generation (RAG) lets the product generate answers using those sources.
Testing and refinement
We validate workflows with users or your team, review sample responses and refine instructions and the experience within the agreed scope.
Launch and documentation
We deploy to the agreed environment, hand over access and an operating guide, and document recommendations for the next stage.
How we build your AI MVP
A process focused on reaching a first version you can use and evaluate.
- 01
Discovery
Problem, users, hypothesis, risks and criteria for considering the MVP ready.
- 02
Scope design
Included workflows, technologies, providers, metrics and validation plan.
- 03
Core development
Building the application and the AI capabilities within the agreed scope.
- 04
Data and refinement
Initial sources, instructions, user experience, error handling and costs.
- 05
Validation
Testing with users or the internal team and a round of adjustments under the agreed plan.
- 06
Delivery and next stage
Controlled launch, documentation and a path to a full product, Integration or Care.
We start with the business outcome: finding information, handling requests or making a task easier. If the hypothesis needs more definition, we flag that before building.
Common AI MVP scenarios
Different ways to test an opportunity with a product of defined scope.
Product assistant
For: startups or innovation teams that need to validate a conversational product.
May include: a web application, chat or agent, rules for transferring to a person, user access and a basic dashboard.
Internal knowledge tool
For: teams that need to search documents, manuals or knowledge bases.
May include: an initial index, simple permissions, answers with references and basic source management.
Process automation
For: a specific workflow for request intake, classification, drafts or task assignment.
May include: an interface, automation logic, activity logs and human review at agreed steps.
AI module in a new product
For: projects where AI is one capability within a broader application.
May include: core product features and the AI layer needed to test the hypothesis.
B2B customer pilot
For: companies that want to present and test a solution with a small group of customers.
May include: a stable pilot environment, basic branding, usage metrics and a feedback plan.
These scenarios illustrate possible coverage. Deliverables and limits are agreed in the proposal.
Measure what matters in an MVP
We choose indicators that help you decide whether to refine, expand or rethink the product. Depending on scope, these may include:
- Real usage: sessions, active users and completed tasks.
- AI quality: feedback, evaluated samples and incorrect answers detected.
- Cost per user, task or period.
- Time to the first usable version and validation with users.
- Findings that change priorities for the next version.
- Data and security risks identified during the pilot.
A good fit for companies that…
- Have a clear problem and need a first version to test their idea.
- Want to validate AI value with users before increasing investment.
- Are creating a new product or module with AI built into its design.
- Need a partner that combines product thinking, engineering and judgment in applying AI.
- Can prioritize what goes into the MVP and what comes later.
- Need collaboration and clear delivery responsibilities wherever their business operates.
What we need to get started
Access, environments and data samples are requested at project kickoff.
- 01
The problem or hypothesis: who needs to solve what.
- 02
The target user: internal teams, businesses or consumers.
- 03
The result that would make the pilot successful.
- 04
Whether you already have a brand, data, documents, design or prototype.
An MVP today; evolution and support when needed
We define a path to the next stage. Complementary services are quoted according to the product’s needs.
AI Integration
When the MVP needs to connect to a CRM, store, ERP, support system or other platforms already in operation.
Explore serviceDeskrive Labs
To evolve the product concept and develop new versions beyond the first validation.
Explore serviceWeb systems and application development
When the product needs more features, platforms or an architecture prepared for its next stage of growth.
Explore serviceDeskrive eCommerce
If a store will be your validation channel, we define its development as a complementary scope.
Explore serviceDeskrive Care
After launch, monitoring, adjustments and ongoing support can be contracted separately, with agreed coverage and service hours.
Explore servicePrototypes and specialized tools
If you already have an unstable prototype, we first assess its condition and whether it needs stabilization. Intercom, ElevenLabs or WhatsApp can be part of the MVP when the scope calls for them.
AI Code Rescue
If your current prototype is unstable, we assess what to keep, fix or rebuild before expanding the product.
Explore serviceLet’s talk about your MVP
Tell us about the AI MVP you want to validate
Describe the problem, the user and what you would like to test. We will propose the scope, technologies and schedule for a first version.
A tailored quote, with deliverables and limits agreed before starting.
Frequently asked questions
How is an AI MVP priced?
We prepare a tailored quote based on workflows, data, channels and access requirements. We agree on deliverables, limits and external service costs before starting.
How does this differ from AI Integration?
With an accelerated MVP, we build a new product or module with AI. With Integration, we connect AI capabilities to systems already in operation. A project can start with an MVP and continue with Integration.
How long does it take?
The schedule depends on scope, available data and planned validation. We define it during discovery and include it in the proposal, prioritizing a focused first version.
Does it include training a custom model?
By default, we use existing models through their APIs. If the use case requires tuning or training a model, we assess the data and quote that work separately.
Is it ready for production?
It is ready to validate and operate within the agreed pilot scope. More users, advanced compliance requirements or additional connections may need a second phase.
Do you work with startups and established companies?
Yes. In both cases, we need a clear problem, a defined scope and someone on the client side who can make decisions and coordinate validation.
What if I already have a prototype built with AI?
It can serve as a reference. We review its condition to decide whether to reuse it, stabilize it or build a new foundation. That work is scoped before development begins.