AI Consulting Services
To build AI products and features that bring tangible business results, you need to start with a comprehensive tech strategy. MobiDev’s AI expertise is what you need to succeed in AI adoption.
Leverage AI Expertise to Drive Business Growth
AI Consulting Services
42% of leaders consider inadequate financial justification or business cases to be one of the main obstacles to AI implementation. Get AI consulting to find the right AI opportunities that align with your business.
AI Agents Consulting
Get comprehensive consultation on AI Agents development, from finding the best opportunity for your current business state to tech strategy.
AI Readiness Evaluation
Get a comprehensive evaluation of your AI adoption goals, available data infrastructure, and architecture, and an assessment of your system’s AI readiness.
AI Implementation Roadmap
Get a comprehensive AI product implementation roadmap based on your business goals, available data strategy, and infrastructure.
AI/ML Feasibility Assessment
Get the assessment of your AI implementation plan success, including the development of a POC when necessary.
Data Strategy
Get the evaluation of your data strategy and management to identify gaps for AI adoption and get a roadmap for the new strategy.
Why Choose MobiDev as Your AI Consulting Company
Since 2018
Providing AI consulting
& development services
65+
AI Consulting & Development Projects
10
Successful AI Agents Products since 2024
91%
Middle and senior engineers in the team
13%
Of all clients came through referrals from existing clients
5 years
Average Client Retention
9.1 NPS
As of the 2025 Client Survey
4.9
Client reviews on Clutch
Start Your AI Implementation with Consulting
Let’s talk!
Helen Khailova-Horash
Senior Solutions Manager
AI Agents
Build a custom AI Agent tailored for your use case and business objectives to automate repetitive tasks and boost business growth.
Demand Forecasting
Use AI algorithms to make data-driven forecasts about your future demands and sales, enabling you to optimize your planning.
Recommendation Systems
Deliver personalized product and content recommendations to elevate customer engagement, drive revenue growth, and nurture brand loyalty.
Human Pose Estimation
Apply advanced AI-powered human position and motion tracking to pioneer transformative solutions in fitness, security, and rehabilitation domains.
Biometric Authentication
Implement AI-enhanced biometric authentication to build resilient and secure solutions that mitigate the dangers of identity theft.
Virtual Try-Ons
Offer your customers effective try-before-you-buy solutions powered by AR and AI technologies to reduce return rates and enhance the shopping experience.
Defect Detection
Promptly uncover anomalies to continuously improve your product and operations with reliable defect detection solutions.
AI App Development
Turn your AI concept into a market-ready product with our seasoned engineering team. We have flexible cooperation options to cover your needs.
ARTIFICIAL INTELLIGENCE CONSULTING PROCESS AT MOBIDEV
01
Onboarding & Business Analysis
Identifying business goals and challenges that need to be addressed using AI.
02
Assessing Current Capabilities
Evaluating existing infrastructure, resources, and data availability to determine readiness for AI implementation.
03
Option Estimation Brainstorming
AI solution ideas taking into account functional, business, and marketing perspectives.
04
Data Understanding & Preparation
Identifying, collecting, and analyzing data sets that can help you reach your project goals. Preparing data sets for modeling.
05
Modeling & Evaluation
Selecting modeling techniques and architectures. Testing models for feasibility and performance with the given data.
06
Presentation
Presenting AI consulting results with recommendations for the next steps.
Get a Clear AI Plan
Before You Invest
Fill out the form and share your business challenge or AI idea. Our experts will get back to you within 1 business day.
Get Valuable AI Implementation Insights for Your Business
Planning an AI Product That Doesn’t Stall
It’s rarely about deciding to “add AI.” Successful AI products start with knowing which AI use case is worth pursuing, whether your data can support it, and what should be validated before development begins. We help companies turn AI ideas into clear product concepts, technical requirements, and realistic implementation plans.
The AI Product Strategy Has to Hold Up
If the strategy is weak, the project can fail before the first development sprint. We help you make the right decisions before investing in full-scale AI development.
Not every product needs a custom model, AI agent, or fine-tuning. Sometimes, a third-party AI API is enough. Sometimes the data is not ready. Sometimes the first version should be smaller, cheaper, and easier to validate.
Data defines what is realistic. If your data is incomplete, inconsistent, biased, or difficult to access, the AI feature may not perform as expected. We assess the data foundation and identify what must be improved before development.
Different AI use cases need different technical approaches. AI agents, demand forecasting, recommendation systems, pose estimation, biometric authentication, virtual try-ons, and defect detection all come with different requirements. We help define the right approach for your product scenario.
AI risks should be visible early. Accuracy, latency, privacy, security, hallucinations, model drift, and user trust can all affect product success. We help identify these risks before they become expensive development problems.
Making the AI Feature Useful for Real Users
An AI feature only matters if users understand it, trust it, and have a reason to keep using it.
AI should solve a specific product problem. It should improve decisions, reduce friction, personalize the experience, increase security, or unlock functionality users could not get before. We help connect AI use cases to measurable product value.
User trust has to be planned. If people do not understand why the system recommends something, rejects an action, flags a defect, or predicts an outcome, they may ignore it. We help define how the AI feature should behave inside the product experience.
The user flow matters as much as the model. AI output has to appear at the right moment, in the right format, and with the right level of control. We help define where AI belongs in the product workflow.
Planning the AI MVP Without Wasting Budget
The first AI version should not try to prove everything. It should answer the most important product and technical questions.
Scope needs planning. If the goal is to test whether AI recommendations improve engagement, start there. If the goal is to validate pose tracking accuracy, focus on that. We help narrow the first version to what actually needs to be proven.
A POC should answer a clear question. Can the model reach the required accuracy? Is the data good enough? Will users trust the output? Can the feature work inside the existing product? We help define validation criteria before development starts.
Model choice affects cost, speed, and risk. Custom AI solutions, open-source models, LLM APIs, computer vision models, and AI agents all have trade-offs. We help compare options and recommend the one that fits your goals, budget, and timeline.
Success metrics must be defined before implementation. The AI feature should be measured by product outcomes such as adoption, retention, conversion, reduced manual work, faster decisions, or improved user satisfaction.
What Happens After the AI Roadmap Is Clear
Once the AI concept, risks, data needs, and MVP scope are defined, development becomes much easier to plan. You know:
- What should be built first. Instead of starting with a broad AI vision, you get a focused implementation plan with clear priorities, dependencies, and risks.
- What should wait. Some advanced AI features make sense only after the first version proves value. We help separate must-have functionality from expensive ideas that should move to later phases.
- What the development team needs. Architecture requirements, data requirements, integration points, performance expectations, and success metrics become clearer before the build starts.
Then development can move with less uncertainty. Consulting helps reduce guesswork, avoid unnecessary features, and give the engineering team a practical plan for building an AI product that can reach production.
FAQ
Do we need a fully defined AI idea before starting consulting?No. A rough product idea, business problem, or feature direction is enough. AI consulting helps turn that early idea into a clearer product concept, use case, technical direction, and implementation plan.
Yes. Not every product problem needs AI. Sometimes a rule-based feature, third-party API, or simpler automation is the better first step. The goal is to find where AI creates real product value, not to force AI into the roadmap.
We review your current product workflows, user behavior, data, architecture, and business goals to find where AI can improve the experience. This may include recommendations, AI agents, forecasting, virtual try-on, defect detection, pose estimation, or another use case that fits the product.
That does not mean the project should stop. It means the first step is understanding what data is missing, inconsistent, inaccessible, or not useful for the target AI feature. You get a clearer view of what needs to be improved before development starts.
The right choice depends on your product goals, data, budget, timeline, accuracy requirements, privacy needs, and integration complexity. Consulting helps compare these options before you commit to an expensive development path.
Yes, mostly by preventing unnecessary development. A focused AI roadmap helps avoid building features users do not need, choosing the wrong model approach, or starting development before the data and technical requirements are clear.
Useful inputs include your product goals, current product workflow, available data sources, existing architecture, user pain points, business KPIs, and any AI ideas you already have. If some of this is missing, consulting can help define it.
An AI feature is worth building when it solves a clear product problem, can be supported by available or obtainable data, has realistic technical requirements, and can be measured through business or product outcomes like adoption, retention, conversion, accuracy, or reduced manual work.
Yes. A failed or stalled POC can still provide useful signals. We review the business case, data, model choice, product fit, user flow, technical risks, and implementation assumptions to decide whether the project should be rebuilt, refined, or stopped.
You should have a clearer implementation plan for development. This may include prioritized features, data requirements, architecture recommendations, integration points, MVP or POC scope, technical risks, and success metrics for the development team.
Not necessarily. Product owners, founders, CTOs, or technical managers can start the process. What matters most is access to product knowledge, business goals, and available data. AI consulting can also clarify what skills are needed later for development and maintenance.
The main risks are usually data quality, unclear business value, wrong model choice, privacy issues, poor UX, low model reliability, high infrastructure costs, and weak integration planning. Consulting makes these risks visible before they become expensive development problems.