Question 1

How long does it take to build an AI product?

Accepted Answer

Most of our AI engagements go from kickoff to production in 8 to 16 weeks depending on scope. We have shipped working AI prototypes in as little as 3 weeks for founders who needed to validate fast.

Question 2

Do you only build prototypes, or production systems?

Accepted Answer

We build production AI systems. Every engagement is architected for scale from day one with proper observability, fallback handling, latency optimization, and cost controls built in. We do not ship proof-of-concepts and walk away.

Question 3

What is a RAG system and does my product need one?

Accepted Answer

RAG (Retrieval-Augmented Generation) connects your private data — documents, databases, knowledge bases — to a language model so it answers questions using your content rather than generic training data. If your AI product needs to reference proprietary information accurately, you almost certainly need RAG. We have built RAG pipelines for legal document analysis, medical record summarisation, internal enterprise search, and customer support automation.

Question 4

What data do I need to get started with an AI project?

Accepted Answer

Less than you think. We can build effective AI products with structured CSVs, PDFs, existing databases, or even scraped content. We run a data audit in the first week to assess what you have, identify gaps, and design pipelines that work with your current reality — not an idealised future state.

Question 5

Can you build AI agents that take actions, not just answer questions?

Accepted Answer

Yes. We build autonomous agents using LangGraph and custom orchestration layers that can browse the web, call APIs, update databases, send emails, and trigger workflows without human intervention. We have shipped agents for lead qualification, contract review, inventory management, and customer onboarding automation.

Question 6

How do you control AI costs at scale?

Accepted Answer

Uncontrolled token spend is one of the most common ways AI products fail in production. We implement caching layers, prompt compression, model routing (smaller cheaper models for simple tasks, larger models only when needed), and real-time spend dashboards so you never face a surprise cloud bill.

Question 7

What is the difference between fine-tuning and RAG?

Accepted Answer

Fine-tuning bakes new knowledge into the model weights through additional training — expensive, slow to update, and best for consistent tone or task-specific behaviour. RAG retrieves fresh context at inference time — cheaper, instantly updatable, and better for factual accuracy over private data. Most production products we build use RAG first, and consider fine-tuning only for specific style or domain adaptation needs.

Question 8

How do you handle AI reliability and hallucinations?

Accepted Answer

We architect multiple layers of defence: structured output validation, confidence scoring, retrieval grounding, fallback logic when confidence is low, and human-in-the-loop checkpoints for high-stakes decisions. Every AI system we ship includes an observability stack so you can monitor accuracy, latency, and drift from day one.

Question 9

Can you help us rescue a failing AI project?

Accepted Answer

Yes, and we do this more often than you might expect. If you have an AI prototype that is not scaling, hallucinating too often, costing too much to run, or simply stuck — we offer a two-week AI audit that diagnoses root causes and produces a clear remediation plan. Many of our best long-term clients came to us this way.

Question 10

What industries do you build AI products for?

Accepted Answer

We have shipped AI products across Healthcare, FinTech, InsurTech, LegalTech, PropTech, E-commerce, Food Delivery, and Logistics. The domain changes, the engineering standards do not.

Question 11

How do you choose which AI model is right for our product?

Accepted Answer

Model selection is an engineering decision, not a branding one. We evaluate latency requirements, context window needs, cost per token at your expected volume, accuracy benchmarks on your specific task type, and whether the model can be self-hosted for privacy reasons. For most production products we build, the right answer is a combination: a capable model for complex reasoning tasks, a faster and cheaper model for routine classification or generation, and routing logic that sends each request to the right model automatically.

Question 12

Can you add AI features to our existing product, or do you only build from scratch?

Accepted Answer

Adding AI to an existing product is one of our most common engagements. We integrate AI capabilities directly into your current architecture without requiring a rewrite. Whether you need a semantic search layer on top of your existing database, an AI assistant embedded in your SaaS dashboard, or an automated workflow replacing a manual process, we design the integration to fit your existing system rather than requiring you to rebuild around us.

Question 13

How do you handle data privacy and GDPR compliance in AI systems?

Accepted Answer

Privacy is architected in from the start, not bolted on at the end. We implement data minimisation at the prompt level so only necessary information reaches the model, user data anonymisation pipelines for training and evaluation, opt-out and deletion mechanisms that satisfy GDPR Article 17, and audit logging of all AI interactions. For products handling particularly sensitive data we design for on-premise or private cloud model deployment so data never leaves your infrastructure.

Question 14

What is the difference between AI consulting and what MarsDevs does?

Accepted Answer

AI consultants deliver strategy documents and architecture recommendations. We deliver working AI systems in production. The difference matters because most AI products fail not in the strategy phase but in the implementation phase, where demos fall apart under real user behaviour, latency becomes unacceptable at scale, and hallucination rates that seemed acceptable in testing cause real problems with real users. We solve those problems because we own them.

Question 15

Can you build AI that works in multiple languages?

Accepted Answer

Yes. The leading foundation models have strong multilingual capability, and we have built AI products serving users across Arabic, French, Spanish, German, Hindi, and several other languages. For products with strict multilingual accuracy requirements we implement language detection, language-specific prompt strategies, and human evaluation pipelines in each target language. We have specific experience building AI products for the Middle-East market where Arabic language quality and right-to-left UI considerations are both critical.

Question 16

How do you measure AI accuracy and performance in production?

Accepted Answer

We build an evaluation framework before we build the product. This includes an offline golden dataset for automated regression testing, online quality metrics tracked per production request, human evaluation sampling for tasks where automated metrics are insufficient, and drift detection that alerts the team when accuracy degrades after a model update. Most AI products we inherit have no evaluation framework at all, which is why no one notices when quality deteriorates.

Question 17

What is vector search and do we need it?

Accepted Answer

Vector search allows your AI to find semantically similar content rather than exact keyword matches. If your product needs to answer questions over a large document corpus, find similar products or cases, or retrieve relevant context for an AI to reason over, you almost certainly need it. We have built vector search infrastructure for legal document retrieval, medical literature search, product recommendation, and enterprise knowledge bases. It is a foundational component of most production RAG systems.

Question 18

Can AI meaningfully reduce our customer support costs?

Accepted Answer

Yes, and this is one of the clearest ROI cases for AI. We have built AI support systems that resolve 60 to 80 percent of inbound queries automatically with accuracy rates that match or exceed human agents on routine issues. The key is building with proper escalation logic, confidence thresholds, and human handoff so the AI handles what it is good at and routes everything else appropriately. The teams we have worked with typically see support cost reduction within 90 days of deployment.

Question 19

How do you handle AI model deprecations and version updates?

Accepted Answer

Model deprecations are one of the biggest hidden risks in AI products, and most teams are not prepared for them. We build abstraction layers between your product logic and the model provider so switching models requires a configuration change and an evaluation run, not a codebase rewrite. We also monitor provider deprecation announcements and proactively test replacements before the deadline so you are never forced into a rushed migration.

Question 20

We have no existing data. Can we still build an AI product?

Accepted Answer

Yes. Most AI products we build do not require proprietary training data. Foundation models already contain vast general knowledge. What your product needs is the right retrieval strategy, prompt design, and integration architecture, not a custom training dataset. Where proprietary data genuinely helps, we design data collection pipelines into the product from day one so you accumulate a valuable dataset as your product grows. Starting with no data is not a blocker. It is just a different starting point.

Question 21

How is AI development priced?

Accepted Answer

We price AI engagements based on scope and complexity, not on hours. A focused AI feature integration into an existing product typically runs over 6 to 8 weeks. A full AI product build from scratch runs 12 to 20 weeks depending on the number of AI capabilities, data pipeline complexity, and integration requirements. We quote fixed-scope engagements for well-defined projects and time-and-material for exploratory or evolving scopes. All engagements include a free 30-minute scoping call before any commitment.

Question 22

What is the difference between a chatbot and a real AI product?

Accepted Answer

A chatbot follows scripted decision trees. An AI product reasons over context, retrieves relevant information, takes actions, and handles inputs it has never seen before. The difference is not cosmetic. A chatbot breaks the moment a user phrases something unexpectedly. A well-built AI product handles ambiguity, asks clarifying questions, and degrades gracefully when it is uncertain. We do not build chatbots. We build AI systems that behave intelligently under real-world conditions.

Question 23

How do you keep AI responses consistent and on-brand?

Accepted Answer

Consistency in AI responses requires system prompt engineering, output formatting constraints, tone and style guidelines baked into the prompt architecture, and evaluation pipelines that test for off-brand responses automatically. We also implement response templating for structured outputs and guardrails that block certain response patterns entirely. For products where brand voice is critical, we build a dedicated evaluation suite with human reviewers sampling production responses weekly and feeding corrections back into the prompt system.

Question 24

Can you build AI that integrates with our existing databases and internal tools?

Accepted Answer

Yes, this is standard in our engagements. We connect AI systems to your databases, CRMs, ERPs, internal APIs, and third-party services through function calling and tool use patterns. This means your AI can look up real customer data, create records, send notifications, and trigger workflows rather than just generating text. We have integrated AI products with Salesforce, HubSpot, Notion, Slack, Jira, and custom internal systems across many different industries.

Question 25

How do you handle sensitive or regulated data in AI pipelines?

Accepted Answer

For products in regulated industries we implement several layers of protection: data anonymisation before any external model call, private deployment options where models run in your own cloud account, strict input and output filtering, comprehensive audit logging that satisfies regulatory requirements, and data residency controls that keep data in specific geographic regions. We have built AI systems compliant with HIPAA, GDPR, and financial services regulations. Compliance is designed in from architecture, not added after the product ships.

Build Cutting-Edge AI Products That Scale, Not Stall.

We build AI products that ship to production, not slide decks.

Large Language Model Integration

Retrieval-Augmented Generation (RAG)

Multi-Modal AI Systems

AI Copilots and Autonomous Agents

AI Automation and Workflow Intelligence

AI Product Development (End-to-End)

AI and Multi-modal Development

AI and Multi-modal Development

The AI and Multi-modal Development That Ships Products, Not Promises.

RAG and Knowledge Systems

MLOps and AI Infrastructure

Data Pipeline Engineering

Multimodal AI Systems

LLM Workflow Automation

AI Agent Development

Built With Best-in-Class Tools Across Every Layer

Real founders, real teams, real outcomes

Healthcare SaaS Platform

Founders Across Industries Trust MarsDevs

Numbers That Prove We Deliver

80%

25+

$50M+

3x

290,000

40%

60+

98%

5.4 Billion

The AI and Multi-modal Development Partner That Ships Products, Not Presentations

Senior Engineers With Product Instinct

A Partner, Not a Vendor

Predictable Delivery, Every Time

Built for Scale From Day One

2019

60 +

80 +

75000 +

AI Delivery Process

How We Build AI Products That Actually Work

AI Delivery Process

How We Build AI Products That Actually Work

Built Products Across Industries That Matter

Health Care

Fintech

Travel

E-Commerce

Restaurant

ED Tech

Social Media

Social Impact

Food & Beverages

Sports

Questions, meet answers.

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