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Generative AI Development Services

Your product. Your data. Your competitive advantage β€” powered by generative AI.

GMTA Software builds production-ready generative AI solutions for startups and enterprises β€” custom | that reduce costs, accelerate decisions, and open entirely new revenue streams.

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Generative AI

What Are Generative AI Development Services?

Generative AI development services cover the end-to-end process of designing, building, fine-tuning, and deploying AI systems that can create original content β€” text, code, images, video, audio, and structured data β€” based on patterns learned from large datasets.

Unlike traditional software that follows hard-coded rules, generative AI models (GPT-4o, Claude, Gemini, Llama, Mistral, Stable Diffusion, and others) generate contextually intelligent outputs that adapt in real time. When a specialist generative AI development company like GMTA integrates these capabilities into your existing product or workflow, the result is a system that works smarter, scales faster, and compounds its value the longer it runs.

Founders and CTOs come to GMTA when they need to move from a vague 'we should add AI' mandate to a shipped, measurable product β€” on time and on budget.

30+

Countries Served

7+

Years Of Experience

80+

Clients Worldwide

200+

Successful Projects

40+

Industries covered

50+

Dedicated Professionals

Our Generative AI Development Services

We offer a full spectrum of custom generative AI development services β€” from early-stage consulting all the way through to ongoing maintenance. Every engagement is scoped to your business goals, not a template.

Generative AI Consulting 01

Custom Generative AI 02

LLM Fine-Tuning & Custom Model 03

Retrieval-Augmented Generation 04

AI Agent & Agentic Workflow 05

Generative AI Integration 06

Domain-Specific & Enterprise Generative 07

Generative AI Support & Maintenance 08

Generative AI Consulting

Generative AI Consulting & Strategy

Not sure where to start? Our AI strategists map your workflows, identify the highest-ROI use cases, assess build vs. buy, and deliver a practical AI roadmap – so you invest in what moves the needle, not what's trending. Covers use-case scoring, model selection, data readiness, compliance risk, and 90-day implementation priorities.

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Unlock the Power of Generative AI

Get in touch with us right now to see how our services for developing generative AI can revolutionize your industry.

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Generative AI

Foundation Models & AI Technologies We Work With

We are model-agnostic by design. Our team evaluates the best foundation model for your use case, data type, latency requirement, cost envelope, and compliance constraints β€” then builds around it.

Large Language Models (LLMs) Large Language Models (LLMs)

Large Language Models (LLMs)

GPT-4o, GPT-4 Turbo, Claude 3 Opus/Sonnet, Google Gemini 1.5 Pro, Meta Llama 3, Mistral Large, Mixtral, Command R+, Falcon β€” selected based on performance, cost per token, and deployment flexibility.

Image & Multimodal Image & Multimodal

Image & Multimodal Generation

DALL-E 3, Stable Diffusion XL, Midjourney API, Adobe Firefly, Flux, ControlNet β€” for product imagery, design automation, document understanding, and vision-enabled AI systems.

Speech & Audio Speech & Audio

Speech & Audio AI

OpenAI Whisper, ElevenLabs, AssemblyAI, Azure Speech β€” for voice-to-text, AI call analysis, real-time transcription, and text-to-speech features embedded in your product.

Embedding & Vector Embedding & Vector

Embedding & Vector Search

OpenAI Ada embeddings, Cohere Embed, Sentence Transformers β€” paired with vector databases (Pinecone, Weaviate, Qdrant, Chroma, pgvector) for semantic search and RAG applications.

AI Agent Frameworks AI Agent Frameworks

AI Agent Frameworks

LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel β€” for building multi-step, tool-using AI agents that automate complex workflows end-to-end.

stable-diffusion Model Serving & MLOps

Model Serving & MLOps

AWS Bedrock, Google Vertex AI, Azure OpenAI, vLLM, BentoML, HuggingFace Inference Endpoints, Triton Inference Server β€” for reliable, scalable, cost-optimized model deployment.

Generative AI Use Cases Across Industries

Generative AI delivers measurable impact across verticals when it is applied to the right problem. Below are the most common high-value use cases GMTA has built or is actively deploying for clients in 2025–2026.

Healthcare & MedTech

Clinical documentation automation (SOAP notes, discharge summaries), patient intake chatbots, medical image report generation, drug interaction Q&A systems, and revenue cycle automation. HIPAA-compliant architectures.

Fintech & Banking (BFSI)

AI-generated credit memos, regulatory document summarisation, fraud pattern narration, personalized financial advice bots, automated KYC/AML report drafting, and earnings call analysis.

E-Commerce & Retail

AI product description generation at scale, personalized shopping assistants, review analysis and response automation, dynamic pricing narration, visual search, trend forecasting reports.

Legal & Compliance

Contract review and clause extraction, legal research assistants, policy summarisation, deposition preparation tools, and regulatory change monitoring with natural language alerts.

SaaS & Tech Products

AI copilots embedded in B2B SaaS products, code generation and review features, automated onboarding flows, smart in-app search, and user behaviour narrative reporting.

Logistics & Supply Chain

Intelligent exception management (delay narration and suggested actions), carrier communication automation, document extraction from shipping manifests, and demand forecasting commentary.

Education & EdTech

Personalized learning path generation, automated quiz and assignment creation, AI tutors aligned to curriculum, grading assistance, accessibility features (alt-text, audio description).

On-Demand & Marketplace

AI-powered listing optimization, dynamic service descriptions, intelligent matching explanations, review moderation, and multi-language localization of platform content at scale.

Why Choose GMTA as Your Generative AI Development Company?

There is no shortage of companies calling themselves an 'AI development company'. Here is what actually differentiates GMTA when a founder or CTO is evaluating partners:

Outcome-First Scoping

Every engagement starts with a business outcome β€” reduce support tickets by 40%, cut document review time in half, or launch an AI product feature in 10 weeks. We scope and price the outcome, not the activity.

Full-Stack AI Capability

We handle every layer: data pipelines, model selection, fine-tuning, application development, deployment, and monitoring. No coordination overhead across three vendors.

Flexible Engagement Models

Fixed-price projects for well-defined scopes. Time and materials for evolving research projects. Dedicated team augmentation for long-term product development. You choose what fits your risk tolerance.

IP Ownership β€” 100%

All custom code, trained models, and data pipelines we build for you belong to you. Full IP transfer, NDAs signed before day one, and your choice of deployment environment.

Transparent Communication

Weekly progress reports, a dedicated project manager in your time zone, and access to your development dashboard at all times. No surprises at the end of a sprint.

Proven Track Record

350+ software and AI projects delivered across healthcare, fintech, logistics, retail, and SaaS. Recognized on Clutch, GoodFirms, and DesignRush. References available on request.

Security & Compliance-Ready

We deploy AI in environments where data privacy is non-negotiable. On-premise deployment, private cloud, VPC isolation, data anonymization pipelines, and audit trails are standard options.

6 Months Free Post-Launch Maintenance

Every engagement includes six months of free post-deployment support. We do not disappear after launch β€” we monitor, fix, and iterate so your AI solution compounds in value.

Discover AI Innovation

Make use of our knowledge to incorporate generative AI into your digital goods, improving user experiences and promoting corporate expansion.

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AI Innovation

Engagement Models β€” How We Work With You

Whether you are a founder testing an AI hypothesis or an enterprise deploying at scale, we have a commercial model that fits.

Time & Materials

Time & Materials

Best for: research-heavy or evolving-scope projects. You pay for actual hours at agreed rates. Maximum flexibility for early-stage exploration, complex data science work, or when requirements are expected to change.

Dedicated AI Development Team

Dedicated AI Development Team

Best for: product companies and enterprises that need ongoing AI capacity. We assemble a dedicated team β€” AI engineers, ML scientists, data engineers, QA β€” embedded in your workflow, using your tools, aligned to your roadmap.

AI Consulting Retainer

AI Consulting Retainer

Best for: organizations that need ongoing AI strategic guidance without full development commitment. Monthly access to a senior AI architect for architecture reviews, model selection, vendor evaluation, and team upskilling.

Our Generative AI Development Process

We follow a structured, milestone-driven process designed to minimize risk, surface issues early, and keep you informed at every stage. Here is how a typical custom generative AI development project moves from idea to production.

01
Week 1–2

Discovery & Requirements Workshop

We conduct structured discovery sessions with your team to map business objectives, current workflows, data sources, user personas, success metrics, and compliance constraints. Output: a signed-off requirements document and project scope.

Output: Signed-off requirements document and project scope
02
Week 2–3

AI Strategy & Architecture Design

Our architects design the AI system blueprint β€” model selection rationale, retrieval strategy, data flow diagrams, integration points with existing systems, infrastructure plan, and cost projection. Output: Technical Architecture Document (TAD).

Output: Technical Architecture Document (TAD)
03
Week 3–5

Data Assessment & Pipeline Setup

We audit your existing datasets for quality, volume, and coverage. Where gaps exist, we design data collection or augmentation strategies. We build preprocessing pipelines and set up vector stores or training infrastructure. Output: production-ready data pipeline.

Output: Production-ready data pipeline
04
Week 4–8

Model Selection, Fine-Tuning & Prototyping

We evaluate candidate foundation models against your benchmarks, run baseline tests, and begin fine-tuning or prompt engineering. A working prototype is delivered for stakeholder review at the end of this phase. Output: functional AI prototype.

Output: Functional AI prototype
05
Week 6–12

Application Development & Integration

The AI layer is integrated into your product or platform β€” frontend, backend, APIs, and databases. We build user-facing features, admin controls, logging, and access management. Weekly demos keep stakeholders aligned. Output: feature-complete application.

Output: Feature-complete application
06
Week 10–14

Testing, Safety Evaluation & QA

We run functional testing, accuracy benchmarking against defined KPIs, adversarial red-teaming, bias evaluation, performance load testing, and security review. Issues are fixed before production release. Output: QA-signed application ready for deployment.

Output: QA-signed application ready for deployment
07
Week 13–16

Deployment & Production Launch

We deploy to your preferred environment β€” AWS, GCP, Azure, on-premise, or private cloud β€” using CI/CD pipelines. We configure monitoring dashboards, alerting, and fallback mechanisms. Output: live production system.

Output: Live production system
08
Post-Launch

Monitoring, Optimization & Ongoing Support

We monitor model performance, track cost per inference, manage prompt drift, refresh training data, and deliver continuous improvements. Your 6-month free maintenance window begins at go-live. Output: continuously improving AI system.

Output: Continuously improving AI system

Our AI Tech Stack

We select tools based on what your project requires β€” not what is trending. This is the AI-specific stack our team works with daily.

Foundation Models & APIs
GPT-4oo1Claude 3GeminiLlama 3MistralCohereAWS BedrockAzure OpenAIVertex AI
Open-Source Models
Llama 3 (8B/70B)Mistral 7BMixtral 8x7BFalconPhi-3QwenDeepSeekSDXLFlux
AI/ML Frameworks
PyTorchTensorFlowHuggingFaceAcceleratePEFTTRLDeepSpeedAxolotl
Orchestration & Agents
LangChainLangGraphLlamaIndexAutoGenCrewAISemantic KernelHaystack
Vector Databases
PineconeWeaviateChromaQdrantpgvectorMilvusFaiss
Cloud & Infrastructure
AWS SageMakerGCP Vertex AIAzure AKSPrivate VPCECSCloud Run
Monitoring & Observability
LangfuseArize AIW&BMLflowPrometheusGrafanaDatadog
Backend & Application
PythonFastAPIDjangoNode.jsTypeScriptGraphQLWebSockets
Frontend & UI
ReactNext.jsVue.jsReact NativeFlutter

Frequently Asked Questions

Answers to the questions every founder and CTO asks before starting a generative AI development engagement

Generative AI development services cover the full lifecycle of building AI systems that create original content or perform generative tasks β€” including strategy and consulting, data preparation, model selection, custom LLM fine-tuning, RAG pipeline development, AI application development, integration with existing systems, and post-deployment monitoring. A generative AI development company like GMTA handles all of these layers, so your team does not have to.

Cost depends on scope, model complexity, data requirements, and team size. As a guide: a focused AI feature integration (e.g., embedding a chatbot into an existing product) typically ranges from $8,000–$30,000. A custom generative AI application with fine-tuned models, RAG, and a production frontend ranges from $30,000–$150,000+. Enterprise AI platforms are priced on application. We provide a detailed itemized quote after a free discovery call β€” with no obligation.

A minimum viable AI product (MVAI) typically takes 6–10 weeks from scope sign-off to production launch. A full-featured generative AI platform takes 12–24 weeks. Timeline depends on data readiness, integration complexity, and how much fine-tuning is required. We share a milestone-by-milestone schedule before any contract is signed.

Traditional AI/ML development typically involves building predictive models (classification, regression, recommendation) trained on labelled data for a specific task. Generative AI development uses large foundation models capable of producing novel outputs β€” text, code, images, audio β€” and involves prompt engineering, fine-tuning, RAG, and agent design. The two often complement each other: a generative AI system may call a traditional ML model one of its tools.

Not necessarily. Modern fine-tuning techniques like LoRA and QLoRA can produce domain-adapted models with as few as 500–2,000 high-quality examples. For RAG-based systems, you just need your existing documents in a processable format. Our team will conduct a data readiness assessment during discovery and recommend the most pragmatic path.

Yes β€” integration is one of our most common engagement types. We connect generative AI capabilities to your existing application via OpenAI, Anthropic, Vertex AI, or AWS Bedrock APIs, or by deploying an open-source model on your own infrastructure. We build or update your backend, handle authentication and rate limiting, and ensure the AI layer operates within your existing architecture without disruption.

RAG (Retrieval-Augmented Generation) is a technique where an LLM is augmented with a vector search layer that retrieves relevant information from your private documents before generating a response. This eliminates hallucinations, keeps responses factually grounded, and allows the AI to answer questions about your proprietary data without retraining the model. Most enterprise AI assistants, internal knowledge bots, and document Q&A systems benefit from RAG.

We sign NDAs before any project discussion. For development, we use isolated environments with no cross-client data access. For deployment, we support on-premise, private cloud (your AWS/GCP/Azure account), and VPC-isolated configurations where your data never leaves your infrastructure. We do not use client data to train models for other clients.

Start Your Generative
AI Journey with GMTA

Whether you have a detailed RFP ( request for proposal ready or just a one-line idea,) the conversation starts the same way β€” a 30-minute call with a senior AI specialist from our team. No sales pitch. Just a frank assessment of your opportunity, what it would take to build it, and what it should cost.

Awards & Recognition

Our commitment to excellence has been validated over the years by awards and recognition from renowned names, in addition to client appreciation, positioning us as the top mobile app development company globally.

Awards
clutch

Well-known for offering the best technological support and innovative, cutting-edge solutions for developing mobile apps.

goodfirm

Celebrated by the most popular websites for their unparalleled and outstanding solutions that increase the value of Mobile app development and web development.

Our Happy Clients

know why our prime prospects trust our services and how they feel about working with us.

Our Happy Clients
Our Happy Clients
Our Happy Clients
Our Happy Clients
Our Happy Clients
Our Happy Clients
Our Happy Clients

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Gmta Location
Jaipur

Jaipur

C-305, 2nd Floor, Jan Path, Nirman Nagar, Jaipur, Rajasthan 302019

Bengaluru

Bengaluru

No. 4C-432, 2nd Floor, 2nd block, HRBR Layout, Kalyan Nagar, Bengaluru, Karnataka, India 560043

Singapore

Singapore

55 Serangoon North Avenue 4 (S9) #09-01 Singapore 555859

USA

USA

5214F Diamond Heights Blvd #3136 San Francisco, CA 94131 United States

Japan

Japan

1 Chome-2-9 Minato City Tokyo, Japan γ€’105-0021

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