Generative AI development services
ChatGPT showed you what's possible. Now you need something that actually works with your data, fits your workflows, and runs in production without someone babysitting it. We build generative AI applications that do real work for real businesses, not chatbot demos that impress in a meeting and break the moment someone asks a hard question.
SportHub
Flustr
Armor Up America
Rundivo
RAE HealthWhat we build with generative AI
AI chatbots and virtual assistants
Customer-facing chatbots that answer questions from your actual documentation, not from the model’s training data. Internal assistants that help your team search knowledge bases, draft documents, and handle routine requests. These run on RAG architectures so the answers are grounded in your data and cite their sources.
Content generation tools
Systems that draft marketing copy, product descriptions, email templates, reports, or documentation using your brand voice and your data. Not a "write me a blog post" button. More like a production pipeline that generates, reviews, and formats content at scale with human approval built into the workflow.
Intelligent document processing
AI that reads contracts, invoices, medical records, legal filings, or compliance documents and extracts structured data, flags issues, summarizes findings, and routes them to the right person. Faster than a human reviewer. More reliable than keyword search.
AI-powered search and knowledge retrieval
Replace keyword search with something that understands questions. We build retrieval-augmented generation (RAG) systems that connect LLMs to your documents, databases, and knowledge bases so your team (or your customers) can get real answers instead of a list of ten links.
RAG development services→Data analysis and reporting automation
Generative AI that reads your dashboards so your team doesn’t have to. Systems that analyze datasets, spot anomalies, generate narrative reports, and surface the numbers that matter. Connect this to your BI tools and you get weekly summaries written in plain English instead of spreadsheets nobody opens.
Custom LLM applications
Anything that uses a large language model as its core engine: code review tools, translation services, classification pipelines, recommendation systems, conversational interfaces. If it involves generating, analyzing, or transforming text at scale, we can build it.
Generative AI consulting services
Not every project is ready for a full build. Sometimes you need someone to help you figure out where generative AI actually fits in your business, what it'll cost, and whether the results justify the investment.
Strategy workshops
We spend 1 to 2 days with your leadership team mapping which business processes would benefit from generative AI and which ones wouldn’t. You leave with a prioritized list and a rough budget for each item.
Technical feasibility assessments
You have an idea. We tell you whether it’s buildable, how accurate the results would be with your data, and what the technical architecture looks like. No slides, no buzzwords. A document your engineers can act on.
AI readiness audits
Is your data in good enough shape for generative AI? Do you have the right infrastructure? Are there compliance issues you haven’t thought of? We answer these questions before you spend money on development.
Build vs. buy analysis
You might not need custom development at all. We’ll tell you if an off-the-shelf tool (or a combination of them) solves your problem well enough, and where custom is worth the premium.
Our generative AI development process
Discovery and generative AI strategy
We start by understanding what problem you’re solving and what success looks like. Then we map the technical approach: which models to use, what data sources to connect, how the AI fits into your existing systems, and what the minimum viable version looks like. This takes 1 to 2 weeks and produces a spec you can share with stakeholders.
Proof of concept
We build a working prototype in 3 to 6 weeks using your real data. The goal is to prove the approach works before committing to a full build. If the proof of concept shows the model doesn’t perform well enough with your data, you find out now instead of four months from now.
Generative AI implementation
The full development phase. We build the AI pipeline (model orchestration, prompt management, retrieval logic), the backend services (APIs, data processing, authentication), the frontend (user interfaces, admin tools, review dashboards), and the infrastructure (deployment, monitoring, scaling). You get working demos every two weeks.
Testing and evaluation
Generative AI output is non-deterministic, which means testing it requires different tools than traditional software. We evaluate model accuracy, hallucination rates, latency, token costs, and edge case handling. We test with your actual users and adjust prompts, retrieval logic, and guardrails based on what we find.
Deployment and monitoring
We deploy to your infrastructure (AWS, GCP, Azure, or on-premise) and set up monitoring for the metrics that matter: answer quality, response latency, token spend, user satisfaction, and data freshness. When the model starts drifting or a new model version drops, you see it in your dashboard.
Iteration and scaling
Generative AI projects improve over time. We tune prompts, expand data sources, add new use cases, and optimize costs as usage grows. The first version gets you to production. The iterations after that make it good.
Our generative AI technology stack
Large language models
OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3.5, Claude 4), Meta (Llama 3), Mistral, Google (Gemini), Cohere, open-source models via Ollama and vLLM
Orchestration
LangChain, LlamaIndex, Semantic Kernel, Haystack, custom Python frameworks
Vector databases
Pinecone, Weaviate, Qdrant, pgvector, Milvus, ChromaDB
Embedding models
OpenAI text-embedding-3, Cohere Embed, BGE, E5, sentence-transformers
Infrastructure
AWS (Bedrock, SageMaker, Lambda), GCP (Vertex AI), Azure (OpenAI Service), Docker, Kubernetes
Backend
Python, Node.js, PHP/Laravel, Go
Frontend
React, Next.js, Flutter, Swift, Kotlin
Evaluation
RAGAS, DeepEval, LangSmith, custom test frameworks
Generative AI for business across industries
Healthcare
Clinical documentation assistants, patient intake automation, medical literature search, and treatment protocol retrieval. HIPAA-compliant by design. The model works with your institutional knowledge, not generic internet data.
Financial services
Automated compliance reporting, risk narrative generation, client communication drafting, and research summarization. Financial services demand accuracy and audit trails; we build both into the architecture.
E-commerce
AI-generated product descriptions at scale, personalized marketing copy, smart product search, and customer service automation that pulls from your actual catalog and order data.
Legal
Contract analysis, clause extraction, legal research assistants, and document summarization. Generative AI that gives lawyers citations they can trace, not summaries they have to verify from scratch.
SaaS and technology
AI features embedded in your product: smart help search, auto-generated reports, natural language data queries, onboarding assistants. Your users get a smarter product; you get a defensible competitive advantage.
Why hire Attract Group as your generative AI development company
We ship working software, not research papers.
We’ve been building production systems since 2011. When we say "generative AI development," we mean an application that runs in your environment, handles real users, and survives its first week without someone manually fixing outputs. The AI model is one component. The backend, the frontend, the data pipeline, the monitoring, the deployment: that’s the other 80%.
We’re honest about what generative AI can do today.
LLMs are impressive. They also hallucinate, lose context in long conversations, and sometimes produce wildly wrong answers with total confidence. We design around these limitations instead of pretending they don’t exist. Guardrails, human review steps, retrieval grounding, output validation: these aren’t afterthoughts.
We don’t lock you in.
You own the code, the prompts, the pipeline configurations, the deployment scripts. No proprietary wrappers. If you want to bring development in-house after launch, everything we built is documented and transferable.
We right-size the solution.
Not every problem needs GPT-4. Sometimes a smaller model, a fine-tuned classifier, or a well-designed rule engine gets you the same result at a fraction of the cost. We’ll recommend the cheapest approach that actually works.
We’ve built for regulated industries.
Healthcare, finance, insurance. We know what HIPAA, SOC 2, and GDPR compliance looks like in practice, and we build it into the architecture from day one.
How much do generative AI development services cost?
Here's an honest range based on what we've built:
Generative AI proof of concept
$15,000 to $35,000
3–6 weeks
One use case, one data source, working prototype tested against your real data. Validates the approach before you commit further.
MVP with generative AI features
$40,000 to $120,000
2–4 months
A production application with core AI functionality, 1 to 3 integrations, basic monitoring, and deployment to your infrastructure. Covers the AI pipeline, backend, frontend, and testing.
Full generative AI platform
$120,000 to $350,000+
4–8 months
Multiple AI capabilities, complex data pipelines, custom UIs, evaluation frameworks, compliance controls, and multi-model architectures. This is what an enterprise-grade generative AI product looks like.
What drives the cost up: messy data that needs cleaning, multiple system integrations, strict compliance requirements, real-time (vs. batch) processing, and the number of distinct AI capabilities you need. What keeps it down: clean data, clear scope, starting with a single use case and expanding from there.
What clients say
Frequently asked questions about generative AI development
Ready to build something with generative AI?
Tell us what problem you're trying to solve. We'll give you a straight answer about whether generative AI is the right tool, what the build looks like, and when you can expect results. No pitch decks.
Ready to build something with generative AI?
Tell us what problem you're trying to solve. We'll respond with an honest assessment and realistic timeline.