Custom AI solutions built around your business
Off-the-shelf AI tools do generic things generically. They don't know your workflows, your data, or why your operations team spends four hours a day copying numbers between spreadsheets. We build custom AI solutions that solve the problems your business actually has, not the ones a product company imagined.
SportHub
Flustr
Armor Up America
Rundivo
RAE HealthWhen off-the-shelf AI is not enough
Most companies start with SaaS AI tools. That's a reasonable first step. But at some point, you run into the walls: the tool doesn't integrate with your systems, it can't access your proprietary data, and you're bending your process to fit the software instead of the other way around.
Custom AI development makes sense when:
- Your competitive advantage depends on proprietary data or workflows that no generic tool can replicate
- You’ve outgrown the configuration limits of a SaaS product and need control over the model, the data pipeline, and the output format
- Security or compliance requirements prevent you from sending data to third-party APIs
- You need the AI to integrate tightly with existing internal systems (your ERP, CRM, or legacy databases)
- You want to embed AI into your own product as a feature your customers use
If any of these describe your situation, a custom build is probably worth the investment.
Custom AI solutions vs. SaaS: which is right for your business? →
What we build
AI agents and assistants
Autonomous agents that handle multi-step tasks: scheduling, data lookups, report generation, customer interactions. These aren’t chatbots following a script. They reason about context, use tools, and make decisions based on rules you define.
What is an AI agent? A business leader’s guide→Custom AI models
Sometimes you need a model trained on your specific data. We build and fine-tune custom AI models for classification, prediction, entity extraction, recommendation, and natural language understanding. The model runs on your infrastructure and improves as it sees more of your data.
Retrieval-augmented generation (RAG) systems
Your team has thousands of documents and no good way to search them. We build RAG systems that connect LLMs to your internal knowledge so employees (or customers) can ask questions and get accurate, sourced answers.
RAG development services→Predictive analytics and forecasting
Demand forecasting, churn prediction, lead scoring, fraud detection. If you have historical data and a question about what happens next, we can build a model to answer it. These models plug into your dashboards and decision-making workflows, not just a Jupyter notebook.
AI-powered process automation
Some processes don’t need a human reviewing every row in a spreadsheet. We build AI systems that handle document processing, data extraction, classification, routing, and approval workflows. The human stays in the loop where judgment matters and steps away where it doesn’t.
Computer vision and NLP applications
Custom machine learning solutions for image recognition, object detection, OCR, sentiment analysis, text classification, and language processing. These are typically embedded into larger products or operational systems, not standalone toys.
Our AI development process
Discovery and problem definition
We spend the first two weeks understanding what you actually need. Not what you think the technology should be, but what business outcome you’re after. Sometimes the answer is an LLM agent. Sometimes it’s a simple rules engine. We’ll tell you which one.
We look at your data, your systems, and your team’s workflows. By the end of discovery, you have a clear spec: what we’re building, how it connects to your stack, and what success looks like.
Proof of concept
Before committing to a full build, we validate the approach. A proof of concept typically takes 3 to 6 weeks and answers the question: does this actually work with your data? If it doesn’t, you find out early and cheaply, not six months into development.
Development and integration
This is the main build. We develop the AI components, build the data pipelines, create the API layer, and integrate everything with your existing systems. You get working demos every two weeks, not a surprise delivery after three months of silence.
Testing and evaluation
AI systems need a different kind of testing. We measure model accuracy, retrieval quality, edge case handling, latency, and cost per inference. We also test with your actual users and your actual data, because synthetic tests only get you so far.
Deployment and handoff
We deploy to your infrastructure (AWS, GCP, Azure, or on-premise) and set up monitoring for model performance, data drift, and system health. We train your team on how the system works. You get full ownership of the code, the models, and the data.
Ongoing support
AI systems drift over time as data changes. We offer maintenance packages that include model retraining, pipeline monitoring, and periodic performance reviews. Or you can handle it in-house with the documentation we provide.
Data readiness: the hidden requirement for AI success→Custom AI development vs. generic AI tools
| Custom-built AI | Off-the-shelf AI tools | |
|---|---|---|
| Fits your workflow | Built around how your team actually works | You adapt your process to the tool |
| Data access | Connects to all your internal systems and proprietary data | Limited to supported integrations |
| Ownership | You own the code, models, and data | Vendor owns everything; you rent access |
| Accuracy | Trained on your specific data and edge cases | Generic model, generic results |
| Security | Runs on your infrastructure, your rules | Your data goes to their servers |
| Cost structure | Higher upfront, lower long-term (no per-seat fees) | Low upfront, grows with usage and seats |
| Flexibility | Change anything at any time | Wait for the vendor’s roadmap |
Neither option is always better. For standardized tasks (email marketing, basic analytics, simple chatbots), SaaS tools are usually the right call. For anything that touches your core business logic or proprietary data, custom is worth it.
Custom AI solutions by industry
Healthcare
Clinical decision support, patient triage automation, medical document processing, and predictive models for readmission risk. We build with HIPAA compliance from day one, not bolted on after the fact.
Applications of machine learning in healthcare→Financial services
Fraud detection models, credit risk scoring, regulatory compliance automation, and AI-driven document analysis for KYC/AML workflows. Financial data needs to stay in your environment, and the models need audit trails. We build for both.
E-commerce and retail
Product recommendations that actually work, dynamic pricing engines, inventory demand forecasting, and visual search. These systems pay for themselves when connected to real transaction data.
How generative AI in ecommerce is changing the industry→Logistics and supply chain
Route optimization, demand prediction, warehouse automation, and freight document processing. Logistics companies sit on a gold mine of operational data. Most of it goes unused. We help change that.
SaaS platforms
AI features embedded in your product: smart search, auto-categorization, content generation, anomaly detection. Your users get a better product; you get a stronger competitive position.
Our technology stack for custom AI development
AI/ML frameworks
PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, LangChain, LlamaIndex
LLM providers
OpenAI (GPT-4), Anthropic (Claude), Meta (Llama), Mistral, Google (Gemini), open-source models
Data infrastructure
PostgreSQL, MongoDB, Redis, Apache Kafka, Apache Airflow, Spark
Vector databases
Pinecone, Weaviate, Qdrant, pgvector, ChromaDB
Cloud platforms
AWS (Bedrock, SageMaker, Lambda), GCP (Vertex AI), Azure (OpenAI Service, ML), on-premise
Backend
Python, Node.js, PHP/Laravel, Go
Frontend
React, Next.js, Flutter, Swift, Kotlin
DevOps
Docker, Kubernetes, Terraform, GitHub Actions, CI/CD pipelines
Why companies hire Attract Group for custom AI solutions
We’re a software company that does AI, not an AI lab pretending to ship products.
We’ve been delivering production software since 2011. AI is a tool in our stack, not the only thing we know how to do. That matters because a custom AI solution needs a backend, a frontend, an API, authentication, deployment infrastructure, and someone who knows how to keep it all running. We build the complete system.
We start by understanding your business problem.
The first question we ask isn’t "what model do you want?" It’s "what outcome are you trying to get?" Sometimes the best solution involves AI. Sometimes it doesn’t. We’ve talked clients out of AI projects when a simpler approach would have gotten them the same result in half the time.
You own everything we build.
The code, the trained models, the data pipelines, the deployment scripts. All of it. No proprietary wrappers, no platform lock-in. If you decide to bring development in-house later, you can pick up exactly where we left off.
We’re honest about what AI can and can’t do.
If your data isn’t ready, we’ll tell you. If your use case is better served by a fine-tuned model than an agent, we’ll say so. If the whole project is premature, we’d rather save you the money and come back when the timing is right.
We’ve built software for healthcare, fintech, e-commerce, logistics, and aviation.
Industry context matters. A team that’s worked in your regulatory environment, with your types of data, will get to a working solution faster than one learning as they go.
How much does custom AI development cost?
We won't pretend a single number covers every project. But here's a rough framework based on what we've built:
AI proof of concept
$15,000 to $35,000
3-6 weeks
A working prototype with a single use case, tested against your real data. The goal is to validate the approach before committing to a full build.
MVP with AI features
$40,000 to $100,000
2-4 months
A production-ready system with core AI functionality, integrations with 1-3 data sources, basic monitoring, and deployment to your infrastructure.
Full custom AI solution
$100,000 to $300,000+
4-8 months
Multiple AI capabilities, complex data pipelines, multi-source integrations, custom UI, evaluation frameworks, and ongoing model management. This is what a complete enterprise-grade system looks like.
The biggest cost variables: how clean your data is, how many systems need to integrate, and whether you need real-time inference or batch processing.
How much does custom AI agent development cost? →What clients say
Frequently asked questions about custom AI solutions
Let's talk about what you need
Describe your problem. We'll tell you honestly whether custom AI is the right approach, what it'll take to build, and how soon you can expect results. If it's not the right time, we'll say that too.
Ready to build your custom AI solution?
Describe your problem and what you're trying to achieve. We'll respond with an honest assessment and realistic timeline.