AI Development Services: Custom AI Solutions From Prototype to Production
Most AI prototypes never reach production. The gap isn't technology — it's finding engineers who know how to ship AI systems that perform reliably at scale, integrate with existing architecture, and deliver measurable business outcomes within real-world constraints.
Custom Model Development
Purpose-built AI models trained on your data, not off-the-shelf APIs
Rapid Prototyping
Working proof of concept in 4-8 weeks to validate the business case
Production Deployment
Model serving, monitoring, drift detection, and CI/CD for ML
MLOps & Lifecycle
Automated retraining, versioning, and governance at enterprise scale
The gap between demo and deployment is an engineering problem
Every data science team can build a model that performs well in a Jupyter notebook. The challenge is everything that comes after: serializing models for production serving, building feature stores that refresh without manual intervention, integrating predictions into existing workflows, and monitoring for data drift that degrades accuracy over time.
This is why AI development requires a different kind of engineer. Not a data scientist who codes — but a production-minded ML engineer who understands distributed systems, API design, observability, and the operational reality of keeping AI systems alive long after the proof of concept is approved.
Xylity's AI development practice exists specifically to close this gap. Through a consulting-led matching process, we connect enterprises with pre-qualified AI engineers who have shipped production systems — not just built models.
AI development capabilities that ship to production
Every capability below is staffed by pre-qualified engineers matched to your specific architecture, data environment, and business domain through our consulting-led process.
Computer Vision Systems
Object detection, image classification, OCR, and video analytics pipelines. From quality inspection on manufacturing lines to document processing in financial services — trained on your visual data, deployed on your infrastructure.
Explore AI consulting →Natural Language Processing
Text classification, entity extraction, sentiment analysis, and document understanding. Custom NLP models that process your domain-specific language — medical records, legal contracts, financial filings — with accuracy that generic APIs can't match.
See LLM development →Predictive Analytics & Forecasting
Demand forecasting, churn prediction, risk scoring, and propensity models. Built on your historical data, validated against business outcomes, and deployed as real-time scoring APIs or batch prediction pipelines your teams can act on daily.
See analytics consulting →Generative AI Applications
Content generation, code assistants, summarization engines, and conversational AI. Custom-tuned on your enterprise data with guardrails, content filtering, and responsible AI governance baked into the architecture from day one.
See RAG services →Recommendation & Personalization
Product recommendations, content personalization, and dynamic pricing engines. Collaborative filtering, content-based approaches, and hybrid models — implemented as low-latency APIs that integrate with your existing user experience.
See AI software dev →MLOps & Model Lifecycle
Automated training pipelines, model versioning, A/B testing frameworks, drift detection, and retraining triggers. The infrastructure that keeps your AI systems accurate and compliant long after initial deployment.
See AI agents →AI frameworks and platforms our engineers work with daily
Every engineer in the Xylity network is evaluated on hands-on production experience — not just framework familiarity. Our matching process verifies depth through scenario-based technical assessments.
PyTorch
Deep learning, custom model architectures, research-to-production workflows
TensorFlow / Keras
Production ML at scale, TFServing, TF Lite for edge deployment
Hugging Face
Transformer models, fine-tuning, model hub, inference APIs
LangChain / LlamaIndex
LLM orchestration, RAG pipelines, agent frameworks
Azure AI / OpenAI
Azure ML, Cognitive Services, Azure OpenAI, responsible AI
AWS SageMaker
ML pipelines, model registry, inference endpoints, feature store
Google Vertex AI
AutoML, custom training, prediction serving, Gemini integration
MLflow / Kubeflow
Experiment tracking, model registry, pipeline orchestration, deployment automation
AI development starts with data engineering maturity
The most elegant model architecture is useless without clean, reliable, timely data. That's why Xylity pairs AI development expertise with deep data engineering capability.
Every production AI system depends on a data pipeline that runs without manual intervention. Features need to be computed consistently between training and serving. Historical data needs to be versioned. Streaming data needs to be processed with predictable latency.
When your AI project requires data infrastructure work — and most do — Xylity can staff both sides of the equation simultaneously. Pre-qualified data engineers build the pipeline layer while AI engineers develop the model layer. Parallel execution means faster time to production and fewer integration surprises.
This is particularly relevant for enterprises working with Microsoft Fabric or Databricks — where the lakehouse architecture provides the foundation for both analytics and machine learning workloads.
From requirement to production-ready AI in four steps
Xylity's consulting-led process matches the right AI engineer to your project — not just their resume to your job description.
Requirement Deep-Dive
15-minute technical discovery: your AI use case, data maturity, tech stack, team structure, and timeline. We understand the project before we search the network.
Consulting-Led Matching
We don't keyword-match. We evaluate candidates against your specific scenario — the frameworks your codebase uses, the domain your data lives in, and the deployment target.
Technical Evaluation
Every AI specialist goes through a 4-stage assessment: skill verification, scenario interview, reference validation, and domain-specific technical review. 92% pass your interview on the first match.
Deploy & Support
Your AI engineer starts contributing within the first week — not the first month. A dedicated delivery manager monitors engagement quality and handles any adjustments.
AI development talent for two audiences
Building AI products or integrating AI into existing systems?
Whether you're developing a computer vision pipeline, deploying an LLM-powered application, or building predictive models from your enterprise data — Xylity matches pre-qualified AI engineers to your project. Companies of 500-10,000 employees trust our consulting-led approach to deliver specialists who ship production code in week one.
Start a Consulting Engagement →Client demanding AI expertise you don't have on the bench?
AI development roles are among the hardest to fill. When your client's project calls for a PyTorch engineer, a computer vision specialist, or an MLOps architect you don't have, Xylity's network of 200+ pre-qualified partners delivers curated profiles in days — not weeks. IT services companies of 20-1,000 employees use Xylity as their delivery safety net for niche AI talent.
Scale Your AI Delivery →AI development — what to expect
Your AI project deserves engineers
who've shipped production AI before.
Tell us about your use case. We'll match pre-qualified AI development specialists from our network — curated for your architecture, your data, and your timeline.