AI Implementation Services
CoreLine helps product teams integrate AI into enterprise applications. We build LLM-powered features, ML pipelines, and intelligent automation - embedded in your product architecture, not bolted on as a demo.
LLM Integration
Retrieval-augmented generation
Conversational interfaces
Document processing
ML Pipelines
Data pipeline architecture
Model training and evaluation
Production model serving
AI Strategy
Use case identification
Build vs. buy assessment
Vendor portability planning
Sound familiar?
You want to add AI features to your product but your team doesn't have ML experience
Your AI proof-of-concept works in a notebook but won't survive production traffic
You're worried about vendor lock-in with a single AI provider
You need AI features that handle sensitive data under compliance constraints
AI implementation starts with the business problem, not the model. We identify where AI creates measurable value, build the pipeline, and deploy to production with monitoring and guardrails.
We assess which product features benefit from AI, evaluate data availability and quality, and produce a technical feasibility report with expected accuracy, cost, and timeline.
We build the AI pipeline: data preparation, model selection (or fine-tuning), evaluation framework, and integration with your application. Output quality is validated against ground-truth datasets.
Production deployment includes inference monitoring, cost tracking, quality metrics, and fallback paths. We instrument for model drift and set up retraining pipelines where needed.
AI-assisted outcomes
Using solutions such as React Native, Flutter, AWS, and many others makes it possible for us to upgrade your product as much as possible and to achieve a thriving collaboration. Here you will find our guide through our most used technologies and case studies related to each one.
AWS offers the tools to build powerful, flexible, and scalable applications, from compute and storage to databases and content delivery.
Simply put, Flutter is Google’s cross-platform framework that lets you build mobile, web, desktop, and embedded apps, all from a single codebase.
React Native is Facebook’s cross-platform framework that combines the best of React with native platform features - perfect for new projects or enhancing existing ones.
Other technologies we use
Frequently asked questions
Not always. LLM-based features (RAG, document processing, conversational interfaces) work with your existing content and data. Custom ML models typically need domain-specific training data, but we can start with transfer learning and fine-tuning to reduce data requirements.
We design the integration layer for portability: standardized prompt interfaces, model-agnostic evaluation frameworks, and abstraction layers that let you swap providers (OpenAI, Anthropic, open-source models) without rewriting application code.
Yes. We implement data handling policies, audit trails, and output filtering appropriate to your compliance framework. For sensitive data we deploy models in your own infrastructure or use providers with appropriate certifications.
Initial implementation typically takes 6-10 weeks. Ongoing costs depend on inference volume, model choice, and infrastructure. We provide cost modelling during feasibility so you know the economics before committing.
We define evaluation metrics during feasibility: accuracy, precision/recall for classification, BLEU/ROUGE for generation, latency, and cost per inference. Production monitoring tracks these continuously with alerting on degradation.
