Best fit
A document, support, search, classification, extraction, or decision-support workflow with representative data, a measurable acceptance threshold, and a named process owner.
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
Engagement fit
When is an AI implementation worth building?
Last reviewed
Reviewed by
Dominik Dragičević, Founder and CEO
AI is a fit when a defined user or operational task can be evaluated against real examples and the expected value justifies model, review, and operating costs. CoreLine turns that task into a testable product capability with an evaluation set, integration boundary, monitoring, fallbacks, and an explicit human-review policy.
Not the right fit
An undefined mandate to 'add AI,' a fully autonomous high-stakes decision with no accountable human owner, or a use case with no way to judge whether outputs are correct.
First engagement
Feasibility work defines the task, baseline, evaluation dataset, data and privacy constraints, provider options, expected operating cost, and failure modes before production scope is approved.
First deliverable
A tested proof of value and decision record showing measured quality, known limitations, recommended architecture, rollout controls, and a costed path to production.
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
How we implement
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.
Use Case & Feasibility
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.
Build & Validate
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.
Deploy & Monitor
Production deployment includes inference monitoring, cost tracking, quality metrics, and fallback paths. We instrument for model drift and set up retraining pipelines where needed.
Production acceptance criteria
Evaluated
against an agreed test set
Task-specific quality thresholds are defined before production approval
Portable
across suitable model providers
Application logic is separated from provider-specific model calls
Operable
with monitoring and fallbacks
Latency, cost, quality signals, errors, and escalation paths are observable
What we work with
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
AWS offers the tools to build powerful, flexible, and scalable applications, from compute and storage to databases and content delivery.
learn more
learn more
Flutter
Simply put, Flutter is Google’s cross-platform framework that lets you build mobile, web, desktop, and embedded apps, all from a single codebase.
learn more
learn more
React Native
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.
learn more
learn more
Other technologies we use
PHP
Node.js
Vue.js
React.js
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.
Cost depends on data preparation, evaluation design, integrations, security controls, review workflows, inference volume, and model choice. Feasibility work produces separate build and operating-cost estimates before production scope is approved.
We create a representative evaluation set and choose metrics for the actual task: accuracy or precision/recall for classification, field-level correctness for extraction, and human-scored factuality, completeness, and usefulness for generated outputs. We also measure latency, failure rate, and cost per completed task.
Related posts
ready to start?
Tell us what you need built, modernized, or unblocked. We scope it in one call.