AI Engineering
Intelligence that works inside your business.
Build useful AI systems around your knowledge, applications and operating constraints—not a disconnected chat window.
Product owners, operations teams and engineering leaders with a bounded AI use case.
Approved data samples; task examples; access boundaries; a business owner; a baseline for evaluation.
Model and API usage, licensed datasets and ongoing operations are scoped separately. High-impact decisions require agreed human review.
7 offerings in AI Engineering.
Each offering has a defined deliverable and acceptance criteria. The scope is what makes delivery measurable.
AI Chatbots & Copilots
An assistant interface, conversation flows, approved knowledge connections and a human-handoff route.
Agreed task scenarios pass evaluation; unsupported questions and escalation paths are demonstrated.
AI Agents & Workflows
A bounded multi-step workflow with tool permissions, approval checkpoints and execution logs.
The agent completes agreed tasks and safely handles tool failures, retries and rejected approvals.
RAG & Knowledge Systems
A document ingestion pipeline, retrieval service, source-linked answers and role-aware access.
Answers are tested against a reviewed question set; private sources stay within their access boundaries.
Generative AI Applications
AI-assisted content or application features with editable outputs and user controls.
The feature meets the agreed output rubric, latency budget and review workflow.
Document Intelligence
Data extraction for agreed document types, structured output and a human-review queue.
A representative sample is reconciled against expected fields, confidence rules and exceptions.
Predictive ML Solutions
A scoped baseline, model experiment, inference interface and validation report.
Results are compared with the baseline on an agreed held-out dataset, with limitations recorded.
AI Evaluation & Monitoring
An evaluation set, versioned prompts, test reports and cost/latency monitoring configuration.
Changes can be compared reproducibly and operational alerts can be demonstrated.
AI & Intelligence
Turn enterprise data, workflows and knowledge into systems that can understand, reason and act.
Business outcomes this service supports
Typical engagement models
Buyer teams
Questions about AI Engineering.
What does AI Engineering include?
The current scope examples include ai chatbots & copilots, ai agents & workflows, rag & knowledge systems. The exact deliverable, acceptance criteria, dependencies and exclusions are agreed for each engagement.
What does APPNEURAL need to scope AI Engineering?
Approved data samples; task examples; access boundaries; a business owner; a baseline for evaluation. The first step is to confirm owners, system boundaries, access and the expected handover.
How do you verify delivery?
Acceptance criteria are agreed before delivery. Agreed task scenarios pass evaluation; unsupported questions and escalation paths are demonstrated.
AI Engineering is part of these solutions.
Add AI to Existing Software
Introduce one useful AI capability without losing control of your existing application.
For Enterprises
Modernise, integrate and scale without disrupting what is already working.
AI Customer Support
Deflect routine questions and route complex ones to the right team.
Ready to get started with AI Engineering?
Tell us about your requirement and we will outline the right scope and approach.