AI advisory
Decide where AI can create value and define a credible route to production.
Choose the engagement model
Custom AI development
Build integrated agents, knowledge systems, and operational automation.
What is AI dingkai?
Understand the Chinese shorthand and verify the real production delivery boundary.
Hong Kong AI provider shortlist
Compare five publicly verifiable options by delivery need, without a ranking.
Enterprise AI RFP template
Issue comparable requirements for evidence, controls, delivery, commercials, and exit.
Forward Deployed Engineering
Solve complex, underspecified problems through embedded diagnosis and delivery.
Choose the engagement model
Compare advisory, custom development, and FDE by the uncertainty they resolve.
Choose an AI delivery partner
Compare providers by evidence, production controls, ownership, and validation.
AI pilot charter
Define evidence gates and sign a scale, redesign, hold, or stop decision.
Hong Kong AI governance
Turn PCPD guidance into accountable controls and lifecycle evidence.
AI development cost guide
Understand the scope and risk factors behind a credible estimate.
ERP integration guide
Connect agents to systems of record through explicit authority tiers.
Enterprise AI evaluation
Evaluate capability, control, workflow reliability, and operating outcomes.
Legal AI evidence workflow
Trace dossier conclusions to files, pages, source text, and lawyer review.
Recruiting AI workflow
Automate candidate context and replies with send verification and takeover.
Industrial ERP and AI
Connect field data to management without handing safety control to AI.
Credit research agents
Accelerate research while retaining source coverage and analyst judgement.
What is FDE?
Read the practical definition, fit criteria, and evidence loop.
When should an enterprise stop an AI project?
A practical stop-condition framework for enterprise AI projects: mandatory gates, pause signals, decision ownership, and evidence needed to stop, redesign, or scale.
How should an enterprise verify an AI vendor before signing?
A buyer-side checklist for verifying an enterprise AI vendor’s entity, delivery team, production controls, evidence, commercials, ownership, and exit plan.
Is Forward Deployed Engineering just staff augmentation?
A practical comparison of Forward Deployed Engineering, staff augmentation, and outsourced delivery by accountability, uncertainty, workflow access, evidence, and exit.
What permissions and audit records does an enterprise AI agent need?
A production checklist for AI-agent authority tiers, least privilege, identity mapping, confirmations, audit records, idempotency, revocation, and incident response.
Should an enterprise buy AI software or build a custom system?
A practical framework for choosing SaaS, configuration, custom AI development, or a hybrid system by differentiation, integration, control, evidence, cost, and exit.
Should an enterprise use RAG, fine-tuning, or AI agents?
Choose RAG, fine-tuning, tools, workflows, or AI agents according to the required knowledge, behaviour, actions, evidence, controls, and update cycle.
What must be true before an enterprise AI system goes live?
A go-live checklist for enterprise AI covering ownership, representative evaluation, data and permissions, reliability, cost, observability, recovery, adoption, and release gates.
What should an enterprise receive when an AI project is handed over?
A practical handover and vendor-exit checklist covering ownership, source and configuration, data, evaluation, operations, access revocation, knowledge transfer, and continuity.
How should a law firm procure and accept a legal AI system?
A buyer guide for legal AI covering dossier coverage, page-level citations, matter permissions, lawyer review, evaluation, audit, and production acceptance.
How should accounting work be automated with AI without losing control?
An implementation guide for accounting AI covering source evidence, deterministic rules, exception handling, review, close controls, audit, and official submission boundaries.
How should a manufacturer connect ERP and operational data to AI?
A manufacturing AI guide covering ERP, MES and PLC boundaries, trusted operational data, read and write authority, safety isolation, prediction, audit, and phased deployment.
How should a recruiting AI agent work with human recruiters?
A recruiting AI guide covering candidate context, factual profiles, drafting, approval, send verification, fairness review, privacy, audit, and recruiter takeover.
How does an AI proof of concept become a production system?
A practical guide for moving an enterprise AI proof of concept into production through workflow evidence, controls, integration, ownership, operations, and staged authority.
How can an enterprise tell whether its data is ready for AI?
Assess whether enterprise data is ready for AI through workflow relevance, source ownership, identity, quality, history, permissions, labels, retrieval, and operational feedback.
How should an enterprise estimate AI ROI and total cost of ownership?
Estimate enterprise AI return and total cost through a measured workflow baseline, attributable benefits, delivery and operating costs, risk, adoption, scenarios, and decision gates.
How should an enterprise diagnose and recover a stalled AI project?
A recovery framework for stalled enterprise AI projects covering evidence preservation, diagnosis, business value, data, evaluation, architecture, ownership, controls, and restart decisions.
What should an enterprise AI RFP require?
A practical enterprise AI RFP guide covering workflow scope, evidence, data, security, integration, evaluation, operations, pricing, ownership, handover, and acceptance.
How long does an enterprise AI implementation take?
A stage-based enterprise AI implementation timeline covering discovery, evidence, integration, controlled pilot, production release, ownership, dependencies, and decision gates.
How should enterprise AI connect to existing systems?
A practical architecture guide for connecting enterprise AI to systems of record through identity, data contracts, retrieval, tools, events, write controls, observability, and recovery.
Should an enterprise run AI in-house or use a managed service?
A practical guide for choosing an in-house, managed, or hybrid AI operating model based on decision rights, capability, continuity, cost, controls, knowledge transfer, and exit.
How should an enterprise identify the right AI opportunities?
A practical framework for identifying and prioritising enterprise AI opportunities using workflow evidence, intervention fit, data readiness, risk, value, adoption, and test cost.
How should an enterprise build a reliable RAG knowledge base?
A practical enterprise RAG guide covering source authority, document coverage, permissions, parsing, retrieval, citations, evaluation, freshness, operations, and escalation.
How should an enterprise security-review an AI agent?
A practical AI agent security review covering threat model, identity, tenant isolation, prompt injection, tool permissions, data egress, supply chain, audit, incidents, and recovery.
How should an enterprise operate and monitor AI in production?
A practical AI operations guide covering service objectives, model and workflow monitoring, evidence, change control, cost, incidents, human fallback, recovery, and continual review.
Case-study evidence register
Read the metric definitions, source pages, and limitations behind six delivery cases.