RAG
Retrieval-Augmented Generation: AI answers questions using your company's own documents, manuals and knowledge bases — not just model training data.
Architecture & Patterns
How AI connects to your systems
Event-Driven Workflows
Webhooks and event triggers fire AI actions when something happens — a new order, an incoming email, a status change. Zero polling, instant reactions.
API Bridges
Clean API layer between AI and your systems. Queuing, error handling, retries and rate limiting built in. No direct coupling between AI model and core system.
Separation of Layers
AI layer stays separate from core systems. Swap models, change providers, add features — without touching the business logic underneath.
By System
What AI does inside each system
CRM
- Enrich leads with company data and buying signals
- Pre-draft proposals and follow-up emails
- Auto-generate call summaries and next actions
- Score lead quality and flag hot opportunities
ERP
- Recognise and code incoming invoices automatically
- Suggest bookings and flag anomalies
- Match purchase orders to deliveries
- Trigger reorder alerts from inventory patterns
DMS
- Semantic search across all company documents
- Auto-generate summaries and key-points
- Extract structured data from unstructured documents
- Suggest templates and pre-fill from context
PMS / Project Tools
- Auto-prioritise tasks based on deadlines and dependencies
- Generate status reports from task updates
- Surface blockers and risks proactively
- Onboarding document packages for new projects
The Approach
Integration without disruption
1
System assessment (Week 1)
Map your current stack, data flows and integration points. Identify the best entry point for AI — the highest-value touchpoint with the lowest integration risk.
2
Parallel PoC with real data (Weeks 2-3)
Build the integration alongside your existing process. Run in shadow mode: the AI does its job, you compare outputs to your current results before committing.
3
Controlled rollout (Weeks 4-6)
Release in stages with fallbacks active. Monitor quality metrics, error rates and latency. Adjust before expanding to full volume.
4
Handover & ongoing ops
Documentation, access controls, monitoring dashboards and team training. Your team runs it — or I maintain it. Fully your choice.
No big-bang projects
AI that fits into what you already have
I don't replace your stack — I extend it. Every integration I build is designed to work with your existing systems, not against them. Legacy ERP, custom databases, old APIs — I've seen it all and have solutions that don't require a three-year migration project.
Book a free system checkFAQ
Common questions
Will AI integration disrupt our current operations?
No. We run a parallel proof of concept with real data, test in shadow mode and release in controlled stages. Your live system stays untouched until quality is validated.
How do you handle legacy systems without APIs?
Via file-based exchange, lightweight RPA adapters or thin API wrappers. No big-bang replacements — we work with what you have and add a clean interface layer on top.
On-prem or cloud — which is right for us?
Depends on data classification, latency, cost and auditability. Mixed deployments are common — sensitive data stays on-prem, general queries go to cloud models. We help you design the right architecture.
Who operates and maintains the integration afterwards?
Your team or me — fully your choice. Handover includes documentation, monitoring setup and training. I offer optional maintenance retainers.