1300 71 61 41AI Solutions
We Don’t Sell AI. We Engineer Systems That Actually Work.
Your business runs on six platforms that don’t talk to each other.
The AI tool you bought last year reads one of them. Meanwhile, your team is still exporting CSVs every Monday morning to figure out what happened last week.
We build the intelligence layer that connects your CRM, accounting, telephony, and operations into a unified system — with purpose-built databases, scheduled data pipelines, and custom platforms engineered from the ground up. Not a chatbot wrapper. Not a workflow plugin. Real architecture that runs your business on real data.
HubSpot Diamond Partner. Custom platform engineers.
Sound Familiar?
These are the problems we hear from every new client. If any of these sound like your Monday morning, we should talk.
HubSpot has your deals. Xero has your invoices. Aircall has your call logs. Google Workspace has your calendar. And somewhere, a spreadsheet is holding it all together. Nobody has the full picture because the full picture doesn’t exist in any single system.
You invested in an AI tool that was supposed to change everything. It answers basic questions. Maybe it summarises emails. It doesn’t touch your CRM, your accounting system, or your operations data — because it was never built to.
Every Monday, someone exports data from three platforms, copies it into a spreadsheet, and builds a report that’s already stale by the time it reaches a decision-maker. The data was fine at 7am. By the 10am meeting, the numbers are wrong.
The vendor promised real-time data. What you got is an integration that works until API rate limits hit, a token expires, and your dashboard shows yesterday’s numbers. Live API calls are a single point of failure.
Your CRM vendor added “AI” to their feature list. It auto-populates a field. It scores a lead based on rules someone wrote. That’s automation — and it’s fine. But it’s not intelligence. The difference matters.
A Zapier flow triggers a Slack notification that reminds someone to update a spreadsheet that feeds a dashboard that someone else checks once a week. Each piece works. The chain is fragile.
What We Build
Layer 1
Data Foundation
What it is: Connecting your disconnected business systems into a single, unified data layer that every other capability builds on.
How we build it: Custom data pipelines that pull from your business platforms on controlled schedules — not live API calls that break at scale. HubSpot deals, Xero invoices, Aircall call logs, Google Workspace calendars: each source syncs into a purpose-built PostgreSQL database designed around your specific operations.
Real outcome: In one build, we connected five business platforms into a unified operational database with cron-based sync running every four hours. A capacity planning process that consumed two hours of manual work every week now updates automatically.
Layer 2
Intelligence Layer
What it is: Turning raw operational data into insights and views that drive real business decisions — not reports that confirm what you already suspected last week.
How we build it: Purpose-built dashboards and operational views that serve pre-computed insights from the database. Sales pipeline health, financial position, team capacity, client delivery status — each view answers a specific operational question. DB-cached: when your team opens a dashboard at 9am, the data is already there.
Real outcome: An operations intelligence platform with eight distinct modules — spanning sales, finance, operations, client success, marketing, and strategic planning — each pulling from the same unified database.
Layer 3
Automation Engine
What it is: Rule-based process execution that eliminates manual operational work — the kind that follows a predictable pattern but still requires a person to push the buttons.
How we build it: Scheduled workflows, conditional routing, and multi-system triggers that execute deterministic logic without human intervention. When a deal closes, the finance system updates, the delivery team is notified, and capacity allocation adjusts — automatically.
Real outcome: A data sync that previously required manual export, transform, and import across three platforms now runs unattended on a schedule. The process hasn’t required manual intervention since deployment.
Layer 4
Custom Platforms
What it is: Purpose-built applications with their own interface, their own database, and their own business logic — not a HubSpot workflow configuration or a marketplace plugin.
How we build it: Full-stack engineering. Next.js and React for production web applications. Laravel and PHP for enterprise backend systems. PostgreSQL for purpose-built databases. We’ve built platforms that embed directly into existing tools — React-based interfaces that appear as native extensions within a client’s CRM.
Real outcome: For a professional services firm, we engineered a financial intelligence platform that lives inside their CRM interface. The platform extracts structured data from uploaded financial documents and surfaces insights on the same screen where the team manages client relationships.
Layer 5
AI-Assisted
Decision-Making
What it is: AI models that predict, classify, extract, and detect — the actual intelligence layer where trained models do work that rules and schedules cannot.
How we build it: Document classification that reads uploaded files and extracts structured data. Spending anomaly detection that flags patterns deviating from historical norms. Pattern recognition across datasets that surfaces trends a human reviewing spreadsheets would miss. These are trained models applied to specific business problems.
Real outcome: An AI-powered document extraction system reads uploaded financial documents, identifies key data fields, and populates structured records automatically. What previously required manual data entry now runs through a model that learns the document structure.
What’s in the Workshop
Platforms & Languages
- Next.js & React — Production web applications
- Laravel & PHP — Enterprise backend systems
- PostgreSQL — Purpose-built operational databases
- HubSpot APIs — CRM integration, custom CRM cards, private apps
- Python — Data pipelines, AI model integration
- Node.js — Serverless functions, webhook handlers
Infrastructure & Practices
- DigitalOcean & cloud hosting — Production deployment
- PM2 process management — Zero-downtime restarts
- Cron-based data sync — Scheduled pipelines, not fragile live calls
- Git-based CI/CD — Version-controlled deployments
- Drizzle ORM — Type-safe database access
- Claude AI integration — Document extraction, classification, analysis
What This Looks Like in Practice
Three real builds. Different problems. Same architectural approach.
Client type: Professional services firm (20+ staff)
Problem: Five business platforms, no single source of truth, 2+ hours/week on manual reporting
Stack: Layers 1–3 (Data Foundation + Intelligence + Automation)
Outcome: Unified database, 8-module dashboard, automated 4-hourly sync. Manual reporting eliminated entirely.
Client type: Financial advisory firm
Problem: Manual data entry from paper documents, high error rates, slow onboarding
Stack: Layers 4–5 (Custom Platform + AI Decision-Making)
Outcome: AI document extraction embedded inside existing CRM. Structured data populated automatically from uploaded files. Error rates dropped dramatically.
Client type: Growing B2B company
Problem: CRM, accounting, telephony, and project management data siloed. No cross-system visibility.
Stack: Layers 1–2 (Data Foundation + Intelligence Layer)
Outcome: Custom pipelines pull from all platforms on schedule. Leadership dashboard shows cross-system metrics. Decision-making moved from weekly spreadsheet reviews to real-time.
Two Approaches. Different Problems.
| Dimension | Engineering (NBH) | Configuration |
|---|---|---|
| Data Architecture | Purpose-built databases, scheduled pipelines | Platform’s built-in storage only |
| Integration Approach | Custom code, controlled sync schedules | Marketplace connectors, Zapier |
| AI Capability | Trained models for extraction, classification, detection | Built-in AI features (summarise, score) |
| Scalability | Own infrastructure, no platform limits | Bound by platform tier limits |
| Best For | Multi-system, cross-platform intelligence | Single-platform workflow automation |
Configuration is the right answer when your problem lives inside one platform. Engineering is the right answer when your problem lives between platforms. We do both — and we’ll tell you which one you actually need.
How We Work
Five phases. Each one has a clear deliverable. You know exactly what you’re getting and when.
We map your current systems, data flows, and operational bottlenecks. No assumptions — we look at what’s actually happening, not what the vendor said should happen. You get a systems audit document and a prioritised opportunity list.
We design the solution architecture: which layers of the stack you need, how your systems will connect, what the data model looks like, and where AI adds genuine value versus where simple automation is the right answer.
We build in sprints with visible progress. You see working software early, not a reveal at the end. Data pipelines, dashboards, platforms, and AI models are built incrementally so you can course-correct as we go.
Your team tests with real data. We verify every integration, every data flow, every edge case. Nothing goes live until you’ve confirmed it works the way your business actually operates — not the way a demo pretends it does.
We monitor, maintain, and optimise. Data pipelines are watched. AI models are tuned. When your business changes — new systems, new processes, new team structure — the platform adapts with you.
Security and Control
- Database isolation. Your data lives in its own database instance. Not a shared table with other clients. Not a multi-tenant SaaS platform. Your data, your schema, your access controls.
- Encrypted credentials. API tokens, access keys, and authentication credentials are stored encrypted on the server. Never in code. Never in config files committed to version control.
- Token-based authentication. Every user session, every API call, every inter-service communication is authenticated. No anonymous access to any endpoint.
- Audit logging. Every action an AI model takes is logged — what it read, what it wrote, what it decided. You can trace any output back to its source data and the model that produced it.
- Kill switch. Every AI process can be paused or shut down instantly. If a model produces unexpected output, your team can halt it before it touches downstream systems.
- No vendor lock-in. Your databases, your code, your infrastructure. If you stop working with us, you keep everything. We don’t hold your data or your platform hostage.
Frequently Asked Questions
What’s the difference between AI automation and custom AI systems?
AI automation uses pre-built tools to handle repetitive tasks inside a single platform. Custom AI systems connect multiple platforms, build unified data layers, and apply trained models to problems that pre-built tools can’t solve. We build both — and we’ll tell you which one you actually need.
Do I need HubSpot to work with you?
HubSpot is our home ground — we’re a Diamond Partner with 10+ years on the platform. But our AI Solutions work extends beyond HubSpot. If your problem involves connecting multiple business systems, we can help regardless of your CRM.
How long does a typical project take?
It depends on which layers of the stack you need. A data foundation build might take 4–6 weeks. A full intelligence platform with custom AI models is typically 8–12 weeks. We scope every project individually after the diagnostic phase.
Is my data safe?
Yes. Database isolation means your data lives in its own instance. All credentials are encrypted. Every AI action is logged with a full audit trail. You can pause any process instantly. We don’t export, scrape, or store your data externally.
What happens if an AI model makes a mistake?
Every AI process has a kill switch. Your team can halt any model instantly. All outputs are logged and traceable back to source data. For critical workflows, we build human-approval checkpoints where your team reviews before anything touches downstream systems.
How much does it cost?
AI Solutions projects are scoped per engagement. The cost depends on which layers of the stack you need and the complexity of your systems. Every project starts with a diagnostic so we can give you an accurate scope before any build work begins. Talk to us for a scoped proposal.
What if I already have AI tools that aren’t working?
That’s actually our most common starting point. Most businesses have invested in AI tools that don’t connect to their other systems. We audit what you have, identify what’s working and what isn’t, and either integrate your existing tools into a unified architecture or replace them with purpose-built solutions.
Can you just do the data foundation without the AI layer?
Absolutely. The capability stack is designed so each layer is independently valuable. Many clients start with Layer 1 (Data Foundation) and Layer 2 (Intelligence Layer) before adding AI. A unified database with proper dashboards often solves 80% of the problem.
Ready to Build Something Real?
Whether you need a data foundation, an intelligence dashboard, or a full AI-powered platform — it starts with a conversation about your systems, your data, and what’s actually broken.
Related HubSpot Services
- HubSpot Operations Hub Setup & Configuration
- Custom Coded Workflow Actions
- AI Agent Configuration (Breeze Agents)
- Lead Routing & Round-Robin Automation
- Quote-to-Cash Automation
- Automated Reporting & Alerts
- Data Migration & Transformation Workflows
- SLA & Escalation Automation
Learn more about AI Solutions
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