AI Consulting
The model was never the hard part. Connecting it to your CRM, ads, and data is.
Every business now has access to the same AI. Claude, GPT, Copilot — commodity. What separates the businesses actually getting value from AI is whether it can see and act across their CRM, ad platforms, SEO tools, accounting software, customer support, and the dashboards and spreadsheets everyone pretends don't run the company. We build that layer.
Schedule a Call →Buying a Claude or ChatGPT subscription doesn't give you an AI-powered business. It gives your team a smarter search bar. The value only shows up when that intelligence has full context — when it can see a lead in your CRM, check what ad brought them in, look at their support history, and know your margins before it recommends anything.
That context doesn't exist by default. It's locked in a dozen disconnected tools, each with its own login, its own data model, and no idea the others exist. Our job is building the integration layer that gives AI — any AI — a single, current, connected view of your business. The model is the engine. We build the wiring.
One system of record, not twelve silos
Connect your CRM, marketing and sales platforms, accounting software, support desk, and reporting spreadsheets into a single data layer AI can actually query — instead of an AI tool per app that only sees a fraction of the picture.
AI that acts, not just answers
Once systems are connected, AI stops being a chat window and starts being an operator — updating the CRM when a deal closes, flagging churn risk from support tickets, adjusting ad spend based on real margin data, without a human relaying information between tools.
Full context, every time, across every model
Because the integration layer is model-agnostic, you’re never locked into one vendor. Switch from Claude to GPT to Copilot to whatever’s next — the connected data and workflows underneath stay exactly the same.
Most companies don't have an AI problem. They have an integration problem.
01
The model isn’t the bottleneck.
Every frontier model can write copy, summarise a call, or answer a question. That capability is now free or nearly free. It stopped being a differentiator the moment three labs shipped equally good versions.
02
Better prompting isn’t a strategy.
Prompt engineering optimises what you already told the AI. It doesn’t get the AI closer to what’s actually happening in your business — your pipeline, your ad spend, your ticket queue — because none of that data reaches the model in the first place.
03
Disconnected tools cap what AI can do for you.
An AI chatbot that can’t see your CRM will hallucinate order status. A reporting tool that can’t see ad spend will miss why revenue moved. Every AI use case is capped by how much of your business it can actually see.
"The AI isn't the moat. The plumbing is."
AI integration, end to end
Connect
CRM ↔ Everything
Wire your CRM (HubSpot, Salesforce, Pipedrive) to ad platforms, support tools, and finance software so lead source, deal status, and customer value live in one place AI can query.
Ad Platforms ↔ Revenue Data
Connect Google Ads, Meta, and LinkedIn spend data directly to your CRM and accounting system, so AI can optimise toward actual profit, not just click-through rate.
Support ↔ Product & Sales
Feed support tickets and chat logs into the same system as sales and product data, so AI can spot churn risk, feature requests, and upsell signals your team would otherwise never connect.
Unify
Central Data Warehouse
The foundation underneath everything else: APIs and data ingestion pipelines pull records out of your CRM, ad platforms, accounting software, and support tools into one central warehouse. AI doesn’t query twelve live systems with twelve sets of permissions — it queries one clean, structured source of truth.
Spreadsheet Consolidation
Bring the "shadow system" — the spreadsheets your ops, finance, and marketing teams quietly run the business on — into the same warehouse instead of leaving them as blind spots.
Unified Reporting Layer
One dashboard fed by the warehouse, so leadership sees a single number for CAC, LTV, and pipeline instead of reconciling five exports by hand.
This is analytics infrastructure, not just AI infrastructure.
The central warehouse we build for AI integration is the same foundation our Analytics practice runs on.
Automate & Act
Cross-System Workflows
Automations that read from one system and write to another: a closed deal in the CRM updates the accounting system, triggers a support onboarding sequence, and adjusts ad targeting — automatically.
AI Agents With Full Context
Chat and voice agents that don’t just answer from a script — they check CRM status, order history, and account value before responding, because the integration layer gives them access.
Build
MCP & API Integration
Model Context Protocol and custom API work that gives any AI model — Claude, GPT, Copilot, or whatever you standardise on next — secure, structured access to your internal systems.
Custom Internal Tools
Purpose-built dashboards and apps for teams who’ve outgrown generic software, built on top of the same connected data layer.
Customer-Facing AI
AI Chatbot
Intelligent chat agents trained on your product, docs, and FAQs — with real-time access to CRM and order data, not just a static knowledge base. Handle support, qualify leads, and guide users 24/7.
Customer Service AI
Support automation that reads the full customer record — order history, past tickets, account value — before it resolves or escalates a ticket, because the integration layer already connected it.
Outbound Sales AI
Outbound sequences that draw on live CRM and firmographic data to personalise at scale and book meetings — instead of running from a static list.
Industry benchmarks
12+
average number of disconnected tools a mid-size business runs before we start
1
unified data layer we connect them into, queryable by any AI model
0
vendor lock-in: switch models any time without rebuilding your integrations
What clients say about our data work
"Jarrah built us an end-to-end data warehouse, bringing all our marketing and product data into BigQuery."
Nick Keyes
VP Marketing · DocuSketch
"Jarrah developed sophisticated attribution and LTV models and built robust data infrastructure."
Rob Dove
Director of Product · Vetster
AI Consulting FAQs
What is AI integration consulting? +
It’s the work of connecting your business systems — CRM, ad platforms, accounting software, support tools, spreadsheets — into a single data layer that AI models can query and act on. Most "AI consulting" focuses on the model itself; we focus on giving that model full context by wiring your systems together.
Isn’t this just prompt engineering with extra steps? +
No — prompting only shapes how AI responds to what it’s already been told. Integration determines what it’s told in the first place. We focus on the second problem, because it’s the one that actually limits what AI can do for your business.
We already pay for Claude/ChatGPT — why do we need this? +
A model subscription gives your team access to intelligence. It doesn’t connect that intelligence to your CRM, ad accounts, or support desk. Most businesses we talk to have the model and none of the plumbing — which is exactly why they aren’t seeing the ROI they expected.
Which systems can you connect? +
Most modern SaaS platforms with an API or native integration — CRMs (HubSpot, Salesforce, Pipedrive), ad platforms (Google, Meta, LinkedIn), accounting software (Xero, QuickBooks), support tools (Zendesk, Intercom), and yes, spreadsheets.
Are we locked into one AI model once this is built? +
No — that’s the point. The integration layer sits between your systems and whichever model you use. Swap Claude for GPT for Copilot without touching the underlying connections.
Do you build a full data warehouse, or just connect APIs directly? +
Depends on scale. For a handful of systems, direct API connections are often enough. Once you’re past 4-5 systems, we typically recommend centralising into a proper data warehouse (BigQuery, Snowflake) — it’s more reliable, easier to govern permissions on, and the same infrastructure your reporting and analytics already benefit from.
How long does an integration project take? +
A single system-to-system connection (e.g. CRM ↔ ad platform) typically takes 1–2 weeks. A full unified data layer across 5+ systems is scoped per project, usually 4–8 weeks.
Get started
Ready to connect the systems AI needs to actually help?
Tell us which tools your team runs on and we'll map out what's disconnected — and what it would take to fix it.
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