Build
Timeline
Discovery to live: 8-10 weeks.
What It Is
An AI-powered analytics dashboard is a centralized interface that pulls data from multiple business tools, applies machine learning to identify patterns and anomalies, and delivers plain-language summaries with recommended next steps. Unlike traditional dashboards that display charts and leave interpretation to the viewer, an AI dashboard reads the data, explains what changed, and tells your client what to do about it.
When Reports
Stop Getting Read
Your client’s marketing team sends you a report request on Tuesday. You pull data from GA4, HubSpot, and Google Ads into a spreadsheet. Two hours later, the numbers don’t match what the client sees in their own tools. You spend another hour defending your data instead of your strategy.
Someone sends the report on Friday. The client skims it. Nobody acts on it because the actionable piece got buried under three pages of charts.
This is what the data era actually looks like: spreadsheets that take hours to build, meetings spent debating which number is right, and clients making decisions on last week’s data because this week’s isn’t ready yet.
The Dashboard
Graveyard
Your client probably already has a dashboard. Or they had one. Someone built it, everyone used it for three weeks, then it broke when HubSpot changed their API. Nobody fixed it. The Monday report went back to the spreadsheet.
It still sits there, half-functional, a reminder that dashboards need maintenance, not just building.
We get it. You’ve seen it happen. The dashboard project feels solved until it’s not.
What the
AI Does
1
Summarization
Takes 50 data points across 5 systems and delivers a 3-line executive summary. “MRR up 12%. Churn in the highest-risk segment is up 4%. Lead quality dropped month-over-month.”
2
Trend Analysis
Identifies patterns in the data that wouldn’t be obvious in a static report — growth accelerating, slowdowns emerging, seasonal shifts. “This is the third month of declining sales velocity in the West region. Pattern started when Territory Manager 2 was promoted out.”
3
Anomaly Detection
Flags when something breaks its usual pattern. A customer who always pays on day 3 suddenly paying on day 28. A channel that converts at 8% suddenly at 3%. “Customer cohort from Q2 is trending toward 40% churn — 15 points higher than historical average.”
4
Recommendations
Doesn’t just show what happened. Suggests the next step based on what the data shows. “Pause spend in Meta Conversions campaign (CPA up 34%). Shift budget to DPA — lowest CAC increase, highest ROAS stability.”
5
Natural Language Queries
Your client asks the dashboard a question in English. No SQL. No clicking around looking for a chart. “Which accounts are at risk of churning in the next 30 days?” — Dashboard answers with a list and the early warning signals.
How It Works
Under the Hood
The system runs on three layers. The first is a data ingestion layer — authenticated connections to every tool your client uses, pulling data on a schedule you set (hourly, daily, or real-time). Each connector normalizes the data into a common format so Google Ads numbers and HubSpot numbers actually mean the same thing.
The second layer is AI processing. This is where large language models and statistical analysis work together. The LLM reads the normalized data, compares it against historical baselines, identifies what moved, and writes the summary in plain English. Rule-based logic handles anomaly thresholds and trend detection. The AI doesn’t hallucinate numbers — it only reports what the data shows.
The third layer is the dashboard interface — a custom-built front end your client logs into. Charts, filters, drill-down paths, natural language search. Branded with their logo, organized the way their team thinks, responsive on mobile. We build this on a modern stack (React front end, serverless API layer) so it loads fast and scales without maintenance headaches.
Seven Dashboards
You Can Sell
1
Business Owner Dashboard
CEO sees the thing that matters in 30 seconds. MRR, ARR, cash position, headcount, top three risks. Reads like a board brief for tomorrow’s decisions.
2
Sales Dashboard
The forecast spreadsheet finally dies. Pipeline health. Win rate by rep. Quota progress. Early warnings when deals are at risk. Reps see their own numbers against benchmarks.
3
Marketing Dashboard
Your client’s marketing manager stops defending channel spend and starts proving it. ROAS by source. CAC by campaign. Lead quality from first touch to closed deal. Automatic recommendations on what to pause.
4
Operations Dashboard
The ops manager sees operational health instead of activity. Process metrics. Time-to-close. Delivery timelines. Cash conversion cycle. Resource utilization. The things that actually need attention.
5
Client Reporting Dashboard
Pre-built dashboard your client white-labels for their own clients. GA4, ad spend, conversion data, all in one place. They send this instead of a PowerPoint and charge more because it’s better. This is where your recurring revenue grows.
6
Financial Dashboard
Real-time GL. Profitability by department. Cash flow forecast. Margin by customer. Pulls from QuickBooks, Xero, Netsuite. Finance teams stop guessing on cash position.
7
HR Dashboard
Headcount. Turnover risk. Tenure distribution. Hiring pipeline. Time-to-hire. Cost per hire. Early signals on retention risk.
AI Dashboard vs.
Traditional BI
| WLIQ AI Dashboard | Power BI / Tableau | Spreadsheet Reports | |
|---|---|---|---|
| Setup Time | 8-10 weeks, fully managed | Varies. Requires data team to build models, queries, views | Immediate, but manual every time |
| AI Summaries | Built in. Plain-language analysis on every refresh | Not native. Requires add-ons or custom development | None. Interpretation is 100% manual |
| Anomaly Detection | Automatic. Flags deviations from baseline | Possible with configuration. Not default | Only if someone spots it |
| Recommendations | Yes. “Pause this campaign. Shift budget here.” | No. Shows data, doesn’t suggest action | No |
| Maintenance | We handle it. API breaks, updates, monitoring | Client’s data team maintains it | Someone rebuilds it every week |
| White-Label | Yes. Your client never sees us | Microsoft or Salesforce branding visible | Your spreadsheet, your brand |
| Natural Language Queries | Yes. Ask questions in English | Limited. Copilot in Power BI is early | No |
| Best For | Agencies selling insights as a service | Companies with in-house data teams | One-off reports with low stakes |
Data Sources
We Connect
Accounting & Finance
QuickBooks Online, Xero, FreshBooks, Stripe, PayPal, Wave, ADP Payroll
CRM & Sales
HubSpot, Salesforce, Pipedrive, Close, Copper, Zoho CRM
Marketing & Analytics
Google Ads, Meta (Facebook), Google Analytics 4, Mailchimp, Klaviyo, Drift, Intercom
E-Commerce
Shopify, WooCommerce, Magento, Amazon Seller Central, BigCommerce
Project Management & Operations
Asana, Monday.com, Jira, Linear, Toggl, Clockify
HR & Payroll
Gusto, BambooHR, ADP Workforce Now, Workday, Lattice
Custom & Everything Else
Databases (PostgreSQL, MySQL), APIs (REST, GraphQL), CSV/Spreadsheet imports, Webhooks
Most connectors can be built in 2-3 days if we don’t have a direct integration.
3-5 Days
Discovery
Understand what the client is trying to decide. Which systems actually get used. Where the data currently lives. What decisions matter.
1-2 Weeks
Data Connection
Connect each source. Test the flow. Handle authentication. Make sure data is clean before the AI layer reads it.
1-2 Weeks
AI Processing
Build the logic. Define what “anomaly” means for this client. Write the summary rules. Set up the recommendation engine.
2-3 Weeks
Dashboard Development
Build the interface. Charts. Filters. Drill-down paths. Beautiful and usable, not cluttered.
1 Week
Testing
Your client uses it. You catch the “wait, that doesn’t make sense” moments. We fix them.
2-3 Days
Deployment
Goes live. We monitor the first two weeks for real-time issues.
What You Deliver
to Your Client
Data Connections
All sources authenticated and flowing on schedule (daily, hourly, real-time — your choice). No more manual exports.
AI Processing Layer
The logic that reads the data and decides what matters. Anomalies get flagged. Trends get identified. Recommendations get written and prioritized.
Custom Dashboard
The interface they log into. Branded with their logo. Organized the way their team thinks. Mobile-responsive. Fast.
Real-Time Updates
Data refreshes on a schedule you set. Dashboards never stale. Alerts push to Slack or email when something critical changes.
Real Engagement
Pattern
Case Study
Marketing Agency — Unified Client Reporting
A mid-size digital agency was spending 8-10 hours per week per account manager assembling client reports from six different platforms. By the time reports were sent, the data was already stale. Two clients had churned in the previous quarter citing “lack of visibility into performance.”
We built a Marketing Dashboard and a Client Reporting Dashboard. Data from Google Ads, Meta, GA4, HubSpot, and Shopify flows into a single interface. The AI layer writes the performance summary, flags underperforming campaigns, and recommends budget shifts.
8hrs
Saved per account manager per week
0
Client churn in the 6 months after
3
Additional dashboard upsells in first quarter
$4,200
New monthly recurring revenue per client
The agency started with one dashboard type. Within six months, they were selling three — Business Owner, Marketing, and Client Reporting — as a bundled analytics package.
Use Cases
by Industry
E-Commerce — Revenue + Inventory Intelligence
Connect Shopify, Amazon, and ad platforms into one view. AI tracks conversion trends, flags inventory risk, and recommends ad spend shifts based on product margin.
SaaS — MRR, Churn & Pipeline Tracking
Stripe + CRM + support data in a single dashboard. AI identifies churn risk before it shows up in the monthly report. Expansion revenue tracked alongside new business.
Professional Services — Utilization & Profitability
Time tracking, project management, and accounting data combined. Real-time margin by project, utilization by team, and early warnings when projects trend over budget.
Agencies — Client Reporting as a Revenue Stream
White-label the dashboard and sell it to your clients as a premium reporting service. Charge a monthly fee. Let the AI write the performance narrative your clients used to wait a week for.
FAQ
How is this different from Power BI or Tableau?
Power BI and Tableau are excellent tools. If your client has a data team or wants complete flexibility to build any query, they’re the right choice. The gap: Power BI and Tableau require someone to build the data model, write the queries, design the views, then maintain them when systems change. Your client still has to know what questions to ask.
We handle the build once. We maintain the connections. The AI writes the summary and recommendations. Your client logs in and sees what matters. We’re faster to deploy, easier to operate, and the AI layer does thinking a human would normally do.
One more thing: we’re white-label. Your client never sees us. Power BI puts Microsoft’s name in the interface.
Can it connect to our tools?
Probably. We have direct connectors to 40+ platforms. If your client uses Salesforce, HubSpot, QuickBooks, Google Ads, Shopify, or most major SaaS tools — yes. If they use something niche or proprietary, we scope it in discovery. Most custom connectors take 2-3 additional days.
Is this real AI or just charts?
Both. The charts are real and well-designed. The AI layer on top does what charts can’t: summarize, detect anomalies, identify trends, and recommend next steps. It’s not predictive AI that guesses the future. It’s reading what happened and explaining it faster and better than a human building a report by hand.
How long does the whole thing take?
Discovery to deployment: 8-10 weeks. Most of that is data connection and testing, not email loops. If you’re in a rush and willing to cut scope (fewer sources, simpler dashboard), we can do 5-6 weeks.
How much does a custom AI dashboard cost?
It depends on the number of data sources, the complexity of the AI logic, and how many dashboard views your client needs. Most projects land between $15K and $35K for the initial build. Ongoing maintenance is scoped separately. Send us the details and we’ll give you a number you can take to your client the same week.
Where does the dashboard live?
On our secure servers, behind your client’s login. We handle security, backups, uptime, and scale. They see it through a browser or mobile app. If your client has compliance requirements (healthcare, finance) and needs it on their own infrastructure, we can host it there instead.
Who maintains it?
We do. APIs shift. Systems update. Data sources change. We monitor the connections and fix breaks on our side. Your client owns the questions they ask. We own the technical layer. If your client wants to add a new data source in six months, we handle the integration. That’s part of the service.
Can multiple people use it?
Yes. Team dashboards, individual dashboards, read-only views for stakeholders. We handle user management. Your client owns who has access.
Can we extend it later?
All the time. Start with one dashboard, add three more in month four. Connect a new data source in month eight. This is where recurring revenue gets interesting. Your client starts with a Business Owner dashboard. In three months they ask for a Sales dashboard. In six months they want Client Reporting. You keep selling. We keep building.
What ROI can agencies expect from offering AI dashboards?
Two revenue paths. First, the project fee: you mark up the build and earn margin on a $15K-$35K engagement. Second, the recurring fee: most agencies charge their clients $2,000-$5,000 per month for the dashboard as a managed analytics service. Five clients on dashboards is $10K-$25K in monthly recurring revenue. The dashboard pays for itself inside the first quarter for most agencies.
Let’s Build This
You already have a client in mind. Someone whose decisions are buried in spreadsheets. Someone whose reports take too long and never quite land the way they should.
Send us three things:
- Who this is (and their role)
- Which systems they’re using
- The thing that makes everyone sigh (the report that takes too long, the data that never quite lines up, the forecast nobody trusts)
We’ll scope it out. Give you a timeline and number you can take to your client. You’ll know whether this is a $15K project or $35K or something in between.
If it doesn’t fit their situation, we’ll tell you that too. That’s how partnerships last.
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