Data Intelligence

Make Smarter Decisions With the Data You Already Have.

No Data Team Required.

We build AI-powered analytics and dashboards that turn your existing business data into clear, actionable insights. Stop guessing and start knowing, without hiring analysts or learning complex tools.

Book a scoping call Take the free AI audit

Hours to minutes
Report generation time: No data team
Needed to get started: Actionable insights
Not just charts

Your Data Shouldn't Sit There Collecting Dust

Data Exists But Nobody Analyzes It

You're sitting on months or years of sales data, customer records, and transaction history. But without a data analyst on staff, it just sits there unused.

Decisions Made on Gut Feeling

Without clear data insights, you're making pricing, inventory, and marketing decisions based on intuition. Sometimes you're right, but you don't know when you're wrong.

Reports Take Days to Compile

Pulling numbers from spreadsheets, CRMs, and accounting software takes hours of manual work. By the time the report is done, the data is already stale.

What we build for data intelligence teams

Each of these is a real engagement shape. Open one to see how it works, what it needs from you, and where a person stays in the loop.

Any figures are targets we scope towards, not guarantees. What you actually get depends on your volume, your systems, and how much stays under human review.

Automated Reporting Dashboards

You spend hours every week pulling data from different tools into spreadsheets just to understand how your business is performing.

Real-time dashboards that automatically pull data from your existing sources (POS, CRM, accounting software) and present key metrics in one place, updated continuously.

How it works

01We connect to your existing data sources: CRM, accounting software, POS, spreadsheets, and databases
02Design custom dashboards around the metrics that matter most to your business
03Automate data collection and transformation so dashboards stay up-to-date in real time
04Set up alerts for key thresholds (e.g., revenue drops below target, inventory runs low)
05Deliver daily or weekly email summaries so you don't even have to log in

Before and after

E-commerce business tracking sales across multiple channels

Before: Owner spent 5+ hours every Monday pulling data from Shopify, Amazon, and QuickBooks into a spreadsheet to understand weekly performance. Numbers were often inconsistent due to manual errors.
After: Automated dashboard consolidates all channel data in real time. Owner opens one screen to see revenue, margins, top products, and inventory levels. Monday reporting time dropped from 5 hours to 5 minutes.

What it needs from you

Access to data sources (CRM, accounting software, POS, spreadsheets)
Admin credentials for API connections (we walk you through it)
No special hardware or software needed. Dashboards are web-based
Works with most common business tools out of the box

Common questions

What data sources can you connect to?: Most common business tools: QuickBooks, Xero, Shopify, WooCommerce, HubSpot, Salesforce, Google Analytics, Square, Stripe, and many more. If it has an API or can export data, we can likely connect to it.
How often do the dashboards update?: Most dashboards update in real time or near-real time (within minutes). For sources that don't support live connections, we set up scheduled syncs: hourly, daily, or whatever makes sense for your business.
Can I customize what I see?: Absolutely. We build dashboards around your specific KPIs. During setup, we work with you to identify the metrics that matter most and design views that answer your key business questions.

Typical build: 1-2 weeks·Aim: Real-time business visibility

Talk through this one

Customer Sentiment Analysis

You have hundreds of reviews, support tickets, and feedback messages but no way to spot trends or understand what customers really think at scale.

AI that analyzes reviews, support tickets, survey responses, and social mentions to surface sentiment trends, recurring complaints, and opportunities you'd otherwise miss.

How it works

01Connect to your review platforms, support ticketing system, and survey tools
02AI categorizes feedback by topic, sentiment (positive/negative/neutral), and urgency
03Surface trending themes: what customers love, what frustrates them, what they're asking for
04Set up alerts for sudden sentiment shifts or spikes in negative feedback
05Generate monthly reports with actionable recommendations based on customer voice

Before and after

E-commerce brand with 500+ reviews per month across multiple platforms

Before: Marketing team manually skimmed reviews but couldn't keep up. A recurring complaint about packaging went unnoticed for 3 months, leading to increased returns and negative word-of-mouth.
After: Sentiment analysis flagged the packaging issue within the first week. After fixing it, return rate dropped 18% and average review score improved from 3.8 to 4.3 stars over the following quarter.

What it needs from you

Access to review platforms (Google, Yelp, Amazon, etc.)
Support ticketing system access (Zendesk, Freshdesk, Intercom, etc.)
Optional: social media account access for mention monitoring
Optional: survey tool integration (Typeform, SurveyMonkey, etc.)

Common questions

How accurate is the sentiment analysis?: Our models achieve 85-90% accuracy on sentiment classification. We fine-tune for your industry and business context, which improves accuracy over time. Edge cases are flagged for review rather than misclassified.
Can it handle sarcasm and nuance?: Modern AI handles most sarcasm and context well, though it's not perfect. We configure confidence thresholds so ambiguous cases are flagged rather than misclassified. Accuracy improves as the system learns your specific customer language.
What languages are supported?: We support sentiment analysis in English, Spanish, French, German, and most major languages. Multi-language analysis is included at no extra cost.

Typical build: 1-2 weeks·Aim: Understand customer feelings at scale

Talk through this one

Competitive Intelligence

You know competitors exist but have no systematic way to track their pricing, positioning, new offerings, or customer sentiment.

Automated monitoring of competitor pricing, reviews, product launches, and market positioning, delivered as actionable briefings so you can respond strategically.

How it works

01Identify your key competitors and the signals that matter (pricing, reviews, new products, hiring)
02Set up automated monitoring of public data: websites, review platforms, job postings, press releases
03AI analyzes changes and surfaces what's actually meaningful vs. noise
04Deliver weekly or monthly competitive briefings with context and recommended actions
05Alert you immediately when significant competitive moves happen

Before and after

Real estate brokerage tracking market trends and competitor positioning in their region

Before: Agents relied on word-of-mouth and occasional manual checks of competitor websites. They missed a competitor's aggressive price reduction and lost 3 listings before realizing what happened.
After: Automated tracking flagged the competitor's pricing change within 24 hours. Brokerage adjusted their value proposition and marketing messaging immediately, retaining existing clients and winning back market share.

What it needs from you

List of competitors to monitor
No technical setup required on your end
We handle all data collection and analysis
Optional: industry-specific data sources for deeper analysis

Common questions

Is this legal?: Yes. We only monitor publicly available information: websites, published reviews, public job postings, press releases, and social media posts. This is standard competitive intelligence, not hacking or scraping private data.
How many competitors can you track?: We typically start with 3-5 key competitors and expand from there. There's no hard limit, but focusing on your most relevant competitors yields the best insights.
How is this different from Google Alerts?: Google Alerts sends you raw links. We analyze the content, identify what's actually significant, and deliver actionable insights with context. You get "Competitor X dropped prices 15% on their core product, and here's what that means for you" rather than a list of URLs.

Typical build: 2-3 weeks·Aim: Know what competitors are doing

Talk through this one

Sales Forecasting

You're planning inventory, staffing, and budgets based on rough estimates. Overstock wastes money; understock loses sales.

AI models that analyze your historical sales data to predict revenue trends, seasonal patterns, and demand shifts, so you can plan with confidence.

How it works

01Analyze your historical sales data (ideally 12+ months) to identify patterns and trends
02Factor in seasonality, day-of-week patterns, promotional impacts, and external variables
03Build forecasting models calibrated to your specific business dynamics
04Generate weekly or monthly forecasts with confidence intervals (not just single numbers)
05Continuously refine predictions as new data comes in and accuracy improves over time

Before and after

Retail business struggling with inventory planning across 200+ SKUs

Before: Buyer ordered inventory based on gut feeling and last year's rough numbers. Regularly had 20% overstock on slow items and stockouts on popular items during peak periods.
After: AI forecasting model predicted demand by SKU with 82% accuracy. Overstock reduced by 35%, stockouts cut in half. The business freed up $40K in working capital previously tied up in excess inventory.

What it needs from you

Historical sales data (minimum 6 months, ideally 12+ months)
POS or e-commerce platform access for ongoing data feeds
Optional: marketing calendar for promotional impact modeling
Optional: external data sources (weather, economic indicators) for advanced models

Common questions

How accurate are the forecasts?: Accuracy depends on data quality and business complexity. Most clients see 75-85% accuracy at the product level and 85-95% accuracy at the aggregate level. We always provide confidence intervals so you know the range, not just a single number.
How much historical data do I need?: Minimum 6 months for basic forecasting. 12+ months is ideal because it captures seasonal patterns. If you have 2+ years, we can build more sophisticated models. We'll be honest about what's possible with the data you have.
What if my business is new or changing rapidly?: We can still help, but we'll set realistic expectations. For newer businesses, we start with simpler models and refine as more data accumulates. We might also supplement with industry benchmarks and comparable business data.

Typical build: 2-3 weeks·Aim: Predict revenue with confidence

Talk through this one

Customer Behavior Analysis

You know who your customers are, but not why they buy, when they leave, or what drives their lifetime value.

AI that analyzes purchase patterns, engagement history, and customer journeys to identify churn risk, upsell opportunities, and what makes your best customers tick.

How it works

01Connect to your CRM, e-commerce platform, and transaction history
02Segment customers by behavior: purchase frequency, recency, monetary value, and engagement
03Identify churn risk signals and flag at-risk customers before they leave
04Calculate customer lifetime value and identify what drives your best customers
05Generate actionable recommendations: who to reach out to, what to offer, and when

Before and after

Accounting firm wanting to reduce client churn and identify growth opportunities

Before: Partners didn't realize clients were disengaging until they received a cancellation email. No systematic way to spot at-risk clients or identify upsell timing.
After: Behavior analysis flagged 12 at-risk clients based on declining engagement patterns. Proactive outreach retained 9 of them. Also identified 15 clients likely to benefit from advisory services, generating $45K in new annual revenue.

What it needs from you

CRM or customer database access
Transaction or purchase history (6+ months)
Optional: email engagement data for fuller picture
Optional: support ticket history for churn signal analysis

Common questions

What's the difference between this and my CRM's built-in reports?: CRM reports show you what happened. Our analysis tells you what's likely to happen next and what to do about it. We use AI to identify patterns, predict churn, and recommend specific actions, not just display historical data.
How do you predict churn?: We analyze patterns that precede churn in your historical data: declining purchase frequency, reduced engagement, support ticket spikes, etc. The model learns what "about to leave" looks like for your specific business and flags at-risk customers early.
Is my customer data safe?: Absolutely. We use encryption in transit and at rest, follow data minimization principles, and never share or sell your customer data. We can sign data processing agreements and comply with GDPR, CCPA, and other privacy regulations.

Typical build: 2-3 weeks·Aim: Know your customers deeply

Talk through this one

Document Data Extraction

Critical business data is locked inside PDFs, invoices, contracts, and scanned documents. Manually entering it into systems takes forever and introduces errors.

AI that reads and extracts structured data from unstructured documents (invoices, receipts, contracts, forms) at scale, with human-level accuracy.

How it works

01Define the data fields you need extracted from each document type (e.g., invoice number, line items, totals)
02Train the extraction model on a sample of your actual documents for maximum accuracy
03Set up automated ingestion: email attachment, folder upload, or API integration
04AI extracts data and outputs it in your preferred format (spreadsheet, database, or directly into your systems)
05Confidence scoring flags low-certainty extractions for human review instead of guessing

Before and after

Accounting firm processing hundreds of client invoices and receipts monthly

Before: Junior staff spent 30+ hours per month manually entering invoice data into accounting software. Error rate was 3-5%, leading to reconciliation headaches and occasional compliance issues.
After: AI extracts invoice data automatically with 97% accuracy. Processing time dropped from 30 hours to 3 hours (mostly reviewing flagged items). Error rate dropped below 1%, and staff now focus on higher-value advisory work.

What it needs from you

Sample documents for initial model training (10-20 per document type)
Defined data fields you need extracted
Optional: destination system access for direct data loading
Works with PDFs, scanned images, photos, Word docs, and most document formats

Common questions

How accurate is the extraction?: Typically 95-98% accuracy after training on your specific document types. We use confidence scoring, so uncertain extractions are flagged for human review rather than entered incorrectly. Accuracy improves over time as the model sees more of your documents.
Can it handle handwritten documents?: Yes, with caveats. Printed text and typed documents achieve the highest accuracy (97%+). Clear handwriting typically achieves 85-90% accuracy. Very messy handwriting may need manual review. We'll be upfront about what to expect with your specific documents.
What formats do you support?: PDF, scanned images (JPEG, PNG, TIFF), Word documents, Excel files, and most common document formats. We can also process photos taken with a phone camera, though scan quality affects accuracy.

Typical build: 1-2 weeks·Aim: Structured data from any document

Talk through this one

Questions we get

What data sources do you support?

We connect to most common business tools: QuickBooks, Xero, Shopify, WooCommerce, HubSpot, Salesforce, Google Analytics, Square, Stripe, PostgreSQL, MySQL, Excel/CSV files, Google Sheets, and many more. If your tool has an API or can export data, we can almost certainly work with it.

How accurate are the predictions and insights?

Accuracy varies by use case. Sales forecasting typically achieves 75-85% accuracy at the product level and 85-95% at the aggregate level. Sentiment analysis runs 85-90% accuracy. Document extraction hits 95-98%. We always provide confidence intervals and are transparent about limitations. We'd rather give you a realistic range than a false sense of precision.

How do you handle data privacy and security?

We use end-to-end encryption for all data in transit and at rest. We follow data minimization principles: we only access the data needed for your specific use case. We never share, sell, or train general models on your data. We can sign data processing agreements and comply with GDPR, CCPA, and industry-specific regulations.

How often are dashboards and reports updated?

Most dashboards update in real time or near-real time. For data sources that don't support live connections, we set up scheduled syncs (hourly, daily, or weekly). Forecasting models are retrained monthly or quarterly depending on your needs. You can also request on-demand refreshes anytime.

Can I customize the dashboards and reports?

Absolutely. We build dashboards around your specific KPIs and business questions. During setup, we work with you to identify the metrics that matter most. After launch, you can request changes and additions as your needs evolve, and that's included in your monthly service.

Does this integrate with existing BI tools like Tableau or Power BI?

Yes. If you already use Tableau, Power BI, Looker, or similar tools, we can feed our AI-processed data into them. We can also build standalone dashboards if you don't have existing BI tools. Many small businesses find our built-in dashboards are all they need.

What kind of ROI can I expect?

ROI depends on your specific use case. Common outcomes include: 5-10 hours per week saved on manual reporting, 15-35% reduction in overstock through better forecasting, 10-20% improvement in customer retention through behavior analysis, and significant time savings from document extraction. We'll give you realistic estimates during your consultation based on your specific situation.

Do I need any technical skills to use this?

No. We handle all the technical setup, integration, and ongoing maintenance. You interact with simple dashboards and receive reports in plain language. If you can read a chart and open an email, you can use our data intelligence tools. We also provide a walkthrough so your team feels confident from day one.

How long does it take to get started?

Most implementations take 1-3 weeks depending on the use case. Simple dashboards can be live in a week. More complex projects like forecasting models or multi-source integrations typically take 2-3 weeks. We'll give you a clear timeline during your consultation.

What if my data is messy or incomplete?

That's normal. Most small business data isn't perfectly clean. Part of our service is data cleaning and normalization. We'll be honest about what your data can and can't tell you. Sometimes the first step is just getting your data organized, which itself provides immediate value.

Ready to Unlock the Value in Your Data?

Book a free consultation to see how AI-powered analytics can work for your specific business. We'll review your data sources and show you what's possible.

Book a scoping call