Data Panda brings a geocoding feed together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
A geocoding API takes an address as a string and returns a structured result: a formatted address, separate components for street, number, postal code, city, region and country, a latitude and longitude pair, and usually a bounding box and a confidence score. Reverse geocoding does the opposite, turning a coordinate back into an address. Most providers cover the same global base map and differ on coverage depth in specific countries, refresh cadence and licensing.
The reason to land geocoded results in a warehouse is that addresses in your business systems were rarely typed by the same person twice. The same Brussels office shows up as three different rows in the CRM, the ERP and the shipping platform. A geocoding pass collapses those into one location, with stable lat-long that downstream models, dashboards and route planners can join on.
Sales, deliveries and accounts grouped by region the way the map sees them, not by whatever someone typed.
Every new contact, lead or supplier passes through geocoding before it lands in the downstream systems.
Give models a stable location feature instead of a string they cannot reliably parse.
Internal tools that show your customers, sites and suppliers on a map your team trusts.
A list of concrete reports, automations and AI features we have built on Geocode API data. Pick the one that matches your situation.
How many of our customer records are the same place?
Every customer address goes through the geocoding feed and the lat-long is rounded to a small grid. Records that land on the same grid cell get flagged as candidates for merge, with the source system and entry date next to each one. The duplicate count usually surprises sales and finance both.
Where are our deliveries expensive?
Order addresses are geocoded and joined to carrier cost per shipment. Cost per kilometre and per drop is plotted by region, with the postcodes and routes that drag the average up listed separately. The conversation moves from a flat freight line in the books to a map of where the margin leaks.
Are our sales territories matching the customer base?
Active customer locations are mapped against the territory each rep currently owns. Accounts that sit closer to a different rep or depot than to their assigned one show up as candidates to reassign, with the revenue and visit history attached so the trade-off is visible.
One canonical customer and supplier per location, so debit and credit notes stop landing on the wrong record. Freight and travel costs broken out by region using addresses that match the carrier invoice.
Territory maps and account-density views built on real coordinates, not postcode prefixes. New leads appear next to the closest rep and the existing accounts in their patch, so handover happens without a meeting.
Routing, dispatch and field service start from a list of clean coordinates per stop. Failed deliveries caused by typoed addresses go down, and depot planning has the customer map it needs before the next location decision.
New and updated HubSpot contacts and companies pass through geocoding before they reach the warehouse. Each record gets a clean address, structured components and a lat-long that lets sales spot duplicates and assign territories on real geography instead of on postcode prefixes.
Salesforce account and contact addresses are geocoded and pushed back as enriched fields. Territory planning runs on coordinates and drive time instead of free-text fields, and account managers stop tripping over three versions of the same client site.
Exact Online debtor and creditor addresses are geocoded and joined to the same lat-long key. Finance gets a deduplicated master list, freight cost per region falls out cleanly, and credit notes stop landing on a parallel record for the same site.
Sales orders in Business Central pass their ship-to address through geocoding before they leave the warehouse. Failed deliveries caused by typoed streets or missing postcodes go down, and the route planner gets coordinates instead of strings to interpret.
Service-job items in monday.com get a lat-long and the nearest depot pre-filled from geocoded customer addresses. Dispatchers see the right team for each job at a glance, and the board reflects the same geography the field engineers drive.
When a new customer or order address fails to geocode at street level, a Slack note lands in the channel that owns the source system. The data-quality conversation happens at the moment the bad record is created, not weeks later in a cleanup project.
You keep the reporting tool you already have. We connect it to the warehouse where your Geocode API data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
We review your Geocode API setup and the systems around it. Together we pick the first thing worth building.