GIS-Point

GIS-Point GIS-Point is a UK-based GIS company with over 8 years of experience.

We deliver custom web and mobile GIS solutions and data processing, helping industries such as agriculture, logistics, construction, etc, scale with precise geospatial technology.

GIS software built by engineers who've done the job.Most GIS development projects lose the first three months to onboard...
13/05/2026

GIS software built by engineers who've done the job.

Most GIS development projects lose the first three months to onboarding. Developers learn what CRS means, why topology matters, and how a spatial database differs from a relational one. That time comes off the client's budget and timeline.

Our team starts without it. Every engineer holds a BSc in geospatial science or geodesy. Before building software for geospatial workflows, they worked inside them, through Mirnychyj Engineering Group, the parent company running geodetic field operations since 2009.

The result is a team that doesn't need geospatial explained. They've processed the data, and now write pipelines for.

390+ projects across 12 countries.
Under 7% rework rate, tracked per deliverable.

This isn't a dev shop that learned to use a spatial database. It's a GIS company that also builds the software.

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GIS-Point has completed the first phase of the AgriTech Business Accelerator, a program for Ukrainian AgriTech companies...
06/05/2026

GIS-Point has completed the first phase of the AgriTech Business Accelerator, a program for Ukrainian AgriTech companies focused on entering the US market.

16 companies were selected to participate. GIS-Point is among them.

The online phase ran from April 9 to April 30 — a series of sessions led by Gauthier Vasseur (Fisher Center for Business Analytics - UC Berkeley , UC Berkeley) and Rhonda Shrader (Haas School of Business - UC Berkeley , University of California, Berkeley). The curriculum covered Lean Startup methodology, data-driven decision-making, Business Model Canvas, and the specifics of pitching to American investors.
On April 28, participants gathered in Kyiv for an in-person pre-brief ahead of the US trip.

Next week, Phase 2 begins: 10 days in California, May 11–22—UC Berkeley lectures, go-to-market and pitching workshops, business visits, and networking in Silicon Valley.

For us, this is not a credential. It is focused work. Learning from the best. Testing assumptions. Moving forward.

The program is made possible by the support of the US Government through the Agriculture and Rural Development Program (AGRO), implemented by Chemonics International, in partnership with the Fisher Center for Business Analytics at UC Berkeley and the IТ Ukraine Association .

On May 14, 2026, the Львівська політехніка will host a hands-on educational event dedicated to 3D city modelling and di...
06/05/2026

On May 14, 2026, the Львівська політехніка will host a hands-on educational event dedicated to 3D city modelling and digital twin technologies. The organizers are Japanese company Eukarya and UNIDO - United Nations Industrial Development Organization

Eukarya are the developers of the Re:Earth platform and participants in Japan's national PLATEAU project: a large-scale programme for 3D city modelling that already covers hundreds of Japanese municipalities. This is a team that has walked this path in practice.

The event focuses on applied questions: how digital twins are built, how to work with 3D data in a web environment, and how Japanese experience can be adapted to the Ukrainian context.

Our team, GIS-Point and ТзОВ "Мірничий" is a partner of the event. For over a year, we have been working on a pilot project for 3D modelling of the city of Horodok (Lviv region), one of the first practical steps toward smart city systems in Ukraine.

This event in Lviv is the next step: learning from a team that has already scaled what we are building now. The strategic goal is a methodology and infrastructure for the digitalisation of cities across Ukraine.

01/05/2026

Web-based 3D point cloud visualisation: from classified LAS to browser.

Most point cloud workflows end with a file transfer. The surveyor captures the data. The client downloads a LAS, opens it in a desktop application, works with it locally. The web delivery layer, the ability to query, inspect, and share point cloud data through a browser, is rarely built.

We built it.

A geospatial technology provider needed their engineering and GIS teams to access high-volume point cloud datasets without heavyweight desktop installations. Billions of points. Real-time interaction. Browser-only access.

What we delivered:
• A scalable 3D viewer built on Potree and Angular, with a Python backend and SQL Server for metadata management.
• PotreeConverter processes raw LiDAR data into an optimised octree format, near-instant loading, smooth interaction at scale.
• JWT authentication, role-based access control, and secure data delivery for high-value geospatial assets.
• Interactive tools in-browser: volume measurement, height profiling, clipping, layer-based class visibility, annotation.
• Flexible deployment, AWS, GCP, or on-prem, cloud-agnostic by design.

The platform is now used for city planning, environmental monitoring, and infrastructure inspection: by surveyors, GIS analysts, and engineers who previously needed specialist desktop software to do the same work.

The delivery model: structured LiDAR processing pipeline, then a web layer the end client can use without installing anything.

What does your current client delivery workflow look like for point cloud data?

Location assessment that took weeks now takes minutes.A European retail chain was evaluating sites for rapid network exp...
29/04/2026

Location assessment that took weeks now takes minutes.

A European retail chain was evaluating sites for rapid network expansion.
The problem: mobility data, population density figures, and competitor footprints were scattered across departments and formats.
Each potential location required weeks of manual field surveys and fragmented spreadsheet analysis.

We built a GeoAnalytics platform that replaced that process entirely.

The platform consolidates geospatial datasets, mobility analytics, and business intelligence into a single environment. Decision-makers now assess new store opportunities through automated spatial analytics, not field trips and manual review.

What we delivered:
— Pedestrian and vehicle flow heatmaps across four radius buffers (500–1,500 m)
— H3-grid spatial aggregation for nighttime residents and daytime worker density
— Dynamic demand scoring and competitive density mapping
— Exportable reports with XLSX/CSV outputs for downstream planning

Stack: PostgreSQL + PostGIS, H3 grid, Python (GeoPandas, Dask), FastAPI, React + MapLibre + Deck.gl, Docker, AWS.

The analysis that previously required weeks of field work now runs in minutes. Site selection has shifted from intuition to spatial evidence.

If your site selection still depends on field surveys and spreadsheets, a full project breakdown is in the comments. What does your current location evaluation process look like?

Over a year ago, Eukarya, a Japanese company behind Re:Earth, a browser-based platform for 3D city data, approached GIS-...
23/04/2026

Over a year ago, Eukarya, a Japanese company behind Re:Earth, a browser-based platform for 3D city data, approached GIS-Point with an idea.

That conversation became the foundation for something larger.
For over a year, our team has been working on the first Smart City pilot of its kind in Ukraine, and the work is still ongoing.
Hundreds of engineering hours have already been invested in a real implementation in Horodok, Lviv region, led by GIS-Point together with Eukarya and ТзОВ "Мірничий", supported by UNIDO - United Nations Industrial Development Organization under the Green Industrial Recovery for Ukraine programme.

This is not just about implementing Smart City solutions. It is also a strategic step towards Ukraine's future: preparing cities for digital transformation and ensuring they can fully benefit from Smart City technologies as part of the country's reconstruction.

On 28 April, as part of this ongoing collaboration, Eukarya's founders will present at an international online seminar organised by the ГО "ГІС - Кадастрова асоціація України", where GIS-Point and Mirnychyj Engineering Group will participate as partners too.
During the session, Eukarya's founders will share insights from the Japan PLATEAU project and how the same methodology is now being applied across Ukrainian cities.

The seminar is free and open to GIS professionals, urban planners, municipal teams, and anyone working on spatial data infrastructure in Ukraine.

Registration link in the first comment.

07/04/2026

Digitizing Bridge Engineering Data in Just 35 Hours

Our partner faced a common challenge in infrastructure projects: a large set of bridge engineering data existed only on paper and PDF drawings, marked with manual notes of damages.

They needed an updated digital model for engineering analysis, repair planning, and seamless integration into BIM/GIS environments, without additional hiring or internal processing delays.

Our GIS-Point Approach

We avoided manual entry and partial automation, choosing a structured, step-by-step digitization workflow with geometric referencing:
1. Digitizing PDF drawings with manual damage annotations.
2. Interpreting engineering marks and structuring the data.
3. Updating DWG files with precise geometric alignment.
4. Creating an accurate digital model of the structure’s current state.

Ex*****on & Insights

• Approximately 35 hours of work processed the entire dataset at an average speed of 100 m²/hour.
• Identified unclear or conflicting annotations required a clarification protocol, enhancing the model’s accuracy and reliability.
• Our workflow eliminated the need for extra personnel, accelerating decision-making.

Results

• A precise, structured CAD foundation for engineering analysis and repair planning.
• Ready integration into BIM/GIS workflows without additional conversions.
- Reduced risk of data loss, shortened processing times, and minimized internal overheads.
• The client received a high-accuracy digital model quickly, without hiring additional specialists.

At GIS-Point, we transform unstructured engineering data into ready-to-use digital models, enabling faster, smarter, and more cost-effective decisions.

If your company’s data still “lives” on paper or in PDFs, we can help digitize it with precision and seamless BIM/GIS integration!

Happy upcoming Easter from the GIS-Point team! ✨To our valued customers, partners, and friends, may this season bring re...
03/04/2026

Happy upcoming Easter from the GIS-Point team! ✨
To our valued customers, partners, and friends, may this season bring renewed energy, fresh perspectives, and peace to your homes.
We are grateful for your trust and look forward to reaching new heights together.

Wishing you a joyful and blessed holiday! 🐣

Turn Your Fleet Into a Smart Machine: AI That Sees Everything on the MapLogistics companies generate massive volumes of ...
02/04/2026

Turn Your Fleet Into a Smart Machine: AI That Sees Everything on the Map

Logistics companies generate massive volumes of spatial data:
from GPS trackers and telematics to route planners and warehouse systems.
Yet turning this data into actionable insights is often slow, complex, and requires GIS expertise.

That’s why at GIS-Point, we develop customized Logistics Intelligence AI solutions that transform complex spatial data into clear operational decisions.

Our AI-powered geospatial assistant allows logistics teams to ask questions in natural language and instantly receive:
• optimized routes
• real-time analytics
• visual insights on the map

What problems does Logistics Intelligence AI solve?
▪️ Slow, complex analysis
No more navigating GIS dashboards or waiting for reports. Insights and route optimizations are delivered in seconds.
▪️ Inefficient fleet and resource use
The system detects bottlenecks, predicts delays, and recommends optimal fleet allocation — reducing fuel, time, and costs.
▪️ Limited adoption of spatial analytics
Even non-technical teams can work with complex data through simple queries.

What does this look like in practice?
A logistics manager can ask:
“Which routes are at risk of delay today?”
“How can we reduce fuel costs across the fleet this week?”

And get instant, data-driven answers: visualized directly on the map.
This means faster decisions, more efficient operations, and a stronger competitive edge.

🔗 Want to see how your logistics operations could run smarter and faster?
Message us for a personalized demo.

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This is what we build at GIS Point,AI-powered geospatial solutions using MCP (Model Context Protocol)A unified AI layer ...
01/04/2026

This is what we build at GIS Point,
AI-powered geospatial solutions using MCP (Model Context Protocol)
A unified AI layer that:
• connects all your spatial data
• integrates with ERP, telemetry, weather, satellite sources
• works securely in your environment
• and delivers insights in plain language

The real shift isn’t UI. It’s speed.
Decisions that used to take days — now take seconds.
Data that used to sit idle — now drives action.

This is becoming the standard for companies in:
• infrastructure
• agriculture
• telecom
• logistics
• energy

If you’re interested in unlocking real-time decision-making from your GIS data - let’s talk!

Agricultural companies generate massive volumes of spatial data—from satellites, drones, field sensors, and farm managem...
18/03/2026

Agricultural companies generate massive volumes of spatial data—from satellites, drones, field sensors, and farm management systems. Yet, turning this data into actionable operational insights is often slow, complex, and requires GIS expertise.

That’s why GIS-Point developed Farm Intelligence AI—an AI-powered geospatial analytics platform that lets farm managers ask natural language questions and instantly visualize results on an interactive map.

What problems does Farm Intelligence AI solve?
▪️ Slow, complex analysis
Farm managers no longer need to navigate traditional GIS dashboards or wait hours for manual reports. Insights are delivered in seconds.
▪️ Hidden crop and environmental risks
The platform detects early vegetation stress, evaluates fertilizer efficiency, and identifies environmental risks such as drought or flooding—before they impact yields.
▪️ Limited adoption of spatial analytics
Even non-technical users can interact with complex datasets through natural language, making advanced GIS insights accessible to the whole farm operations team.

What does this mean for agricultural businesses in practice?
Implementing Farm Intelligence AI transforms how spatial data is used on farms. Managers can instantly answer operational questions, optimize resource allocation, and respond to environmental risks proactively. This not only increases crop yield and operational efficiency but also strengthens the AgTech startup’s product offering, giving them a competitive edge in precision agriculture.

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Wednesday 9am - 6pm
Thursday 9am - 6pm
Friday 9am - 6pm

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+380672088520

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