04 · AI consulting
Practical AI, built on solid engineering
From deciding where AI will pay off to running it reliably in production: LLM applications, retrieval over your own data, agents, and machine learning on spatial data.
Start with the problem, not the model
Most organizations don't need more AI enthusiasm. They need to know which problems AI can actually solve for them, what it will take to build, what it will cost to run, and how to keep it accurate and safe.
We help you answer those questions honestly, then build the solution with the same engineering discipline as any other production system: evaluation, monitoring, security, and cost control included.
Who it's for
- Leadership teams setting an AI strategy or assessing readiness
- Product teams adding LLM features to an existing application
- Organizations with large document, data, or geospatial holdings they want to make useful
- Teams whose location data could drive better predictions and decisions
Typical engagements
- AI readiness assessments covering data, infrastructure, skills, and governance
- Use-case discovery and prioritization by value, feasibility, and risk
- LLM integration with Azure OpenAI and other model providers
- Retrieval-augmented generation (RAG) over your documents and data with Azure AI Search
- Agents and assistants that use tools and act on your systems
- Geospatial AI: natural-language access to spatial data, spatial feature engineering, and predictive models
- Evaluation, monitoring, and cost controls for AI in production
Outcomes
- A short list of AI use cases worth funding, with a plan for each
- Prototypes that test value on your own data before a larger commitment
- Production systems with evaluation and guardrails, not demos
- A team equipped to extend and operate what's built
Where AI meets location
AI that understands place
Location adds context that general-purpose AI often misses. Where geospatial data is part of your business, it can make AI solutions noticeably more useful.
Natural-language spatial search
Assistants that let people ask questions of maps, imagery, and spatial datasets without needing GIS skills.
Spatial machine learning
Feature engineering and predictive models that account for distance, proximity, and geography.
Optimization
AI-enabled routing, logistics, and supply chain decisions grounded in real-world networks.
The experience behind it
Hands-on since the early days
Our founder was part of the launch of Microsoft's Bot Framework and Cognitive Services developer programs, and has since architected AI solutions for some of the company's largest customers.
- Lead architect for a geospatial AI assistant with access to more than 100 PB of open data
- Delivered AI-enabled supply chain optimization for a major global enterprise
- Developed open source geospatial ML and AI tools with Microsoft's AI for Earth team
- Part of the initial launch of Microsoft's Bot Framework, Cortana Skills Kit, and Azure Cognitive Services (now Azure AI services)
Responsible by design
AI systems should be accurate enough for the job, clear about their limits, and safe with your data. We build those qualities in from the start rather than bolting them on later.
- Data privacy and security reviewed before any data reaches a model
- Evaluation against real tasks, with measurable quality targets
- Human oversight where decisions carry real consequences
- Transparency for users about what the system does and doesn't know
How it's delivered
Often as a fixed-scope readiness assessment or prototype, followed by a delivery project or ongoing advisory. More on how we engage.
Have a geospatial, cloud, or AI challenge in front of you?
Tell us what you're working on. We'll give you a straight answer on how we can help, or tell you if we're not the right fit.