AI you can rely on in production: assistants, agents, vision, and predictive models.
AI is more than chatbots. We build language assistants and agents, computer vision on imagery and video, and forecasting and risk models, each with the evaluation, guardrails, and operations that let people depend on it.
What ai engineering solves
The questions that bring people to us for this work, what is usually behind them, and what we do about it.
“Can we trust what the assistant tells the public?”
The problemChatbots answer confidently from outdated or wrong sources, and nobody measures how often.
How we solve itRetrieval grounded in governed sources, with factuality and grounding evaluated on every release.
“Which routine work can an agent safely take on?”
The problemStaff spend hours on data entry, summaries, and routing that follow clear rules.
How we solve itAgentic automations with tool access scoped to the task, human review where it matters, and a deterministic fallback.
“What is growing into our lines, or building on our easements?”
The problemEncroachment, vegetation, and asset condition are found by patrols and field visits, long after they became a risk.
How we solve itComputer vision on satellite, aerial, drone, and camera imagery that detects encroachment, condition, and change, and routes results as work orders.
“Where will demand, outages, or failures come next?”
The problemPlans rest on last year’s averages while the history that could predict next month sits unused.
How we solve itForecasting, risk, and causal models built on your own history, validated at realistic operating points and monitored for drift.
Each answer rests on the same method. Here it is for ai engineering.
How we do it
Four steps, in order, with governance designed in from the first one.
Scope one use case
One workflow or decision, clear success criteria, and the evaluation set that will prove it.
Build the model or system
Retrieval and tool use for language, detection and segmentation for imagery, forecasting and risk models for tabular and spatial data.
Add guardrails
Input and output validation, redaction of personal data and sensitive scenes, and tests for bias, jailbreaks, and prompt injection.
Operate it
Shadow deployments, drift and cost monitoring, rollback-ready model versions, and results routed into GIS and work systems.
Data governance in this work
An AI system is only as trustworthy as its data and its tests.
- Sources governed by data contracts; models, prompts, and training data versioned.
- Personal data, health data, and sensitive imagery redacted, scoped, and logged by policy.
- Every model evaluated for accuracy and bias before release and monitored after, with results kept for audit.
What you get
- A working AI system for one use case
- An evaluation suite: accuracy, bias, safety, cost
- Guardrails and redaction
- Integration with GIS, work orders, or your applications
- Monitoring, retraining, and an operations runbook
A method is only useful once it is running. Here is how it gets there.
Phase by phase, with a gate at each one
Each gate is agreed before its phase begins, so nothing moves forward on optimism.
Scope
Choose the use case, define success, and build the evaluation set from your own data.
Gate: Success criteria agreedBuild
The model or system, built and evaluated against that set.
Gate: Passes the evaluation setLaunch
Shadow deployment first, then live, with monitoring and integration.
Gate: Service levels met in productionImprove
Feedback, drift, and incidents drive retraining and updates.
Gate: Ongoing
How it runs
Models are chosen by how they score on your evaluation set, and deployed in your cloud, or on drones, vehicles, and cameras where latency demands it.
Technical detail
Assistants & agents
- Assistants and chatbots for service centers and internal operations
- Agentic automations for data entry, summarization, and routing
- Document Q&A over policies, permits, and technical standards
- Decision support with tool use: search, GIS, ticketing, email
- Hybrid retrieval with filters, metadata, recency, and freshness
- Streaming interfaces, multi-turn memory, and deterministic fallbacks
Computer vision
- Imagery pipelines: acquisition planning, STAC catalogs, orthorectification, cloud-optimized GeoTIFFs
- Detection and segmentation models with uncertainty masks
- Change detection and trend alerts across time series
- Depth estimation for volume, clearance, and distance
- Photogrammetry: orthomosaics, surface models, point clouds, meshes
- Document processing for forms and public records
Machine learning models
- Demand and outage forecasting
- Causal inference for intervention effects
- Uplift modeling for customer programs
- Geospatial risk modeling for siting, routing, and maintenance
- Program evaluation and A/B testing
Edge & integration
- Model optimization and streaming inference on drones, vehicles, and fixed cameras
- Events into work management when thresholds are crossed
- Vectorized results published to GIS feature and map services
Evaluation & safety
- Evaluation suites for task success, grounding, and accuracy by class and scenario
- Bias and robustness testing; jailbreak and prompt injection tests
- Personal, health, and sensitive-scene redaction
- Observability: traces, drift monitors, and cost, latency, and error dashboards
Operations
- A/B and shadow deployments with rollbacks
- Versioned model and prompt registries with approval gates
- Scheduled and drift-triggered retraining
- Caching and cost controls with service-level objectives
Tell us which decision or workflow AI should take on first.
We will scope a minimal version, define how to measure it, and tell you plainly whether AI is the right tool.