Model behavior
Prompt and context design, retrieval, dataset work, fine-tuning and task-specific evaluation.
AI ENGINEERING
AI Engineering
Model adaptation, AI integrations, agentic workflows and realtime game AI backed by evaluation, backend services and cloud operations.
We connect the model layer to data, tools, permissions, application logic and observability so the result can be measured, operated and improved.
DISCIPLINE SCOPE
We engineer AI as a measurable product system, not a model demonstration.
Our AI discipline spans product integration, retrieval, model training and fine-tuning, agent orchestration and realtime game AI. We choose between deterministic software, retrieval, model adaptation and generative behavior according to the task.
Backend & Cloud is part of the offer: authentication, data, queues, workers, APIs, persistence, observability and cost controls are what turn useful model behavior into a dependable workflow.
Agent and application logic
Tools, explicit state, branching, validation, human approval and user-facing workflow.
Production platform
APIs, data, queues, workers, permissions, deployment, tracing and usage controls.
Realtime game AI
Decision systems, navigation, tactics and simulation under frame-time and multiplayer constraints.
The model, application and operating system designed as one product.
The model, application and operating system designed as one product.
We build AI product features and internal workflows that retrieve knowledge, produce structured results, call tools and connect to existing business systems. Permissions, validation, latency, cost and human review are engineered alongside model behavior.
AI product features
Assistants, copilots, classification, extraction, summarization and generation embedded in a purposeful user workflow.
Retrieval and knowledge systems
Ingestion, chunking, metadata, search, permissions and grounded response behavior over product or organizational data.
System integration
Models connected to APIs, databases, files and business tools through authenticated, validated and auditable actions.
Evaluation and operation
Test datasets, structured checks, tracing, latency and cost measurement, fallback behavior and production monitoring.
How we move beyond an impressive prompt
How we move beyond an impressive prompt
We define the task and acceptable failure first, establish a baseline, then select the smallest combination of context, retrieval, model adaptation and deterministic code that meets the requirement.
- 01
Define the decision or workflow
Map users, inputs, desired output, permissions, risk, review needs and the measurable baseline.
- 02
Build an evaluated prototype
Create representative test cases and compare approaches against quality, latency and cost.
- 03
Integrate the production system
Add data pipelines, tools, state, validation, security, retries and user-facing behavior.
- 04
Observe and improve
Version prompts and configuration, trace outcomes, analyze failures and maintain a regression evaluation suite.
AI product deliverables
AI product deliverables
The output is a maintainable product system with measurable behavior, not a notebook or isolated API demonstration.
- Use-case, risk and evaluation definition
- Working AI feature or workflow
- Retrieval, tool and backend integrations
- Evaluation suite and quality baseline
- Deployment, monitoring and cost controls
AI product deliverables
The model, application and operating system designed as one product.
A data and evaluation pipeline for changing model behavior deliberately.
A data and evaluation pipeline for changing model behavior deliberately.
When prompting and retrieval cannot meet the requirement, we prepare domain data, run controlled training or fine-tuning experiments and integrate the resulting model into a measurable inference path.
Dataset engineering
Source review, cleaning, normalization, labeling guidance, splits, lineage and quality checks aligned with the target task.
Training experiments
Supervised fine-tuning, continued adaptation or task-specific models where evidence supports the added complexity.
Evaluation and regression
Held-out datasets, task metrics, qualitative rubrics, safety cases and comparison against simpler baselines.
Inference integration
Serving, batching, latency, hardware, privacy, monitoring and retraining triggers within the surrounding product.
How we choose between retrieval, fine-tuning and training
How we choose between retrieval, fine-tuning and training
Training is not the default answer. We use the cheapest valid baseline first, measure the gap and add model adaptation only when it improves the behavior the product actually values.
- 01
Define the task and metric
Specify expected inputs, outputs, edge cases, quality threshold, privacy constraints and inference envelope.
- 02
Audit and prepare data
Assess volume, representativeness, rights, leakage, labeling consistency and the cost of maintaining the dataset.
- 03
Run controlled experiments
Compare baselines and adaptations with tracked configurations and held-out evaluation.
- 04
Integrate and maintain
Package inference, monitor drift and failures and define the conditions for future data or model updates.
Model adaptation deliverables
Model adaptation deliverables
Work is documented so a result can be reproduced, compared and maintained rather than depending on one opaque experiment.
- Dataset specification and preparation pipeline
- Baseline and experiment record
- Fine-tuned or task-specific model artifacts where applicable
- Evaluation and regression suite
- Inference and retraining integration plan
Model adaptation deliverables
A data and evaluation pipeline for changing model behavior deliberately.
A data and evaluation pipeline for changing model behavior deliberately.When prompting and retrieval cannot meet the requirement, we prepare domain data, run controlled training or fine-tuning experiments and integrate the resulting model into a measurable inference path.
Long-running AI workflows with explicit state and control.
Long-running AI workflows with explicit state and control.
We build systems in which models select or use tools across multiple steps, while deterministic application logic controls permissions, branching, persistence, retries and human decisions. LangChain or LangGraph may support this architecture when their abstractions fit the workflow.
Tool-using agents
Scoped access to search, databases, internal APIs and external services with typed inputs, validated outputs and auditable actions.
Stateful orchestration
Explicit workflow state, branching, checkpoints, durable execution and recovery for work that cannot live inside one request.
Human approval
Review queues, thresholds, escalation and editable intermediate results for actions that require accountability.
Multi-agent systems
Specialized agents only where decomposition improves quality or ownership enough to justify coordination cost and complexity.
How we make an agentic workflow production-safe
How we make an agentic workflow production-safe
We separate decisions that benefit from a model from rules that should remain deterministic. Every external action has an owner, permission boundary, validation path and recovery behavior.
- 01
Map the workflow graph
Identify states, tools, decisions, failure points, approval gates, completion criteria and existing system owners.
- 02
Evaluate the uncertain steps
Build datasets for routing, extraction or generation and compare model behavior before automating actions.
- 03
Add durable execution
Implement persistence, queues, idempotency, retries, timeouts, cancellation and human review.
- 04
Trace and govern
Record model, prompt, tool and state transitions, monitor cost and failures and support controlled configuration rollout.
Agent and orchestration deliverables
Agent and orchestration deliverables
The system includes the backend mechanics required for reliable work, not only the agent prompt and tool declarations.
- Workflow and responsibility graph
- Agent, tool and deterministic control implementation
- Persistent state, queues and recovery
- Human review and administrative controls
- Tracing, evaluation and operating documentation
Agent and orchestration deliverables
Long-running AI workflows with explicit state and control.
Long-running AI workflows with explicit state and control.We build systems in which models select or use tools across multiple steps, while deterministic application logic controls permissions, branching, persistence, retries and human decisions. LangChain or LangGraph may support this architecture when their abstractions fit the workflow.
Behavior systems selected for the experience, world and frame budget.
Behavior systems selected for the experience, world and frame budget.
We create NPC decision-making, navigation, tactics, group behavior, encounter direction and simulation systems. Classical realtime approaches and generative AI can coexist, but each is used only where its behavior, latency and operating cost fit the game.
Decision architecture
State machines, behavior trees, utility systems, goal-oriented planning and hybrids organized for readable authoring and debugging.
Navigation and movement
Nav meshes, grids, custom pathfinding, steering, avoidance and spatial queries for static, procedural or destructible worlds.
Tactics and simulation
Target selection, cover, squads, economies, populations, encounter directors and systemic responses to player behavior.
Networked and generative behavior
Server-authoritative NPCs, relevance and bandwidth controls, plus dialogue or content generation where a bounded model improves the product.
How we build AI players can read and developers can tune
How we build AI players can read and developers can tune
We define the behavior the player should perceive before choosing an implementation. Debug views, authoring controls and performance budgets are designed with the runtime system.
- 01
Define observable behavior
Describe goals, information available to the agent, acceptable mistakes, difficulty, fairness and multiplayer authority.
- 02
Prototype the decision loop
Build a small encounter with visualization and telemetry to test behavior and designer control.
- 03
Integrate world and content
Connect navigation, animation, combat, simulation, authoring data and network ownership.
- 04
Stress and tune
Test many agents, adverse world states and frame budgets while giving designers useful controls and diagnostics.
Game AI deliverables
Game AI deliverables
The system includes runtime behavior, content-authoring support and the tools required to understand why an agent acted.
- Behavior and information model
- Decision, navigation or simulation implementation
- Designer authoring and tuning tools
- Debug visualization and telemetry
- Performance, multiplayer and extension documentation
Game AI deliverables
Behavior systems selected for the experience, world and frame budget.
RELATED PROOF & PATHS
Continue the technical evaluation.
Incrempire
Incrempire
One of our featured releases: a relaxing incremental survival and base-defense game where every defeat feeds permanent progression. Players defend a kingdom across 25 levels and four biomes, combine a deep skill tree with run-specific blessings, unlock spells, towers, mining and rune systems, and experiment with different builds. Following a rapid two-month public-demo cycle, Incrempire launched on Steam for Windows, macOS and Linux with Steam Deck support, achievements, leaderboards and eight interface languages.
View details
Bad Goons
Bad Goons
A flagship multiplayer FPS built around a battlefield players can destroy, excavate and rebuild during every match. Fully networked voxel destruction, dynamic capture points, construction and fortification, deployable spawn networks, procedural maps, tactical support assets and a broad arsenal create shifting front lines instead of static cover. The playable Early Access build is in active development toward its planned December 2026 Steam release, supported by shared developer-and-community playtests.
View detailsTECHNICAL PRIORITIES
From decision to production.
- 01
Define
Specify the task, baseline, acceptable failure, data rights and human responsibility.
- 02
Evaluate
Build representative tests and compare the simplest viable approaches.
- 03
Integrate
Connect models to product data, tools, state, permissions and deterministic controls.
- 04
Improve
Trace outcomes, measure quality, latency and cost and run regression evaluation before changes.
START WITH THIS DISCIPLINE