Applied AI engineered as part of the system, not added beside it.
A model is only one component of an intelligent system. Its usefulness depends on the information it can retrieve, the context it receives, the tools it can use, the actions it is permitted to take, and the workflow that determines what happens next.
Weak surrounding systems produce confident answers from stale sources, hide uncertainty, bypass approvals, and turn isolated demonstrations into operational liabilities.
General Applications engineers intelligent systems with grounded retrieval, structured context, evaluation, deterministic controls, and explicit human accountability built into the architecture from the start.
What We Build
- AI readiness assessments, use-case selection, and staged implementation plans
- Retrieval-backed assistants grounded in approved documents, data, and system authority
- Decision-support systems with explicit review, approval, override, and escalation paths
- Agentic and tool-using workflows with bounded permissions and observable actions
- Human-in-the-loop authoring, research, classification, and revision systems
- Evaluation harnesses, domain test sets, validation checks, and release quality gates
- Structured context assembly, schema-constrained outputs, and model and tool orchestration
- Model and provider selection, routing, fallback, performance, and cost controls
- Production monitoring for retrieval quality, model behavior, tool execution, and workflow outcomes
- Focused prototypes designed to answer real technical and operational questions before broader investment
Probabilistic systems still need deterministic controls.
A model can generate options, summaries, classifications, plans, and drafts. It should not silently decide what is authoritative, complete, permitted, safe, or ready to act on.
The dependable system is built around the model: authoritative retrieval, structured context, schema validation, permissions, evaluation, fallback behavior, review paths, instrumentation, and clear ownership of every consequential decision.
Typical Questions
- Where can AI improve this workflow without obscuring authority or accountability?
- What information, tools, and actions should the system be permitted to use?
- When should the system use retrieval, tools, deterministic rules, model customization, or no model at all?
- What must be validated before an output can be trusted or acted on?
- Where should human review, approval, or override remain mandatory?
- What evidence would demonstrate that the system is improving the real workflow rather than merely producing plausible output?
- How do we keep a useful prototype from becoming an operational liability?
Related
Have a system that touches this capability?
Bring the workflow, the data problem, or the AI question — we will help make the next move clearer.