We design and build governed agents, company brains, decision-support workflows, and the runtime around them.
Evidence stack // three systems // real UI frames
Capabilities
The model is one component. Useful systems also need context, permissions, state, workflow rules, human authority, verification, and recovery.
Interpret incoming work, create or update the right records, trigger supported workflows, and escalate uncertainty.
Request → typed action → workflow → escalation
Service capability · No public client case
Governed organizational memory across documents, decisions, messages, project history, and live systems.
Source → scoped retrieval → reviewed update
Assemble evidence, run deterministic analysis, apply bounded model judgement, and prepare an accountable decision.
Evidence → analysis → human decision
Tool policies, typed actions, isolated execution, durable state, traces, evaluation, recovery, and proof.
Policy → execution → verification → stop or repair
Systems in practice
A client deployment, a product demonstrator, and archived R&D—shown without inflating one into another.
Natural-language field updates become structured operations, clarification, and explicit approval paths.
Claim boundary. A deployed system with representative public demo data. A material-request workflow-start gap remains; approvals are not autonomous.
Read the case studySourced evidence, deterministic analysis, model-assisted judgement, and separate human approval.
Claim boundary. Representative-data demonstrator—not a bank deployment, regulated advice, or autonomous credit decision.
Read the case studyDiscover, refine, generate, review—and preserve failed verification before deployment.
Claim boundary. Archived prototype evidence. It does not prove production readiness, ArcGIS equivalence, or a completed customer migration.
Read the case studySupporting evidence
What makes an agent operational
Documents, records, history, and live state for the current job.
APIs, databases, specialist software, and isolated runtimes.
Rules for what the agent may read, draft, update, or trigger.
A record of what happened, what is open, and what may resume.
Named review points for consequential or uncertain actions.
Traces, artifacts, validation, failure records, and repair paths.
Runtime R&D · Durable events, bounded execution, verified artifacts, signed evidence, and stable reload state. Not presented as a completed Agent OS.
How we work
We define the operating reality, action boundary, human authority, integrations, evidence, failure states, and smallest complete slice.
Map people, systems, inputs, decisions, exceptions, and evidence.
Build one complete path from real intake to a reviewable outcome.
Add permissions, integrations, monitoring, and operating procedures.
Test weak evidence, failed tools, unsafe actions, recovery, and cost.
From Axis Spatial
We will identify what the agent should do, where it must stop, and the smallest paid slice worth building.
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