Werkon AI services
Werkon starts with the workflow and the decision. The right system may combine ordinary software, deterministic rules, search, models, tools, permissions, and human review, or may not need AI at all.
AI service map
These service areas can combine within one outcome, but they solve different problems. The useful starting point is the missing responsibility, not a model name or a promise to add AI everywhere. Start with AI consulting when the opportunity is unclear, or custom AI development when a bounded workflow needs a complete system.
Map workflows, systems, data, friction, risks, economics, authority, and viable opportunities before committing to an AI architecture or delivery plan.
Coordinate repeatable work across approved tools and data, using deterministic steps where rules are known and model judgment only where its uncertainty is useful.
Connect AI capabilities to authoritative applications, APIs, identity, data, permissions, queues, interfaces, logs, and the failure paths already used by the operation.
Select, adapt, or develop models only after defining data, context, evaluation, limits, serving, security, monitoring, cost, rollback, and operational ownership.
Build grounded search, retrieval, classification, extraction, summarization, generation, and conversational workflows with source visibility and review where needed.
Evaluate visual systems against their real conditions, then monitor, secure, improve, and scale any deployed AI capability without hiding changed behavior or operating cost.
AI delivery method
Model performance is only one part of the operating result. The work also includes context, people, data, rules, interfaces, permissions, failure handling, monitoring, and change over time.
Specify the users, intended purpose, workflow, baseline, actions, affected people, decision authority, failure consequences, and the conditions outside intended use.
Identify authoritative sources, provenance, sensitive data, identity, access, permitted tools, deterministic rules, review points, logging, retention, and recovery paths.
Compare leaving the workflow alone, ordinary software, retrieval, provider models, adapted models, or custom development, then test quality, limits, risk, cost, and human use in context.
Monitor inputs, outputs, failures, misuse, drift, provider changes, cost, adoption, incidents, overrides, and outcomes, with clear owners for changes, rollback, escalation, and retirement.
AI boundaries
The system around a model determines which information it can see, which tools it can use, what happens after an output, who reviews it, and whether a failure becomes visible or harmful.
Source basis
NIST AI Resource Center
AI Risk Management Framework PlaybookCurrent voluntary guidance organized around govern, map, measure, and manage. NIST notes that AI RMF 1.0 is being revised.
National Institute of Standards and Technology
Generative Artificial Intelligence ProfileCross-sector guidance covering governance, testing before deployment, provenance, incident disclosure, and context-sensitive oversight.
A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.
Show Us the WorkflowStart with the free automation readiness checklistOBSERVEQUANTIFYDECIDEBUILD
Continue with the work
Stay with the operating question. The technology can wait until the work is clear.
Compare custom agent patterns by function and industry, then define exact sources, identity, policy, tools, approvals, receipts, exceptions and accountable outcomes.
Open path02Join service, model, human, cost, and outcome measures with incident learning, controlled change, bypass, and retirement.
Open path03Define capture conditions, region evidence, task shape, scene-segment evaluation, abstention, review, and controlled downstream use.
Open path04Define language, text boundaries, labels, source spans, uncertainty, evaluation segments, and review for one bounded NLP task.
Open path05Constrain the output, keep sources visible, test difficult cases, preserve review authority, and record release or rejection.
Open path06Bind one exact artifact to its runtime contract, staged exposure, live-context measures, rollback, and retirement path.
Open path07Compare rules, existing models, adaptation, and new training through owned data, protected evaluation, and reproducible release evidence.
Open path08Define identity, data meaning, action authority, failure handling, and reconciliation across one accountable integration boundary.
Open path09Allocate stable rules, bounded model judgment, human approval, and explicit exceptions across one complete flow.
Open path10Bind one task to scoped context, user-aware identity, narrow tools, deterministic policy, approval, audit, revocation, and recovery.
Open path11Own the complete path across sources, deterministic rules, model behavior, permissions, interface, evaluation, operation, and handover.
Open path12Compare candidate workflows through value, feasibility, data, controls, risk, evidence, and stop, prepare, test, or proceed decisions.
Open path13Map the workflow, systems, data, friction, risks, and keep, connect, replace, or build decision before committing to AI delivery.
Open path14Compare AI with software, data, cloud, delivery, and operational service responsibilities.
Open path15Follow the delivery path from operating context through verification and client-owned handover.
Open path16Start from the cross-functional outcome when an AI category is still too narrow.
Open path17See how authority, risk, records, and physical work change the AI control model.
Open path