Procurement
AI Workflows for Procurement Exception Handling
Procurement exception handling is a strong AI workflow candidate because mismatches, approvals, supplier context, and evidence already define the work.
Self-improving agentic workflows your organization owns.
People, AI agents, and systems run each process under a released, governed version. Every run leaves a replayable record you own: proof for any audit, and the data your workflows improve on.
§ 01 · Governance
Governed, observed, audit ready.
Hand critical work to AI and automation, without handing over control, or the proof. You always know the true state of every run, prove it the moment anyone asks, and fix a broken one in minutes, not days.
01
Operational certainty
Every run is visible while it happens: which step is live, which actor holds it, what was just decided. The state of the operation is a fact on screen, not a guess assembled from check-ins.
02
Evidence for auditors
When DORA, the EU AI Act, or an internal review asks how a decision was made, the recorded evidence already answers: what ran, who approved it, under which released version. Evidence is a lookup, not a scramble.
03
Debugging in minutes
A failed run replays step by step with the exact state it ran under. Root cause stops being detective work, and the fix can be proven against the very run that broke.
§ 02 · Authoring
Plain language is the new builder.
Saying what a process should do is enough. Kora turns that plain description into a versioned definition, ready for release; nobody wires up nodes by hand.
KoraClaudeCodex
What should Kora build?
process-map.bpmn24 KBapprovals-sop.pdf180 KBdata-schema.xlsx41 KB
Turn this BPMN and our SOP docs into a governed workflow.
The shift to plain language is not ours alone.
You'll simply tell your device, in everyday language, what you want to do.
Bill GatesCo-founder, MicrosoftGatesNotes · 2023The programming language is human.
Jensen HuangFounder and CEO, NVIDIAWorld Governments Summit · 2024The hottest new programming language is English.
Andrej KarpathyFounding member, OpenAIOn X · 2023The most universal interface: natural language.
Satya NadellaChairman and CEO, MicrosoftAnnual letter · 2023You could write programs in English, and the computer would translate them into machine code.
Grace HopperComputing pioneer, US NavyOn the first compilerBuild software entirely using natural language.
Amjad MasadCo-founder and CEO, ReplitOn Replit Agent · 2024§ 03 · Optimization
Workflows that improve with every run.
You own the execution data from every run. It shows exactly where operations slow down or break, so you fix the cause and release a faster, cleaner version.
Approval workflow1,240 runs
Request received1,240 runs
Bottleneck
Requests wait 1.4 days for a manager decision.
Auto-approve requests under the set threshold.
Manager approvalavg 1.4d
Compliance check12% fail
Failing
Fails on 1 in 8 runs, a 12% error rate.
Retry with backoff on the flaky connection.
Unused
Never triggered across 1,240 runs.
Drop this redundant approval step.
Second sign-off0 runs
Requester notifiedavg 3s
§ 04 · Connectors
Connected to the systems already in place.
Workflows act through the tools the organization already runs. Every connector, prebuilt or built for you, operates under the same governance as any participant: scoped permissions and recorded evidence on every call.
Gmail
Outlook
Slack
Salesforce
HubSpot
Notion
Jira
Asana
GitHub
Zendesk
Stripe
SAP
Google Sheets
Snowflake
Airtable
100+ morePrebuilt integrations
Custom connectorsLet Kora build one designed for your own stack, systems, and needs.
§ 05 · Release
Released like software.
Every version is a release: deployed to an environment, promoted when it is ready, and rolled back if it has to be, the same way software ships.
Environments
Productionv1.4
Stagingv1.5
TestingIdle
Recent releases
- v1.5DeployedStaging2h ago
- v1.4ReleasedProduction2d ago
- v1.3Superseded5d ago
- v1.2Rolled back2w ago
§ 06 · Solutions
Where teams apply governed workflows.
The same platform, pointed at the processes a business cannot get wrong. Start from your industry, or from the outcome you are after.
01
Manufacturing
Run quality and production operations as workflows your plant owns, where AI handles the routine path, people approve what matters, and every run leaves audit-ready evidence.
02
Financial services
Run the operations your regulators watch as workflows your firm controls, where AI handles the routine path, people approve what matters, and every run leaves audit-ready evidence.
03
Life sciences and labs
Run lab operations as workflows your team owns, where AI handles the routine analysis, people approve what matters, and every result carries its full provenance.
01
Governed AI Workflows
Connect people, systems, and AI agents into governed workflows that produce reliable, auditable business operations.
02
Workflow Compliance and Governance
Enforce policy in the process itself and let every run produce the audit-ready evidence your auditors and regulators ask for.
03
Operational Excellence
Kora turns manual procedures into governed workflows your organization owns: AI agents run the routine path, people decide the exceptions, and every run is measured so the process keeps improving.
§ 07 · FAQ
Questions, answered.
Straight answers to what teams evaluating governed workflows ask: what Kora is, how it differs, where it runs, how data is handled, what it connects to, and how it is priced.
Kora is a workflow governance platform where people, AI agents, and systems execute business processes together inside agentic workflows the organization owns and controls. Every run executes under a released, governed version and leaves a detailed, replayable evidence record, hash-chained so a downstream system can sign and anchor it for audit.
Workflow governance means an organization controls how its business processes execute: who can change a process, which version runs, who approves exceptions, and what evidence each run leaves behind. It extends workflow automation with ownership, policy, and auditability.
An agentic workflow is a business process where AI agents, people, and systems execute the steps together toward an outcome, with each agent able to take actions, call tools, and make decisions inside the process. In Kora, agentic workflows run under a released, governed version, and every run leaves a detailed, replayable evidence record.
Kora treats the workflow itself as a governed asset, while automation tools focus on executing tasks. Your organization owns the process definition, controls releases, observes every run, and expands automation over time only where the run evidence supports it.
A process is described in conversation, and Kora compiles and validates that description into a versioned workflow definition, then presents the workflow graph as a read-only view to inspect. Teams can also author from an assistant they already work in, such as Claude or Codex, and every change still reaches production through release control.
Yes. AI agents work alongside people and systems in a workflow. Each agent runs on a model chosen from Kora's supported list, across providers you likely already use, including Anthropic's Claude and OpenAI's GPT models, plus Google, xAI, Groq, Mistral, DeepSeek, and more, with an operator supplying the provider API key per environment. Custom and self-hosted model endpoints are on the roadmap. Each agent runs under the released process version, acts only within the permissions granted to its step, and leaves a recorded, hash-chained trace for every action.
Kora runs on your own hardware. You install it with the Docker Compose deploy bundle and the koractl operator command, on-prem or air-gapped, so the runtime and every workflow run inside your environment. Kora Cloud does not run your workflows; it is the account, licensing, and instance-management service for connected deployments. A Kubernetes deployment target is on the roadmap and not yet shipped. Offline and air-gapped installs run fully disconnected.
Your workflows and their evidence records always run on your own hardware, on-prem or air-gapped, so workflow data stays inside your environment. Offline and air-gapped installs send nothing back. For connected deployments, Kora Cloud provides account, licensing, and instance management, and on non-Enterprise plans it also receives telemetry and data-contribution records described in the Terms and Privacy Notice; an Enterprise deployment sends only periodic license check-ins with advisory usage counts.
Yes. Kora's runtime always runs on infrastructure you control: your own hardware on-prem, a private or EU-sovereign cloud account, or a fully air-gapped install, so your workflows execute and their evidence records are produced in your own environment. Offline, air-gapped, and Enterprise deployments keep that data fully in your hands; connected non-Enterprise plans send only the telemetry and data-contribution records described in the Terms and Privacy Notice.
Kora connects to hundreds of prebuilt integrations across the enterprise stack, including Gmail, Outlook, Slack, Salesforce, HubSpot, Jira, GitHub, Snowflake, and Google Sheets. It also reaches internal APIs and databases through service operations that run governed code, extracts content from documents in a workflow step, connects to Streamable HTTP MCP endpoints, and can build a custom connector for your own stack. Every connector runs under the same scoped permissions as any other step, and its calls are recorded in the run evidence.
Every production run is captured as a durable, hash-verifiable record, tagged with its release, environment, and deployment, so the data is attributable to the version that produced it. Every change already ships through release control: immutable releases, controlled environment deployment, and audited rollback by redeploying an earlier release. Replaying a candidate version against those historical runs, and gating promotion on how it compares, is on the active roadmap and not yet enforced today.
That is the direction. Kora keeps cross-referenced evidence layers: a control-plane audit log of who changed what, plus runtime evidence, agent traces, and a journal of the workflow's business facts. The same captured run data an audit relies on is what an evaluation loop would later consume, so no extra instrumentation is needed. Using those records to backtest candidate versions, and to export governed datasets to a fine-tuning or reinforcement-learning provider you choose, is on the roadmap, not live today. Kora does not train models itself, and where that data lives follows your deployment.
Yes. Kora has a free tier you can start on your own without a sales call, plus paid plans: Pro, Teams priced per seat, and Enterprise. Every plan runs the same runtime, which enforces the limits and features your license carries. Pro lifts usage limits for a single user and organization, Teams adds seats and organizations, and Enterprise covers the largest deployments, including fully offline and air-gapped installs. Full details are on the pricing page.
§ 08 · From the blog
Latest from the Kora team.
Workflow governance in practice: product thinking, platform engineering, and lessons from real processes.
Manufacturing
AI Workflows for Manufacturing Quality Deviations
Manufacturing quality deviations are a strong first AI workflow because they are important, evidence-rich, and naturally human-reviewed.
Releases
Release Management for AI Workflows
AI workflows need release management because changing prompts, tools, tasks, assignments, integrations, or policies can change production behavior.
§ 09 · Next step
One real process is enough.
In a demo, we take a process you run today, describe it in chat, run it under governance, and show the record it leaves: evidence for auditors, and the data that makes the next version better.