Our Products

Process GPT

GS Good Software Grade 1 certification mark

END-TO-END PROCESS ORCHESTRATION

From AI that speeds up individual work,
to AI that cuts lead time across the organization.

Writing and analyzing documents has gotten faster. But from a customer's request to the final decision and completed execution, how much faster has your organization's work become?

Process GPT connects people, AI and existing systems into one End-to-End process. It finds the handoff and decision bottlenecks between departments and turns individual time savings into results for the whole organization.

Diagnose your organization's bottlenecks See the features and real screens ↓Product brochure (PDF)

01 · THE PROBLEM

Individuals have gotten faster,
so why does the organization's work still take so long?

Organization-wide lead time is the time that elapses from when work starts until the final decision is made and execution is complete. Even if individual tasks get shorter, overall improvement stays limited while waits remain on the path that decides when work finishes, the critical path.

Within the taskActual processing

Writing documents, analysis, system processing

The part personal AI mostly improves

Between departments and systemsHandoff wait

Waiting for hand-over, intake and assignment

Where a judgment is neededDecision wait

Waiting for review, approval and exception decisions

Where work loops backRework

Filling in omissions, re-review, reprocessing

This view breaks down the elapsed time of a single piece of work. For parallel work, times are not simply added up; instead, we look at the path that determines final completion.

Producing a report quickly is not the same as reaching a decision quickly.

Even when sales finishes a proposal, the time it takes to confirm a commitment to the customer does not shrink unless legal review, finance approval and execution by operations are connected. The next owner must receive the information, authority and completion criteria they need.

The gap between AI investment and organizational results: what 40% · 95% · 5% mean

Over 40%

Agentic AI projects expected to be canceled

Gartner projects that over 40% will be canceled by the end of 2027, citing rising costs, unclear business value and inadequate risk controls. This is not a failure rate that has already occurred.

Gartner · 2025.06.25

95% / 5%

The gap in measurable business results

A 2025 preliminary study by Project NANDA reported that 95% of organizations are getting no return, and that 5% of integrated AI pilots are creating significant value. It highlights the gap between individual productivity gains and P&L results, and stresses integration into workflows and contextual learning.

The GenAI Divide · July 2025 · copy of the report, pp. 2–7

These are projections and studies with different subjects and criteria. They are not a universal failure rate for all AI projects, nor do they mean that missing process connections are the only cause of failure. Process GPT approaches this performance gap from the perspective of organization-wide lead time and process connection.

02 · THE SOLUTION

With End-to-End orchestration,
we connect the start of work to its final result.

Process GPT is an execution platform that carries each agent's answer forward into the next department's decisions and execution. It designs connection, decision-making, exceptions and measurement together to relieve bottlenecks in overall lead time.

01 · Connection

Turn handoffs between departments into a process

The output of one step becomes the input for the next person and system. Who picks it up and when it is complete are defined in a single flow.

Process design features →

02 · Decision-making

Decisions that come with their evidence and accountability

AI organizes the context, rules apply clear criteria, and people make the important decisions. The information needed for review and the approval path are placed inside the work itself.

Rules & collaboration features →

03 · Exceptions

A path for work that loops back, too

When something is missing or rejected, or an integration fails, it sets who fills the gap and how the work is reprocessed. Exceptions are managed as work status instead of being left in someone's inbox.

Execution & feedback features →

04 · Measurement

Improve against total completion time

From the recorded execution history, it breaks down the processing and waiting of people and agents. It finds bottlenecks on the critical path and compares overall lead time, rework rate and quality before and after a change.

Lead time analysis features →

The measure of successNot when the AI finishes its response,
but when the organization's work is done.

The goal is shorter overall lead time. While keeping key approvals and quality standards in place, we find the time that can be cut from handoffs and waits.

Integration scope, exception handling and measurable segments are validated against each customer's business rules and system configuration.

Our vision From individual productivity gains to execution results for the whole organization.
Knowledge grounds decisions, and processes carry those decisions through to completion. The AI-Native Enterprise vision →

FEATURES

From design to execution, integration, and analysis, organized into seven areas. Click any item to see it with real screenshots.

Process design · Reverse engineering Produce executable processes from natural language and existing documents

▶ Reverse-Engineer Processes from Policy Documents Build executable processes from policy PDFs with human review 1 screenshot

ㆍSelect a policy PDF from the Knowledge Base in chat. The Deep Agent presents a draft covering steps, owners, and amount-based approval and rejection paths, along with source documents and page references.
ㆍClick “Approve” on the draft to see suggested agents, skills, and decision tables (DMN) for each step. Only the options a person selects are generated, creating an executable standard BPMN process together with input forms.
ㆍThe generated process is added to the Process Definition Map only when you click “Save”. Compare the generated flow with the original document and describe any discrepancies in natural language to have them corrected.

The Deep Agent’s draft after reading the policy — steps, amount-based approvals, “Approve” and “Reject” — A draft based on the policy — human approval required to proceed

Watch the demo (2:18)

▶ Automatic generation of BPMN processes and business forms Design BPMN workflows and their input forms together in natural language 2 screens

ㆍIdentified processes are visualized as international-standard BPMN diagrams and can be fine-tuned with natural-language prompts.
ㆍThe input forms each task needs (applications, evaluation questionnaires, and so on) are generated automatically and semantically, in keeping with the document's context.
ㆍReference information is wired up automatically so that each step can refer to the output of the previous one.

▶ Turn a task description into a process where people and agents collaborate Review the draft, then generate BPMN and input forms for each step Demo video

ㆍIn the home chat, describe the workflow you want to create to Basic Agent in one or two sentences. Review and refine a draft with the process name, steps, flow, and items that need further clarification.
ㆍSelect “Create the process as is” to generate and immediately save a BPMN process with lanes for human and agent roles, along with input forms for each step.
ㆍIn “Simulation”, preview the flow with AI-generated sample values. No actual execution records are created. Request changes conversationally or edit directly in the BPMN modeler.

Agent setup · Knowledge learning Derive who can do what from the org structure and execution history

▶ Manage business rules taught in natural language as agent knowledge Create Agent Colleagues and review the rules behind their answers Demo video

ㆍIn “Create Agent” on the org chart, describe the agent’s work in one line. AI drafts its name, role, goals, and persona; save it to add the agent as a team member.
ㆍTeach business rules in natural language in “Learning Mode” to record their content and creation dates in “Knowledge Management”. Saved rules can be deleted.
ㆍIn “Query Mode”, the agent answers based on the rules it has learned. Evidence cards show each rule’s original text and similarity score.

▶ Org-Chart-Based Automatic Agent Mapping Your organizational structure becomes your agent roles 1 screen

ㆍFor each task, separates what people do from what agents do, then finds the right AI agent in the org chart and assigns it automatically.
ㆍIf no suitable agent exists in the org chart, it evaluates whether the task can be agentized, creates a new agent, and maps it.
ㆍOn critical paths that require a final human decision, a human approval step is inserted automatically.

▶ Refine Agent Work and Skills Revise task results and review skill improvements 1 screen

ㆍChat feedback prompts the agent to rewrite the same task result. You can view and edit skills made of SKILL.md and supporting files.
ㆍWith a Git account connected, accumulated feedback on the same task prompts skill improvement proposals for human approval.
ㆍApproved skill changes are applied through branches and merge requests. Commits in the linked Git repository record their history.

Watch the demo

Execution · Human Collaboration Agents execute; people step in where a decision is needed

▶ Agents execute assigned steps in sequence Step forms are filled using reference data and inputs from earlier steps Demo video

ㆍOnce a quote request is submitted, agents complete the inventory check and quote calculation in sequence without human review, entering results in each step’s form.
ㆍThe quote agent uses the knowledge base price list and request inputs to apply discounts and installation fees, recording the pricing rules in the form’s notes.
ㆍIf inventory data is missing, the agent reports the gap instead of inventing figures. For steps shared by a person and an agent, the agent drafts and the person reviews and submits.

▶ Delegated Agent Execution with Real-Time Feedback Step in mid-execution and change course 2 screens

ㆍDelegate a task with 'Hand to agent' and the agent builds its own plan, then carries it out using knowledge and tools.
ㆍResults are mapped automatically into the task form; users review them and either adopt the result or give feedback.
ㆍA natural-language remark such as "Generate only 3 questions" is enough for the agent to re-plan and apply the change immediately.

▶ Deep-Agent Autonomous Subprocess Execution & Ontology MCP Automatic subagent assignment and knowledge graph citations 2 screens

ㆍDeep Agents automatically assign the activities within a subprocess to subagents and execute them autonomously. — e.g., foreign-language inquiry intake → translation → review of applicable laws → drafting the reply.
ㆍDuring execution, the agent calls the Ontological Studio MCP (ontology_query) to explore legal knowledge graphs such as the Road Traffic Act and the Framework Act on Building, citing the supporting provisions.
ㆍThe ontology built in Ontology Studio serves as the agent's 'knowledge graph'; because answers are grounded only in explicit knowledge, you get reliable, hallucination-free automation.

Deep agent exploring legislation via the ontology MCP

Watch the demo Learn more about Ontology Studio →

▶ Deterministic Control with DMN Business Rules Pin decisions down in a rule table 1 screen

ㆍBusiness rules such as "Approve if the accuracy rate is 60% or higher" are managed as decision tables in the international DMN (Decision Model and Notation) standard.
ㆍThe agent's conditional branching is governed by explicit rules, guaranteeing transparent and consistent automation.
ㆍRules can also be refined continuously through natural-language feedback, so business policies change without touching code.

Solidified Execution · Reproducibility Once a task is learned, it runs exactly the same every time — re-executed as code with no LLM inference (Deterministic Replay)

▶ Eliminating the Risk of LLM Discretion Solves the problem of behaving differently on the same request by solidifying a verified path 1 screen

Hallucinations that change the execution path for the same request, LLM inference cost and latency on every run, and the impossibility of verifying in advance what will execute — all are solved by ‘solidifying the verified path’.

ㆍRecord the tool-call history of the first run
ㆍSolidify the verified path as parameterized code
ㆍFrom the second run on, code executes instead of AI

▶ First Run — Recording the Tool-Call History Every MCP tool call in the first run is captured as an event 1 screen

On the first run, the LLM agent interprets the instruction and calls MCP tools. Every tool call, such as SELECT→UPDATE→INSERT, is recorded without exception as a tool_usage_finished event.

ㆍUses the MCP servers registered to the tenant (e.g., Supabase MCP)
ㆍRecords every tool call as an event
ㆍShows execution statistics (inference count·elapsed time)

First run - the LLM agent performs the task via MCP

▶ Solidification — The Execution Path Becomes Python Code The recorded history is converted into parameterized Python code 1 screen

Parameterized execution code is generated from the recorded events and stored in the mcp_python_code table under a (process, activity, tenant) key. Business values are extracted as ${variable} templates for reuse.

ㆍHistory → parameterized Python code
ㆍParameter specs (name·type·example) extracted automatically
ㆍNo LLM calls inside the code

▶ Replay — Different Input, Same Path, Zero Inference Only the input changes; the path stays the same — zero LLM inference 1 screen

When a new instruction arrives, only the values are extracted from the input and the solidified code runs as-is. With no agent crew to spin up and zero LLM inference, execution time in the demo dropped from 5.3 seconds to 3.2 seconds.

ㆍOnly parameter values are extracted from the new input
ㆍPath·SQL structure are fixed in code
ㆍ100% reproduction of the path steps

Replay - different input, same path, zero LLM inference

▶ Transparent Verification of Results Which steps ran, and how, stays visible on the task screen 1 screen

A replayed task is shown as a ‘Deterministic Code Execution Result’ card. Which steps ran, and that the execution method was ‘solidified code (zero LLM inference)’, can be confirmed right on the task screen.

ㆍExecution steps·method are kept as part of the task result
ㆍFirst-run/replay history managed from the task list
ㆍLinked to the BPMN process view

▶ Verified All the Way to the Live Database First run and replay write identical results to the real DB 1 screen

Both the first (LLM) run and the second (solidified code) run are written to a live Supabase DB. The inventory table and audit log are recorded identically, passing all 21 E2E checks.

ㆍE2E verification 21/21 PASS
ㆍCompensation (undo) code generated automatically — safe rollback
ㆍAutomation without programming — the system writes its own code

Integration · Tool Management Safely attach internal systems and external tools to your agents

▶ MCP HubNEW Enterprise MCP operations that go beyond pasting JSON 13 screens

ㆍOrganized into four menus — MCP servers, gallery, observability, and integration standards — it actually connects before saving so you can see exactly which tools come in.
ㆍBecause input schemas are stored too, permissions are granted per tool rather than per server, and irreversible tools can be blocked individually.
ㆍConnection details accumulate as immutable snapshots, so you can roll back to a previous version instantly if something breaks, and standards compliance is graded A·B·C automatically before registration.

MCP Hub — Server list — Live connection status — Server list — Live connection status

MCP Hub — Connection test before registration — Connection test before registration

MCP Hub — Server details — Latency, availability, consecutive failures — Server details — Latency, availability, consecutive failures

MCP Hub — Tool catalog — Input schema — Tool catalog — Input schema

MCP Hub — Version management — Immutable snapshots — Version management — Immutable snapshots

MCP Hub — Access permissions — Per-tool blocking — Access permissions — Per-tool blocking

MCP Hub — Gallery — One-click install — Gallery — One-click install

MCP Hub — Official MCP registry integration — Official MCP registry integration

MCP Hub — Observability — Success rate, P95 — Observability — Success rate, P95

MCP Hub — Call details — Instance linkage — Call details — Instance linkage

MCP Hub — Internal integration standards — Internal integration standards

MCP Hub — Standards compliance A/B/C grading — Standards compliance A/B/C grading

Read more Watch the demo

▶Agent Export · External RuntimesExecutable assets that plug into LangGraph and Crew AI

Export the agents you build in LangGraph or Crew AI format and connect them to your existing agent environments. The integration method, such as JSON, a Python module or an API endpoint, is reviewed to fit the target environment.

▶ Register and Run A2A Agents Register agents by URL and assign them to process steps Demo video

ㆍEnter a URL in the org chart’s ‘Create A2A Agent’ tab to register an agent with its name, description, and skills from its card.
ㆍAssign a registered A2A agent to a process step. The system sends a message and saves the agent’s response as the task result.
ㆍRegistered A2A agents appear alongside other agents in the org chart. Chat uses the same message-and-response flow.

Document · Voice Interfaces Work keeps moving even when you're not at a keyboard

▶ HWPX (Korean Hangul) Documents Built by Conversation Create and edit Hangul documents through dialogue 2 screens

ㆍUpload an HWPX (Korean Hangul word-processor) template to the chat room and make a request; the agent drafts proposals, reports, and other documents to match the template automatically.
ㆍCheck the preview on screen and revise in conversational AI editing mode with requests like "Change the project title" or "Make this section longer".
ㆍDownload the finished document instantly as an HWPX file — Zero-Touch automation for public-sector and administrative document work.

Generating an HWPX document by conversation

Editing an HWPX document in AI editing mode

Watch the demo

▶ Document Editor That Keeps the Original Format · Process GPT Office Edit Hangul, Word, Excel, PPT and PDF files with their formatting intact, and watch the agent write Video · 4 screens

ㆍHangul, Word, Excel, PowerPoint and PDF files attached to a task are opened and edited in their original format, with no conversion to a web page. Saving keeps the original and uploads the edited copy separately, and the task's attachment switches to the edited copy. The editor can be maximized or expanded to full screen.
ㆍThe editor's AI assistant receives the task step the document was opened in, along with the request from the previous step. Just saying "Polish it as the team lead asked" is enough for it to know what to change, and it leaves charts and heading styles as they are. The AI runs on the in-house language model server, so document content never leaves the company.
ㆍHand the work to a Process GPT agent in chat, and the agent finds the document that is open right now, fills in the blanks while the person watches, and saves it. A notice below the editor shows what the agent is doing.
ㆍThe editor is built on IeumOffice, open source under Apache 2.0, and ships with the original authors' notices and third-party licenses.

Demo (6 min 32 s, Korean narration and subtitles) · One Hangul draft goes from request → drafting → approval, and then an agent fills in an open Word report. Every screen is from a real run. · Watch on YouTube

A Hangul draft opened in its original format in the maximized editor — A Hangul draft in its original format — maximized editor

The AI assistant, given the task context, has filled in the empty sections — "As the team lead asked" — sections filled from the task context, with changes marked

The chat agent filling in an open Word report — The chat agent filling in the open report

An Excel settlement sheet opened in the same editor — Excel, Word, PowerPoint and PDF in the same editor, with the same assistant

Watch the demo

▶ Hands-Free Work with the Voice Agent Fill out task forms by speaking 1 screen

ㆍTalk to AI by voice to fill out and submit business forms without a keyboard. (Powered by OpenAI Realtime · gpt-realtime)
ㆍThe agent automatically recognizes the task's web form context and fills in the appropriate fields based on your conversation.
ㆍEnjoy natural conversations: the agent detects overlapping speech, pauses, and immediately applies corrections such as "I misspoke".

Voice agent window and a voice-filled quote request form — after correcting the quantity to 30 — Quote request form filled out and corrected by voice

Watch the demo (1:16)

▶ Call Bot: Processes Run over the Phone Phone interview results are recorded as-is 2 screens

ㆍWhen the process advances and a user has a to-do, the agent places a call and conducts the task (such as an interview) directly. (Twilio CPaaS integration)
ㆍIt identifies and verifies the user by phone number, then walks them through and handles the pending work items on their worklist by conversation.
ㆍAnswers given during the call are recorded automatically into the task form and submitted, with built-in fallback handling for off-prompt responses.

Phone interview answers recorded automatically

Watch the demo

Analytics · Observability Make what was executed traceable and accountable

▶ Execution ProfilerNEW Drill four levels deep into why an agent decided what it did 8 screens

ㆍClick an instance row to open its total elapsed time and token cost, with the execution segments unfolded as a waterfall.
ㆍWaterfall → task → language model call → tool call are chained parent-to-child, so you can see which decision invoked which tool.
ㆍPrompts, responses, and tool arguments can be inspected verbatim; tokens and passwords are masked at storage time.

Execution Profiler — Process analytics instance list — Process analytics instance list

Execution Profiler — Execution Profiler — Elapsed time, tokens, cost — Execution Profiler — Elapsed time, tokens, cost

Execution Profiler — Waterfall with tasks expanded — Waterfall with tasks expanded

Execution Profiler — Language model span details — Language model span details

Execution Profiler — Prompt tab — Messages by role — Prompt tab — Messages by role

Execution Profiler — Response tab — Requested tool and arguments — Response tab — Requested tool and arguments

Execution Profiler — Tool span — Arguments, results, errors — Tool span — Arguments, results, errors

Execution Profiler — Unsupported engine notice — Unsupported engine notice

Read more Watch the demo

▶ Lead Time BreakdownNEW Was that time spent by a person, by an agent, or waiting? 7 screens

ㆍSplits total elapsed time into human decision, agent execution, and queue wait to pinpoint where to improve.
ㆍBecause actual work time is often under 1% of lead time, there is a separate work-time axis, and collapsed waits remain as badges under the scale.
ㆍAggregating many instances reveals bottlenecks at the definition level, and segments without evidence are left blank rather than invented.

Lead Time Breakdown — Process analytics dashboard — Process analytics dashboard

Lead Time Breakdown — Lead time breakdown — 100% human decision — Lead time breakdown — 100% human decision

Lead Time Breakdown — Real-time axis — Bars get squeezed — Real-time axis — Bars get squeezed

Lead Time Breakdown — Work-time axis — Only actual work is expanded — Work-time axis — Only actual work is expanded

Lead Time Breakdown — Agent (blue) vs. human review (orange) — Agent (blue) vs. human review (orange)

Lead Time Breakdown — Language model span — Language model span

Lead Time Breakdown — Tool span — Tool span

Read more Watch the demo

▶ Tool AnalyticsNEW A third analysis axis after process and people 5 screens

ㆍTreats tools as a first-class analysis axis you can slice alongside process, department, and execution engine.
ㆍStates coverage before showing any numbers, and hides the totals row for non-additive measures such as failure rate.
ㆍDistinguishes the department that provides a tool from the departments that actually use it, exposing gaps between operational ownership and usage demand.

Tool Analytics — Coverage + tool × department cross-table — Coverage + tool × department cross-table

Tool Analytics — Axis switch dropdown — Axis switch dropdown

Tool Analytics — Tool usage by process axis — Tool usage by process axis

Tool Analytics — Failure rate — Totals row disappears — Failure rate — Totals row disappears

Tool Analytics — Per-tool status and top department–tool combinations — Per-tool status and top department–tool combinations

Read more Watch the demo

LEARN MORE

Get to know Process GPT through video and conversation

Product video · 2 min

Process GPT Podcast · Bringing AI into real work

01 · Why do AI pilots stall in real work?

Listen to two hosts discuss, in Korean, how Process GPT connects people, AI and existing systems. They walk through an invoice workflow example and four questions to ask before adoption.

An AI audio commentary generated by NotebookLM from the Process GPT product brochure and product description. The invoice workflow is a hypothetical design example to aid understanding; the actual scope of application is validated in each customer's environment.

Download audio (Korean) · 13 min 25 s Process GPT product brochure (PDF)

Case Studies

Agent-based process execution is already being proven in real business operations at major Korean enterprises.

Korea Electric Power Corporation

Overseas Business

Agent execution for generating legal·administrative documents
in overseas business

Turning document-intensive activities into AI agent activities inside the process

Challenge

Overseas power generation and transmission·distribution projects are document-intensive end to end: country-specific legal reviews, permit and administrative filings, contract documents, and more. Relying on individual staff experience meant long lead times and wide variation in deliverable quality.

Solution

The overseas business process was defined in BPMN, and the legal review and administrative document generation activities were deployed as AI agent activities. The agents draft documents by referencing internal regulations, standards, and country-specific materials, while staff only review, refine, and approve from the work item screen.

Results

PoC verified that document generation runs automatically inside process execution

Shorter drafting lead time; staff focus on review and judgment

Consistent formats and evidence — no more person-to-person quality variation

Generation history stays on the process instance for a full audit trail

SK Telecom

OSS (Operations Support System)

AI-agent-driven
OSS process capitalization and continuous improvement

Agents run the cycle of process identification → standards gap analysis → improvement

Challenge

OSS operations such as network design, provisioning, and fault response were scattered across teams and systems, with uneven documentation. It was hard to tell what counted as a process asset or where it stood against industry standards.

Solution

AI agents read existing operations documents, work history, and system information to identify the processes actually performed and structure them as BPMN process assets. They then automatically compare the identified processes against industry-standard process frameworks (eTOM and others) and present omissions, duplications, and inconsistencies in a gap analysis report.

Results

Scattered operations work accumulated as reusable process assets

Gaps against standards quantified and made visible — improvement priorities derived

A continuous improvement cycle of identify → compare → improve → re-identify established

Agents run it continuously, not people — assets never go stale

From document generation to process capitalization — agent execution proven in real enterprise operations.

Request a demo

Supply-Chain Demo Scenario One real cross-departmental case, taken all the way to the end

▶ Supply-Chain Delivery Risk ResponseNEW One incident spanning ERP, WMS, customs, and CRM, handled end to end 9 screens

ㆍHandles a single customs hold as it ripples through procurement, inventory, purchasing, and customer support — all within one process.
ㆍRather than centralizing data, it links it through the Ontology Studio knowledge graph and leaves computation to the source databases.
ㆍAll agents are read-only; a person approves only the decisions that spend money, and that judgment is kept on record.

Supply-Chain Delivery Risk Response — Four systems, four different item identifiers — Four systems, four different item identifiers

Supply-Chain Delivery Risk Response — Ontology knowledge graph — Ontology knowledge graph

Supply-Chain Delivery Risk Response — Records stay in the source systems — Records stay in the source systems

Supply-Chain Delivery Risk Response — BPMN — Human lane and agent lane — BPMN — Human lane and agent lane

Supply-Chain Delivery Risk Response — Procurement agent — Penalty calculation and evidence — Procurement agent — Penalty calculation and evidence

Supply-Chain Delivery Risk Response — Inventory and purchasing agents — Inventory and purchasing agents

Supply-Chain Delivery Risk Response — Alternate order approval (HITL) — Alternate order approval (HITL)

Supply-Chain Delivery Risk Response — Drafting the customer reply — Drafting the customer reply

Supply-Chain Delivery Risk Response — Every answer can be traced to its source — Every answer can be traced to its source

Read more Watch the demo

Our Products

Process GPT

From AI that speeds up individual work,
to AI that cuts lead time across the organization.

Individuals have gotten faster,
so why does the organization's work still take so long?

Agentic AI projects expected to be canceled

The gap in measurable business results

With End-to-End orchestration,
we connect the start of work to its final result.

Turn handoffs between departments into a process

Decisions that come with their evidence and accountability

A path for work that loops back, too

Improve against total completion time

FEATURES

Get to know Process GPT through video and conversation

Product video · 2 min

01 · Why do AI pilots stall in real work?

Case Studies

Case Studies

Agent execution for generating legal·administrative documents
in overseas business

Challenge

Solution

Results

AI-agent-driven
OSS process capitalization and continuous improvement

Challenge

Solution

Results

Architecture

Utilized technologies

Our Products

Process GPT

From AI that speeds up individual work,to AI that cuts lead time across the organization.

Individuals have gotten faster,so why does the organization's work still take so long?

Agentic AI projects expected to be canceled

The gap in measurable business results

With End-to-End orchestration,we connect the start of work to its final result.

Turn handoffs between departments into a process

Decisions that come with their evidence and accountability

A path for work that loops back, too

Improve against total completion time

FEATURES

Get to know Process GPT through video and conversation

Product video · 2 min

01 · Why do AI pilots stall in real work?

Case Studies

Case Studies

Agent execution for generating legal·administrative documentsin overseas business

Challenge

Solution

Results

AI-agent-drivenOSS process capitalization and continuous improvement

Challenge

Solution

Results

Related Solutions

Processes don't run alone

The knowledge graph agents reference

Decisions in the ontology, execution in Process GPT

What a process can't absorb becomes a system change

Hands and feet for legacy screens with no API

Architecture

Utilized technologies

From AI that speeds up individual work,
to AI that cuts lead time across the organization.

Individuals have gotten faster,
so why does the organization's work still take so long?

With End-to-End orchestration,
we connect the start of work to its final result.

Agent execution for generating legal·administrative documents
in overseas business

AI-agent-driven
OSS process capitalization and continuous improvement