Put agentic systems to work across your business

Build controlled, observable AI operations for company knowledge, human-reviewed processes, and multi-agent work.

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Built for the teams responsible for operational AI

Legal, finance, sales, and support benefit from these systems. The teams below are the ones who build and run them.

AI Teams

Build agents grounded in company data, connect tools, test outputs, and inspect every model and tool call.

Platform Teams

Provide a shared runtime for deploying, observing, and governing agentic systems across the organization.

Automation Teams

Turn business processes into workflows that combine deterministic logic, AI agents, and human decisions.

Solutions for teams putting agentic systems to work

Three solution areas where Heym is strong today, each backed by real workflows you can import and run.

Knowledge and Policy Systems

Give employees and customers answers grounded in approved company knowledge, with traceable sources and human review where needed.

Documents and dataRAG and agentsReview or validationAuthenticated answer or portal

Problem: Company knowledge lives in scattered documents, wikis, and inboxes. People answer the same questions repeatedly, and nobody can verify where an answer came from or whether it reflects current policy.
How it runs on Heym: Upload documents into managed vector stores, wire RAG and agent nodes into a workflow, and publish it as an internal assistant or an authenticated portal. Answers keep their sources, and sensitive replies can wait for review before they reach the user.
Control point: Guardrails filter unsafe content, human review gates sensitive answers, and execution traces record every retrieval and model call.
Business outcome: One controlled system that answers from approved knowledge instead of guesswork, with an audit trail for every answer.

Internal knowledge assistantLegal document assistantCompany policy assistantCustomer support knowledge systemAuthenticated client portal

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Human-Reviewed Operations

Automate sensitive business processes while keeping people involved before important decisions or actions move forward.

Business requestAgentic workflowHuman approvalApproved action or output

Problem: Legal, finance, and compliance work cannot be fully delegated to AI, but running it entirely by hand does not scale. Teams need automation that stops and asks before anything important happens.
How it runs on Heym: Model the process as a workflow where agents do the analysis and preparation, and human-in-the-loop checkpoints pause execution until a reviewer approves. Execution state is preserved, so approved work continues exactly where it stopped.
Control point: Human-in-the-loop checkpoints before critical actions, guardrails on model output, full workflow history, and Slack or email notifications to reviewers.
Business outcome: Sensitive processes run faster without giving up the approval steps your team is accountable for.

Legal document processingFinance review workflowsSales research and outreach preparationSupport escalationContent approvalCompliance-sensitive operations

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Multi-Agent Research and Processing

Coordinate specialized agents to collect information, analyze findings, and produce structured business outputs.

Research requestSpecialist agentsEvaluation or reviewStructured report

Problem: Research and document-heavy work needs multiple skills at once: collecting sources, extracting facts, checking quality, and writing up results. A single prompt cannot carry that whole chain reliably.
How it runs on Heym: A lead agent delegates to named specialist sub-agents and sub-workflows, each with its own tools and instructions. Independent branches run in parallel, outputs are structured, and evals or a review step check quality before the report ships.
Control point: Structured outputs, evaluation steps, cost and latency tracking, and traces that show what every agent, model, and tool did.
Business outcome: Repeatable research and processing pipelines that produce consistent, structured deliverables instead of one-off documents.

Market and competitor researchAccount researchDocument classification and extractionReport generationMulti-source data processingCampaign planning

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One platform from workflow design to controlled operation

The same four capabilities carry every solution above.

Combine agents and deterministic logic

Build controlled processes without handing every step to AI.

Inspect every execution

See what each agent, model, and tool did on every run.

Keep humans in control

Approve critical outputs and actions before they move forward.

Connect and deploy on your terms

Use your existing tools, models, and infrastructure, in the cloud or self-hosted.

A real workflow, end to end

This is the actual node graph of the RAG Q&A Agent template, the starting point for knowledge and policy systems. Import it and adapt it to your documents.

RAG Q&A Agent

View template

Sticky Note

Before you run

Fill in the storage IDs, dataset names, or file targets that belong to your workspace.

Sticky Note

RAG Q&A Agent

Search your Qdrant vector store for relevant context, then answer with an LLM — grounded in your own documents.

Main path

UserQuestion -> RagSearch -> AnswerLLM -> Answer

UserQuestion

RagSearch

AnswerLLM

GLM-4.7

Answer

1
What goes in
Company documents indexed into a vector store, plus a user question.
2
Agents and tools
A RAG node retrieves matching passages and an agent composes the answer with sources.
3
Where people step in
Guardrails and an optional review step gate answers before they reach the user.
4
What comes out
A grounded answer with traceable sources, delivered in a portal or chat.
5
How it is tracked
Execution traces record every retrieval, model call, and decision for inspection.

Where teams apply these solutions

Business area	Example solution
Legal	Document analysis with human approval
Finance	Evidence collection and review workflows
Sales	Account research and outreach preparation
Support	Knowledge-grounded responses and escalation
Internal operations	Policy assistant and request processing
Marketing	Multi-agent campaign research and content review

A legal team is currently testing document-based agentic workflows with human review before sensitive outputs move forward.

Have a process you want to turn into an agentic system?

Show us the workflow, data sources, tools, and human decisions involved. We will help map it to Heym.

Explore workflow templates