Put agentic systems to work across your business

Build controlled, observable AI operations, from company knowledge and human-reviewed processes to browser automation, dashboards, and evals.

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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

Ten 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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Self-Healing Browser Automation

Automate logins, data collection, and visual checks in real browsers, with AI steps that repair broken selectors and secure code execution when you need it.

Target sitePlaywright steps or codeAuto heal and auth checksData, screenshots, alerts

Problem: Browser automations break the moment a page changes: selectors stop matching, logins expire, and scripts fail silently until someone notices the missing data.
How it runs on Heym: Build the flow as Playwright steps or sandboxed Python, let AI steps repair failed selectors mid-run, and restore sessions from saved cookies with a login fallback.
Control point: Code mode is disabled by default and always runs sandboxed, auth state is verified before steps execute, and every run keeps screenshots and captured network data for inspection.
Business outcome: Browser automations that survive UI changes and expired sessions instead of failing quietly.

Portal data extraction behind loginScheduled visual checks with screenshotsForm filling and submission flowsPrice and availability monitoringLegacy app automation without APIsSession reuse with saved cookies

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Team Skills and Skill Operations

Build a skill once, let your whole team run it, track every change with history and revert, and expose it to outside tools through MCP.

Skill buildTeam adoptionGoverned runsShared outputs and MCP access

Problem: Useful automation knowledge stays trapped in one person’s prompts and scripts. Teams rebuild the same capability, and nobody knows which version produced a given output.
How it runs on Heym: Package instructions, Python tools, and assets into a skill the whole team can attach to agents or expose through MCP, with every edit recorded and a one-click revert.
Control point: Skill history with revert, traces that show which skills each run used, and Drive permissions that keep generated files shared read-only with the right teams.
Business outcome: Shared capabilities the whole team can run and trust, with a clear record of what changed and when.

Company report generator skillData analysis skill with Python toolsDocument formatting and branding skillPII redaction skillInvoice calculation skillResearch and writing skills for agents

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Live Operational Dashboards

Turn workflow output into live dashboards where every chart is powered by a real, inspectable workflow.

Data sourcesWorkflow with Chart OutputDashboard widgetLive, cached charts

Problem: Operational numbers live in scattered sheets and ad hoc queries. Building a live dashboard usually means a BI project, and nobody can verify how a chart was computed.
How it runs on Heym: Every widget is a workflow ending in a Chart Output node, so the chart and its logic live together. Describe a widget and AI builds it, with TTL caching keeping it fast.
Control point: Each widget is inspectable: open its workflow to see exactly which data sources and transformations produced the chart, with every refresh recorded in workflow history.
Business outcome: Live dashboards the team can trust, because every chart traces back to a workflow you can open and audit.

Sales and pipeline dashboardsSupport quality metricsCost and usage trackingMarketing campaign metricsData pipeline health boardsKPI anomaly monitoring

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AI Observability and Cost Governance

See what every agent, model, and tool did on every run, and what it cost, before AI operations scale.

Workflows runTraces and spans recordedCost and latency rollupsInspection and tuning

Problem: Once agents reach production the questions start: why did this run cost so much, which tool failed, and what did the model actually see? Without answers, teams stall.
How it runs on Heym: Every run records a trace with payloads, timing across model and tool calls, and token cost per model. OpenTelemetry export ships the same spans to your own stack.
Control point: Costs compute from your own model price table, error rates surface by model and time range, and any suspicious run opens into its full payloads for audit.
Business outcome: AI operations you can explain: every output links back to its inputs, tool calls, latency, and cost.

Cost per workflow trackingModel comparison by cost and latencyDebugging failed agent runsAudit trails for AI outputsOpenTelemetry export to existing APMError rate monitoring

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Embedded AI Backends

Ship AI features by exposing workflows as APIs, MCP servers, and portals instead of building new backend services.

Product or client requestWorkflow API or MCP callAgents, logic, and integrationsStructured response

Problem: Shipping one AI feature usually means standing up a service: orchestration, retries, provider SDKs, auth, and deployment. The feature is small, the infrastructure is not.
How it runs on Heym: Build the behavior as a workflow, then expose it directly as a token-protected JSON API, an MCP server, or an authenticated portal with structured responses.
Control point: Bearer token auth on execute endpoints, portal-level authentication for end users, and full run history for every external call.
Business outcome: AI features in production without owning another service: one workflow serves your product, your agents, and outside tools.

JSON APIs for product featuresContact and intake form backendsMCP servers for agent clientsAuthenticated chat portalsAsync job processingInternal tool backends

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Evaluation and Release Gating

Test prompts, models, and agent outputs in the Evals tab before changes reach production workflows.

Eval suite and test casesRun across modelsScored resultsApproved configuration ships

Problem: Prompt and model changes ship on gut feeling. Output quality shifts silently, and regressions surface as user complaints instead of failed checks.
How it runs on Heym: The Evals tab turns that into a repeatable check: build a suite around a workflow, add test cases, then score several models at once with a judge model you choose.
Control point: A separate judge credential keeps scoring independent from the model under test, run history preserves prompt, input, and output snapshots for every evaluation, and guardrails plus human review stop failing outputs from reaching users.
Business outcome: Model and prompt decisions backed by recorded, repeatable evaluations instead of instinct.

Model selection across candidatesPrompt regression testingAgent output quality scoringHuman-in-the-loop scenario coverageJudge-scored answer qualityBlind comparison of competing models

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Workflow Monitoring and Alerting

Set thresholds on errors, run duration, LLM spend, and execution count, and be told when a workflow crosses one.

Pick a metricSet a threshold and windowBacktest itGet told when it breaks

Problem: Automation platforms record failures, slow runs, and spend, but they never volunteer any of it. A workflow that started failing at 2am is found when somebody opens a dashboard, or when a customer reports it first.
How it runs on Heym: The Alerts tab turns a metric into a rule: error count, run duration, LLM token or dollar spend, and execution count each get a threshold measured over a time window you choose, on one workflow or across everything you can access.
Control point: The wizard backtests every condition before you save it, reporting how often it would have fired over the past day or week, so a threshold that is far too low is caught before it pages anyone. Alerts fire once and stay quiet until the metric recovers, and each firing records the observed value, the window, and the runs behind it.
Business outcome: Failures, slowdowns, and runaway spend surface on their own, with enough context attached to act on.

Error bursts on a production workflowRuns that got slower after a model changeA daily LLM spend ceilingA trigger firing far more often than it shouldDuration regressions measured at p95Account-wide token budgets

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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
Web operations	Self-healing data collection behind logins
Engineering enablement	Shared agent skills with history and revert
Data and analytics	Workflow-powered dashboards and KPI monitoring
Product teams	Workflow APIs behind product features
AI platform governance	Traces, cost tracking, and eval gates

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.

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