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OrgForge
Pricing
- Model
- Free
Summary
Testing an AI agent against real corporate data is impossible before you have real corporate data — and using real corporate data is a compliance problem before you even start. OrgForge exists to break that deadlock.
OrgForge generates a deterministic, ground-truth corporate ecosystem: Confluence pages, JIRA tickets, Slack threads, Git PRs, Zoom transcripts, Zendesk tickets, Salesforce records, emails, and server telemetry — all parameterized to a target company shape or industry. Because the simulation is deterministic, the same seed produces the same dataset, so evaluation results are reproducible across runs. The ceiling appears when your evaluation scenario requires nuance from a specific real org's culture or data patterns — synthetic artifacts will not match those edge cases. Teams using OrgForge for RAG benchmarking get a controlled baseline; teams needing production-representative data for a specific enterprise client still have to build a separate data-collection pipeline.
Bottom line: OrgForge is the right starting point for benchmarking a retrieval system or AI agent against a controlled corporate knowledge base — but if your evaluation depends on the idiosyncrasies of a specific real organization's data, synthetic generation will not substitute for instrumentation on the actual system.
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Pros
Sign in to edit- Deterministic generation from a seed configuration, which means evaluation runs are reproducible and regression testing against a fixed dataset is possible without storing large static files.
- Cross-system causal consistency across Confluence, JIRA, Slack, Git, Zoom, Zendesk, Salesforce, email, and telemetry, so retrieval benchmarks can test multi-hop reasoning across sources rather than single-document lookups.
- Ground-truth labeling is built into the generation process, which means you can score agent answers against a known correct state without a separate annotation effort.
- Self-hosted, air-gapped operation via Docker, so teams under data residency or compliance constraints can run evaluations without routing synthetic corporate content through a third-party API.
- Insider threat and departure cascade simulation is a documented, first-class scenario type, which means security-focused agent evaluation — testing what an agent should and should not surface — has a ready-made data substrate.
Cons
Sign in to edit- Domain vocabulary is structurally plausible but semantically shallow: a generated pharmaceutical dataset will not reproduce the citation patterns, compound names, or regulatory filing language that a production agent in that vertical will encounter. Teams in regulated industries hit this ceiling when their first real-world agent evaluation fails on cases the synthetic data never generated, and they add a manual curation layer on top.
- There is no graphical interface and no hosted option — setup requires Docker familiarity and comfort reading Python project configuration. Teams without engineering capacity to configure and run a local container environment cannot adopt this without a developer handoff.
- The repository shows 16 stars and no open issues or pull requests at the time of the source snapshot, which signals limited community validation of edge cases in the generation logic. Teams that hit a generation bug have no community-sourced workarounds to draw from and must either debug the source or open a cold issue — the condition under which teams with tight timelines abandon this for a commercial synthetic data vendor with a support channel.
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About
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-13T13:18:13.185Z
Best For
Who it's for
- AI agent evaluation
- Retrieval-augmented generation testing
- Synthetic data for enterprise scenarios
- Grounded LLM output evaluation
What it does well
- Evaluating retrieval systems with ground-truth facts
- Testing enterprise AI agents on institutional knowledge
- Generating synthetic corporate datasets parameterized to any company or industry
- Simulating insider threat and departure cascade scenarios
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Frequently Asked Questions
- Is OrgForge free?
- Yes — OrgForge is fully free to use. There is no paid tier.
- Is OrgForge open source?
- Yes. OrgForge is open source.
- Can I self-host OrgForge?
- Yes. OrgForge supports self-hosting on your own infrastructure.
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Curated lists that include this category
OrgForge simulates weeks of enterprise activity and outputs a coherent, cross-system dataset spanning the tools most corporate AI agents are expected to reason over: wikis, ticketing, chat, version control, call transcripts, customer support records, CRM data, email, and server logs. The core workflow is parameterization-first — you configure a company shape (size, industry, scenario type), run the generator locally via Docker or docker-compose, and receive a dataset where every artifact is traceable back to a defined ground truth. That ground truth is the evaluation hook: you know what the correct answer is before you ask the agent.
The differentiating feature is determinism. Most synthetic data tools produce plausible-looking noise; OrgForge produces a causally consistent simulation where a JIRA ticket references the Confluence page that references the Slack thread that triggered it. That cross-system coherence is what makes it useful for evaluating retrieval systems — you can design a question whose correct answer requires reasoning across two or three sources, then verify whether the agent found the right path.
The tool also ships with documented support for insider threat and departure cascade scenarios, described in a dedicated INSIDER_THREAT.md file in the repository. This makes it relevant for red-teaming access control logic and testing whether an agent correctly surfaces or withholds information based on simulated org-chart context. Where OrgForge breaks down is in scenarios requiring domain-specific vocabulary depth — a generated dataset for a pharmaceutical company will have the structural shape of that organization but will not replicate the precise regulatory citation patterns or product nomenclature that a real QA agent would encounter. Teams building agents for highly regulated verticals report needing to augment the synthetic output with curated real examples.
The project is MIT-licensed, ships with a Dockerfile and docker-compose for self-hosted use, and carries no API dependency, so the entire generation pipeline runs air-gapped — relevant for teams whose data governance rules prohibit sending even synthetic org data to external services.