Shared Experiment Workspace

Inven provides real‑time notebooks and experiment dashboards that multiple users can edit simultaneously. This eliminates the need for separate Jupyter servers and manual syncs, letting teams stay aligned on model progress.

Business outcome: Faster decision‑making and fewer duplicated efforts.

Dataset & Model Lineage

Every dataset upload and model checkpoint is automatically versioned, with metadata tags for easy retrieval. Auditors can trace exactly which data produced a given model version.

Business outcome: Reduces compliance risk and speeds up rollback when issues arise.

Scalable Cloud GPU Pools

Users can spin up GPU clusters on demand, choosing from a range of providers. The platform handles provisioning, billing, and teardown, freeing engineers from infrastructure chores.

Business outcome: Cuts cloud spend by only paying for active compute time.

Built‑in Git & Data Lake Connectors

Inven syncs with GitHub, GitLab, S3, and Azure Blob, allowing seamless code and data flow. This reduces context switching between version control and storage platforms.

Business outcome: Streamlines CI/CD pipelines for ML models.

Audit‑Ready Logs & Access Controls

Role‑based permissions and immutable logs satisfy internal and external audit requirements. Teams can enforce who can modify datasets or promote models to production.

Business outcome: Protects intellectual property and meets regulatory standards.

AI‑Powered Metric Summaries

An embedded analytics engine surfaces performance trends, drift alerts, and resource utilisation across experiments, similar to AI Powered Notes Taker’s summarisation capabilities.

Business outcome: Enables proactive model maintenance before degradation impacts customers.

• Proprietary private market dataInven indexes 28M+ companies, 3M+ transactions, 430M+ verified professional contacts, and covers 160+ markets, including off-market companies that never appear on traditional databases.
• AI built for M&A workflowsInven's AI Engine automates deal sourcing, market and competitor analysis, company materials preparation, and portfolio/pipeline monitoring, with agentic workflow automation that runs repeatable research tasks.
• Proven adoption by leading firmsOver 1,000 leading M&A firms use Inven, with testimonials citing 4–5× faster target identification, 10× faster market research, and 3× more buyers per mandate.
• Integrates with existing toolsInven connects wherever your team already works, including availability in Claude via MCP, and offers integrations, security, and compliance features as part of its product.

• Specific customer metrics are anecdotalWhile the site quotes metrics like '4–5× faster' and '10× faster,' these come from individual testimonials and case studies, not independent benchmarks or controlled studies.
• No detailed pricing or contract termsThe website does not disclose pricing, subscription tiers, or contract lengths. You must book a demo to get details, so costs are not publicly verifiable.
• Integration scope is not fully specifiedThe site mentions integrations and MCP for Claude, but does not list all supported platforms, data sources, or the exact nature of the integrations beyond that.
• Data coverage claims are broadClaims like 'every major market' and 'no blind spots' are marketing statements. The actual depth and recency of data across all 160+ markets are not independently verified on the site.

Enterprise model governance

Financial institutions can enforce strict audit trails for credit‑risk models, leveraging Inven’s immutable logs and role‑based access. This satisfies regulator demands without building custom tooling.

Rapid prototyping in R&D labs

R&D groups spin up GPU clusters for weekend hackathons, then archive experiments for later review, cutting prototype cycles from weeks to days.

Cross‑team ML deployment pipelines

Data engineers use Inven’s Git connectors to push vetted models directly into CI/CD pipelines, ensuring production code matches the trained artifact.

Academic collaborations

University labs share datasets and notebooks with external partners, maintaining version control and reproducibility across institutions.

Sign up for the free tier and create your first workspace.

Connect your preferred data lake (e.g., S3) via the Integrations tab.

Launch a GPU pool, open a shared notebook, and import your dataset.

Run an experiment, tag the run, and invite teammates for review.