Engram

Founded by Dan Biderman in 2024

Train custom AI models that continuously learn from company data

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You can use Engram to train artificial intelligence models directly on your organization's internal data. Instead of relying only on public internet data or temporary chat retrieval, the system internalizes shared context across workspaces like Notion, Slack, and GitHub. It continually updates model memory so agents retain background knowledge without having to re-read files for every prompt. This reduces token costs, prevents forgetfulness, and allows development teams to build autonomous agents that understand project history, team decisions, and proprietary workflows.

What Engram does

Train models on private organizational repositoriesRetrain agent models continually on updated documentsQuery internal knowledge bases using plain languageExtract historical precedents from enterprise file systemsIntegrate contextual AI agents into team collaboration workspacesAnalyze project history across multiple communication channelsDeploy cost-efficient language model agents via APIContinual model training directly on internal workspace dataIntegration with Notion workspaces to power custom agentsDeep contextual learning across GitHub repositories and Slack threadsSignificant token efficiency reduction compared to repetitive context loadingAPI access designed specifically for autonomous enterprise agentsInternalization of firm-wide documents and precedentsScalable compute architecture focusing on memorization and retentionFrequent model updates to reflect constantly evolving company files