Frequently Asked Questions

Memlode is a company brain for enterprise teams. It captures the context behind decisions, incidents, exceptions, and repeated work so people and AI systems can use that knowledge later. The goal is not just to search documents. The goal is to preserve how the company actually works.

Memlode is for teams where important knowledge is scattered across people, conversations, tools, tickets, and old decisions. The first use case is engineering and operations teams that handle incidents, on-call work, reliability issues, and complex internal workflows.

Companies lose time when teams have to rediscover what happened before, why a decision was made, who knows the context, or whether a similar situation has already been solved. Memlode reduces that rediscovery work by turning past decisions and operational history into reusable knowledge.

No. Memlode is designed to capture knowledge during the work itself. It can learn from approved tools such as messages, tickets, incident timelines, pull requests, runbooks, and workflow updates. The intent is to reduce documentation burden, not add another place where people have to write summaries.

Most workplace knowledge tools help people find existing documents. Memlode focuses on decisions and workflows: what happened, why it happened, what was tried, what worked, what failed, and what should happen next time. That makes the system useful for both human teams and AI-assisted operations.

Memlode is intended to connect to the systems where work already happens, including communication tools, issue trackers, code platforms, incident tools, observability systems, and document repositories. Typical sources include Slack, GitHub, Linear, Jira, PagerDuty, incident management tools, Datadog, Grafana, and internal knowledge bases.

When an incident happens, Memlode can surface similar past incidents, likely owners, related code or system changes, relevant runbooks, known failure patterns, and previous decisions. Instead of starting from a blank page, the team gets a short, source-backed starting point for investigation.

Yes. The same company-brain model can apply to customer escalations, refund decisions, pricing exceptions, vendor approvals, compliance reviews, and other operational workflows. The product starts with a focused incident-response wedge because the pain is urgent and measurable.

Memlode is designed to show the source context behind important outputs. Instead of asking users to trust a black-box answer, the product can attach the original thread, ticket, pull request, incident record, or decision record that supports the recommendation. Low-confidence outputs should stay in review mode.

Customer workspaces are treated as separate environments. Memlode is designed so one customer's content, decision records, embeddings, and operational history are not mixed with another customer's workspace. Access controls should also respect source-system permissions, so users do not receive information they should not be able to see.

No. Customer data should not be used to train public foundation models. Where third-party model providers are used, enterprise controls and contractual terms should prevent provider-side training on customer content. Sensitive deployments can add stricter model, retention, and infrastructure requirements through enterprise agreements.

Memlode should earn trust gradually. The normal path is suggestion, then draft, then approval-gated action, and only then limited automation for patterns the customer has approved. High-impact actions should remain human-reviewed unless the customer has explicitly configured and authorized a specific automated workflow.

Setup depends on the customer environment and integration scope. A focused pilot usually starts with a small number of approved systems, a limited workflow, and clear success criteria. The goal is to provide useful context quickly while keeping security review, permissions, and rollout controlled.