Plan vs. shipped vs. reality
Planning docs, merged pull requests, and live status are reconciled automatically. The drift between intent and execution is made visible.
New CBN rules make manual AML non-compliant. See what's required — and how MaiGuard helps.New CBN rules make manual AML non-compliant.
Read the briefAn agentic AI layer that reasons across scattered enterprise signal, uses tools to dig in, and takes supervised actions. Built to run MaiGuard, now built for you.
We use it internally, every day, to run MaiGuard.
why did we ship the new onboarding flow?Reconstructed from the original thread, the pull request that shipped it, and the document that motivated it.
Every transaction MaiGuard scores, every alert our analysts clear, every decision we make is informed by it. Now we are opening it up: the same engine that finds signal in transaction noise, pointed at your operational noise.
Every organization already contains the intelligence it needs. The problem is not creation. It is connection.
Not find me the document, but what is happening across this domain.
Not raw data, but the patterns, trends, and meaning underneath it.
Not another report, but recommendations and workflows that move work forward.
Not isolated tools, but one unified, durable memory of the business.
Conversations, code, documents, customers, operations, and your fraud and transaction signals, unified into one contextual model, with privacy-aware processing and PII protection built in.
Slack, email, internal messaging.
GitHub, CI/CD, deploys.
Drive, knowledge bases, specs.
Support tickets, feedback, CRM.
Issues, sprints, incidents.
MaiGuard's core: the same signal-from-noise engine, on your own data.
Connect your tools with read-only, privacy-aware access. You choose what is in scope.
A unified, continuously updated model of your organization forms as signal flows in.
Ask questions, surface patterns, and get recommendations and workflows that move work forward.
Every team works from the same unified memory of the business, asking the questions that matter to them.
What changed this sprint, why did it slip, and what led to it?
What is actually happening across the company this week?
Which accounts are heating up or going quiet, and why?
What are customers telling us in aggregate, and what is trending?
Where is execution drifting away from the plan we set?
What themes are gaining momentum across our market right now?
Representative outputs MaiGuard Intelligence surfaces by reasoning across the indexed model. Examples shown for illustration.
Planning docs, merged pull requests, and live status are reconciled automatically. The drift between intent and execution is made visible.
Reconstructs a past decision from the original thread, the pull request, and the document that motivated it, gathered in one place.
Support tickets, sales calls, and internal chatter converge on the same theme, surfaced while it is still early enough to act.
A cluster of signals across customers, operations, and transactions points to an issue forming before it reaches the numbers.
MaiGuard Intelligence is designed for regulated environments. Connection is read-only, access is least-privilege, and your data stays isolated.
Data handling is aligned with the Nigeria Data Protection Act 2023 (NDPR), with statutory compliance filings up to date.
PII protection and privacy-aware processing are built into how signal is ingested and reasoned over.
We read to build context. We do not write back into your systems of record.
Every connection requests the minimum scope it needs, and nothing more.
Your contextual model is isolated to your organization. It is never pooled with anyone else's.
Sources are opt-in. You decide what is in scope, and you can revoke it at any time.
Data is encrypted in transit and at rest across the platform.
MaiGuard Intelligence gives every person the collective memory and reasoning of the whole organization, so decisions are made with more context, more confidence, and more speed.