Unite brings together PulseCheck inauthentic behaviour detection, Signal narrative risk intelligence, and AI-driven response automation into a single integrated platform. Full-stack behavioural trust infrastructure for enterprise teams.
Unite runs PulseCheck bot detection and coordinated behaviour analysis continuously across all monitored channels. The moment inauthentic activity is detected, it feeds directly into the intelligence and response layers without manual handoff.
Signal's narrative clustering and risk scoring runs continuously inside Unite, tracking how coordinated campaigns evolve from seeding stage through amplification to media velocity. Your team sees the full picture, not just isolated alerts.
Set risk thresholds that automatically activate response playbooks. When a coordinated campaign crosses a defined boundary, Unite alerts the right people, prepares the right briefings, and initiates the right response, in minutes not hours.
A single dashboard showing the full narrative risk picture across your organisation. Live threat feeds, risk trajectory scoring, network maps, and one-click PDF briefings for leadership, investor relations, and board reporting.
Unite shifts your posture from reactive crisis response to proactive narrative defence. With 6 to 12 hours of early warning built into the platform, your team has time to prepare, coordinate, and act before a campaign reaches critical mass.
Unite archives every signal collected. Replay mode means scoring algorithms can be iterated against historical data without additional API cost. Detection accuracy improves continuously as the shared profile layer grows.
Defend scans a piece of media, by drag-and-drop, upload or a URL, and returns an explainable authenticity verdict in seconds, with no media retained beyond the verdict metadata. The same checks are callable from AI assistants as governed, audited tool calls.
Every action Unite's agents take is risk-classed, budget-capped, identity-scoped and logged in full. LLM Protect, the audit layer built beneath the agents, makes the complete timeline of agent activity inspectable inside Unite.
Unite is the defence layer that closes the loop. It combines detection, intelligence and automated response into one platform, so a verified coordinated campaign moves from alert to action in minutes - not the hours a manual handoff costs. For how the three layers fit together, see detection, intelligence, defence: the unified narrative-threat stack.
Deepfakes and narrative attacks are one threat, so Unite handles both. Defend scans a piece of media, by drag-and-drop, upload or a URL, and returns an explainable authenticity verdict in seconds: an ensemble manipulation probability, an authenticity band and a per-signal breakdown. No media is retained beyond the verdict metadata.
The same authenticity operations are agent-native. An analyst can ask an AI assistant to scan a URL, poll a scan, summarise a brand’s authenticity, or scan an account, executed through the platform’s governed tool surface: identity-scoped, permission-checked and audit-logged. The verdict sits alongside the behavioural and coordination signals for the campaign around the media, not as an isolated score. To understand the science, see how deepfake detection works; for the full picture, see synthetic media detection and Signal.
Autonomous agents that act on your behalf are exactly what security and risk teams are paid to question. Most vendors ship agents and ask for trust. Unite ships agents whose every action is classed, capped, scoped and recorded. You do not have to trust our agents - you can audit them.
Four guarantees apply to every agent action, today:
This is LLM Protect - the policy and audit layer AI Uniti built underneath its own agents. It is not an add-on and it is not optional: it is on for every agent, every action, every customer. Inside Unite, the LLM Protect view shows the complete timeline of agent activity - what was invoked, with what inputs, what came back, and what it cost.
Customer-defined policies and active enforcement are on the roadmap; the audit layer they will control is already running.
For the wider discipline, read the access layer your LLM security stack is missing, what LLM guardrails cover and what they leave exposed, why prompt injection is not the biggest risk in your AI stack, the CISO’s guide to governing enterprise AI agents, building TrustOps for the AI era, and Replay: the fifth tenet of TrustOps.
Every tool call an agent makes is logged in full - which tool was invoked, the complete arguments, a snapshot of the result, its status and duration. The LLM Protect view inside Unite shows that timeline, and activity is identity-scoped, so your organisation sees its own agent activity and nothing else.
No. Credit-spending agent actions run inside a server-created budget context with a hard cap, and the cap is enforced at the ledger, not the prompt. An agent cannot spend beyond its ceiling.
Every tool an agent can call carries a declared risk class, from read-only through to outward-facing. Low-risk actions run autonomously within capped playbooks; outward-facing actions are gated on human approval. The classification is enforced server-side, not left to the model.