AI-first technology for modern teams with fast response times
ilert is the AI-first incident management platform designed from the ground up as a single application and covers the entire incident response lifecycle.
Share your scheduling needs in a simple, chat-like interface. Add team members, rotation rules, and timeframes — and get a ready-to-use on-call calendar everyone can access.
Let AI take the call
Introducing the ilert AI Voice Agent—your first responder for calls, gathering key details and informing your on-call engineers.
Status updates in no time
ilert AI analyzes your system and incidents, offering quick updates and managing communications for efficient issue resolution.
ilert Responder – your real-time incident advisor
ilert Responder is an intelligent agent that analyzes incidents in real time. It connects to your observability stack, investigates alerts across systems, and surfaces actionable insights, without taking control away from your team.
Features
Analyze logs, metrics, and recent changes autonomously
Identify root causes and similar past incidents
Suggest responders, rollback paths, or related service
Ask questions in natural language and get direct, evidence-backed answers
Integrations
Get started immediately using our integrations
ilert seamlessly connects with your tools using our pre-built integrations or via email. ilert integrates with monitoring, ticketing, chat, and collaboration tools.
See how industry leaders achieve 99.9% uptime with ilert
Organizations worldwide trust ilert to streamline incident management, enhance reliability, and minimize downtime. Read what our customers have to say about their experience with our platform.
ilert now offers a native integration with Apica that connects telemetry events to ilert’s alerting, on-call, and incident communication. It helps SRE, DevOps, and IT operations teams turn detection into action faster, reduce alert noise with the aid of AI, and keep stakeholders informed without unnecessary notifications.
Highway to faster development
Mistakes are inevitable if you want to move fast and expand your product. But they shouldn't slow you down. Apica and ilert provide tools to make product changes less stressful and IT incidents manageable. Two solutions prepare you for unpredictable events and help you meet unexpectedness with helpful tools at hand.
The fastest way to reduce time from detection to resolution is to shorten the path from signal to the right human with the right context. Apica detects performance and availability issues across websites, apps, APIs, and more; ilert turns those signals into alerts tied to escalation policies and teams, so responders see ownership and next steps immediately. That means fewer handoffs, quicker acknowledgment, and fewer minutes lost before mitigation starts.
Add ilert AI on top to group similar events by content similarity and deduplicate replications, and your on-call stays focused on the primary incident rather than clearing look-alikes. When a failing Apica check returns to OK, ilert automatically resolves the linked Alert, preventing stale notifications from lingering.
Apica capabilities and strengths
Apica Ascent offers a modular approach to telemetry data management. It includes four four products: Fleet for agent management, Flow for telemetry pipelines, Lake for storage, and Observe for analytics.
Fleet deploys and manages OpenTelemetry and Fluent Bit collectors at scale, making it straightforward to start streaming logs and metrics.
Flow gives “never block, never drop” pipeline control with InstaStore-backed infinite buffering, real-time transform/enrich/route, elastic Kubernetes-native scaling, and 200+ integrations with existing stacks like Datadog, Elastic, Kafka, and S3.
Lake is a single-tier, object-storage data lake with patented InstaStore for indexed, on-demand access and long-term retention.
Observe correlates logs, metrics, traces, events, and web performance in one view and adds automatic anomaly detection, root-cause analysis, dashboards, alerting, and reporting.
Apica can reduce observability spend by up to 40% by decoupling compute from storage, supporting any object store, and letting teams choose what to index and when. The platform is ISO 27001 and SOC 2 certified and supports SaaS, hybrid, and on-prem deployments.
Integration features
Here are the capabilities of the native Apica and ilert integration that users will benefit from:
Native triggering: Apica issues/outages create alerts in ilert via a dedicated Apica alert source; setup is point-and-click on both sides.
Auto-resolve: When an Apica alert returns to OK, the linked ilert alert is resolved automatically.
On-call routing and escalations: Choose an escalation policy during setup so Apica-originated alerts page the right on-call and follow your escalation rules.
Noise reduction with intelligent grouping: enable alert grouping and filtering with the help of ilert AI to collapse near-duplicates and concentrate only on what matters.
Event flows for enrichment and control: use visual ilert Event flows to branch on conditions (e.g., severity/support hours), route, or suppress Apica events before they page.
How to use the integration
To start using Apica and ilert, you need to have accounts. Here are the registration links. Both solutions offer Free trials and plans:
To start sending Apica events to ilert, navigate to the Alert sources menu at ilert and choose the Apica tile. The connection is straightforward and takes no more than five minutes. Find the step-by-step guide in the ilert Documentation.
We are happy to help
If you have any remaining questions, please don't hesitate to reach out to ilert or Apica's team.
The ilert AI Voice Agent is designed to transform how on-call engineers handle urgent calls. Instead of waking engineers at 3 a.m. with minimal context, the AI Voice Agent collects essential details first and routes calls intelligently based on relevant, up-to-date information.
The agent works hand in hand with ilert’s Call Flow Builder – a visual tool that lets users design custom call flows by connecting configurable nodes. Each node represents a step in the call handling process, and the AI Voice Agent is one such node.
This means you can drop the AI into exactly the right place in your call handling logic, making the process seamless and highly customizable.
In this article, we’ll explore the problem it solves, its construction, how it delivers natural and context-aware conversations, and how we ensure it remains secure and reliable in production.
Beta Notice: The ilert AI Voice Agent is currently available in Beta. Users with the Call Flow Builder add-on can request early access by contacting support@ilert.com.
The problem we’re solving, and why it matters for on-call engineers
On-call engineers often receive urgent calls with minimal context, forcing them to ask repetitive questions before they can take action. This wastes valuable time in high-pressure situations.
The ilert AI Voice Agent addresses this by:
Saving time: The AI collects key details before an engineer is called, allowing them to start troubleshooting immediately instead of asking basic qualifying questions. It also reduces unnecessary escalations by checking for open incidents and informing callers if the issue is already being handled.
Visual call flow integration: Add AI Voice Agent nodes directly into your call flow with an easy-to-use interface, so it becomes part of your existing logic without manual workarounds.
Customizable information gathering: Define exactly what data is collected, such as caller name, contact number, email, incident description, affected services, or custom fields.
Architecture: How the ilert AI Voice Agent works
Under the hood, the AI Voice Agent is designed for modular, configurable interactions with low latency.
Key components:
WebSockets – Provide a low-latency channel for conversational AI with OpenAI.
Twilio integration – Streams live audio to and from callers.
Visual flow builder – Configure AI Voice Agent nodes directly in the Call Flow Builder.
Modular configuration:
Intents – Pre-built or custom, define how calls are routed based on the caller's purpose.
Gathers – Structured data collection (e.g., contact details, incident descriptions).
Enrichment – Optionally pull data from configured sources such as ilert Status Pages, service states, open incidents, or active maintenance windows.
Audio messages – Fully customizable greetings and prompts.
Fallback handling – A “catch-all” branch for unmatched conversations.
During the development of the AI Voice Agent, the team faced several complex technical challenges.
One of the first hurdles was tracking who was speaking at any given time. Both Twilio and OpenAI send speaker events, and the system needed to reliably determine whether the bot or the user was speaking in real time. This was essential to avoid interruptions or missed messages during a conversation.
Another major challenge was ensuring a natural conversation flow. Creating smooth, human-like interactions required extensive prompt engineering and fine-tuning. The pacing, tone, and responsiveness of the AI had to be carefully controlled to make the experience feel intuitive and engaging for users.
Finally, synchronizing multi-stream connections proved to be a critical task. The system had to maintain accurate state information between Twilio streams, OpenAI responses, and ilert’s backend. This synchronization was vital for preserving context consistency throughout the conversation.
Making conversations natural, accurate, and context-aware
The Voice Agent goes beyond traditional voice menus by combining intent recognition with optional context enrichment.
With configurable context enrichment, the agent receives intents, gathers potential follow-up nodes, and captures the caller’s number during call initialization. If enrichment is enabled, it can also access additional data, such as open incidents, current service states, and active maintenance windows. This allows the agent to provide more relevant and timely responses.
Through intent-based routing, the system matches the caller’s intent to the appropriate branch of the call flow, enabling faster and more accurate resolution of requests.
Security, compliance, and observability in production
Reliability and compliance are built in from the start. Here are three major principles:
Stateless design: No persistent storage of caller data between requests.
System prompts with operational rules: The AI follows strict, pre-defined guidelines to ensure security and consistent responses.
Detailed call logging: Logs all call events for troubleshooting and performance review.
Lessons learned
During development and early Beta testing, we learned a great deal about delivering smooth, reliable AI-powered conversations. Allowing the AI to be interrupted by the user turned out to be a key feature – many callers prefer to skip the rest of a question or add details they forgot earlier.
However, this made it even more important to track who is speaking at any given time. By monitoring speaker activity, we can detect long periods of silence and prevent calls from running indefinitely when no one is talking.
Coordinating multiple live connections (Twilio, OpenAI, ilert backend) still required careful orchestration to ensure the call state stayed synchronised at all times. Prompt engineering proved essential in making conversations sound natural while ensuring the AI followed operational rules and safety guidelines.
What’s next?
The Beta release has already sparked new ideas for improvements. We plan to extend logging capabilities and provide full recordings of conversations for review and compliance purposes.
To improve flexibility, the AI Voice Agent will gain adjustable speaking speed and verbosity settings, allowing teams to fine-tune the interaction style. We are also exploring ways to detect when callers are frustrated and offer them an immediate option to speak with a human operator.
On the transcription side, we aim to enhance the ilert user experience by moving from Twilio’s built-in transcription to AI-powered voice transcription. This will provide more accurate and context-aware briefings for on-call engineers before a call is connected.
Conclusion
The ilert AI Voice Agent bridges the gap between urgent incident calls and the actionable details engineers need to respond quickly. By integrating directly with ilert’s incident management platform, it delivers natural, context-aware, and secure conversations while giving teams the flexibility to adapt the interaction to their workflows.
With upcoming features such as multilingual support, transcripts, and deeper integrations, the Voice Agent will further reduce on-call friction and accelerate incident response.
On August 2, 2025, a key part of the EU AI Act comes into force. It has serious implications for how you manage incidents related to artificial intelligence.
While the full regulation will not apply until 2026, new obligations for providers of general-purpose AI (GPAI) models begin this summer. If you are building or deploying AI-powered services in Europe, the clock is ticking.
The good news is that if you already have a structured incident response process, you are more prepared than you think. But staying compliant and avoiding penalties will require some important updates to how incidents are detected, documented, and communicated across your organisation.
In this blog, we’ll break down:
What exactly is changing in August 2025.
How organized incident response fits into the EU AI Act timeline.
What high-risk and general-purpose AI obligations actually mean.
And how ilert helps teams stay compliant.
What is the EU AI Act, and who does it apply to?
The EU AI Act is the world’s first comprehensive regulatory framework for artificial intelligence. Its main objective is to ensure that AI systems used in the European Union are safe, transparent, and respect fundamental rights.
Adopted in 2024, the Act uses a risk-based approach to classify AI systems into four levels: unacceptable, high, limited, and minimal risk, with specific requirements for each.
The Act applies to a broad range of actors in the AI value chain, including:
AI system providers (developers or vendors).
Deployers (organisations using AI systems).
Importers and distributors of AI technologies.
Even some downstream users in the EU, regardless of whether the provider is based in the EU or not.
In other words, if your company offers or operates AI-powered services in the EU, especially in areas defined as "high-risk" (like recruitment, healthcare, or finance), you are likely subject to compliance obligations.
To determine this, assess whether your AI system affects safety, rights, or critical services under the AI Act’s risk categories.
The AI Act also affects developers of general-purpose AI models (GPAI) such as LLMs. From August 2025, these providers will need to meet new transparency, documentation, and risk mitigation requirements.
What changes in August 2025
Starting August 2, 2025, new rules under the AI Act take effect for providers of general-purpose AI (GPAI) models, including large language models (LLMs).
According to the European Commission, GPAI providers must comply with the following requirements:
Transparency: Ensuring users are explicitly informed when content has been generated by an AI system.
Data disclosures: Publishing a public summary of training data sources and processing methods.
Risk mitigation: Assessing and reducing systemic risks from powerful AI models.
What obligations are already in effect?
As of February 2, 2025, the EU AI Act has already brought two key sets of obligations into effect:
1. Banned AI practices (unacceptable risk)
The AI Act prohibits a list of AI systems considered to pose an unacceptable risk to safety, human rights, or democratic values. These include:
AI systems that manipulate behaviour or exploit vulnerabilities.
Emotion recognition in workplaces or educational settings.
Predictive policing based on profiling or past offences.
Untargeted scraping of images or videos to build biometric databases.
2. AI literacy obligations
The Act also introduces AI literacy requirements for providers and deployers. This includes:
Ensuring those who use or oversee AI systems are trained to understand how the system works.
Recognising biases, risks, and limitations.
Knowing how to monitor and intervene when needed.
These rules are designed to increase awareness and safe use of AI across industries, even before high-risk systems face stricter rules in 2026.
How does incident management play a central role in EU AI Act compliance?
The EU AI Act compliance centres on five operational duties. We have mapped each duty to its article in the regulation and paired it with a clear next step for incident-response teams.
Providers of high-risk AI must keep tamper-proof logs so authorities can reconstruct system behaviour.
Next step: enable machine-generated timelines that capture every alert, escalation, rollback and mitigation action, then export those logs in a regulator-ready format.
Serious incidents or malfunctions must be reported within seventy-two hours of becoming aware of them.
Next step: use playbooks that notify engineering, legal and communications at the same time so reporting can start while the incident is still unfolding.
Regulators expect clear roles and responsibilities during an incident.
Next step: give legal, security and leadership real-time access to the incident timeline and provide controlled status page updates so external stakeholders receive verified information without extra meetings.
Automated post-incident evidence (Article 73 §4)
Records must be stored for inspection and include description, impact, corrective measures and affected parties.
Next step: generate a post-incident report automatically from the live timeline, then add impact analysis and follow-up actions so every report contains the same compliance fields.
Providers of general-purpose AI models must assess and mitigate systemic risks on an ongoing basis.
Next step: integrate monitoring signals such as model-output drift or inference-error spikes so threshold breaches automatically open an incident and trigger the steps above.
Because the AI Act shares principles with GDPR, NIS2 and DORA, timely notifications, transparent documentation and clear accountability, along with capturing all incident data in one workflow, let you satisfy multiple regulations with the same evidence set.
How ilert meets these requirements
Automatic logs – every alert, escalation and response action is stored in a tamper-proof timeline you can export for regulators.
Fast notification – multi-channel alerting and playbooks notify engineering, legal and comms at once, supporting the 72-hour rule.
Cross-team visibility – role-based views and status pages keep security and leadership informed without extra meetings.
Post-incident evidence – one click turns the live timeline into an audit-ready post-mortem with impact, measures and follow-ups.
The EU AI Act isn’t just another compliance checkbox. It’s a signal that organisations need to rethink how they manage risk in an AI-powered world. For companies deploying or building high-risk AI systems, strong incident response practices are no longer optional. They’re essential.
Whether you’re preparing for the August 2025 requirements or the full rollout in 2026, the key is to embed compliance into your operational workflows, not bolt it on later. With tools like ilert, much of this is already within reach: fully-automated alerting and escalation, cross-team coordination, real-time documentation, and audit-ready postmortems.
The best part? When incident response is done right, compliance becomes a natural by-product, not a burden.
Quick summary
The EU AI Act introduces strict incident reporting obligations, with some rules already active as of February 2025. By August 2025, providers of general-purpose AI models must meet new requirements around transparency, safety, and copyright.
With Article 73 enforcing a 72-hour reporting window for high-risk incidents, having a structured, automated incident response process in place is the most efficient way to stay compliant. ilert makes this achievable by helping teams document incidents in real time, streamline cross-functional collaboration, and reduce the overhead of regulatory reporting.
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