In the race to build faster, ship globally and operate across increasingly fragmented tech stacks, one metric quietly determines whether a company earns customer trust, or loses it: reliability.
But reliability engineering is collapsing under its own weight. AI is helping us write more code faster and roll out the changes at an even faster pace. Systems are scaling faster than teams can keep up; incidents now span dozens of microservices; monitoring dashboards are multiplying instead of simplifying; and SREs are drowning in alerts that tell them something is wrong but not why. The result? Mean Time To Resolution (MTTR) is trending upward, not down, even as companies pour more money into observability tools and headcount.
Enter a new category rising inside forward-thinking engineering orgs: AI-powered SRE teammates. Leading the charge is Sherlocks AI, a platform designed to act not as another dashboard, but as an autonomous reliability engineer embedded directly into a team’s workflow.
The Complexity Crisis No One Wants to Talk About
Modern infrastructure isn’t just complex, it’s unknowable by any single human.
A mid-market SaaS company today might run:
- 100+ microservices
- Distributed data stores across regions
- CI/CD pipelines producing dozens of daily deployments
- Logs, traces, and metrics scattered across 5–12 different tools
- A rotating on-call schedule where context is lost week to week
Even the best SREs spend most of their time firefighting. Industry studies consistently show teams waste 60–80% of engineering hours on operational toil, triaging incidents, reconstructing timelines, searching logs and guessing root causes under pressure.
“The problem isn’t that companies lack data,” says Gaurav Toshniwal, founder and CEO of Sherlocks AI. “It’s that none of the tools they use actually explain what’s going on. They alert you to symptoms, not causes,and your team is left stitching the story together manually.”
It’s this gap between observability and meaningful insight, that Sherlocks AI was built to close.
From Observability to Autonomous Reliability
Sherlocks AI positions itself as a 24/7 autonomous expert that sits inside a team’s Slack/MS Teams workspace, continuously learning the behavior of every system, every deployment, and every historical incident.
Instead of forcing SREs to jump between dashboards, Sherlocks consolidates all telemetry logs, traces, metrics, and change events, into a single understanding of system behavior.
When something breaks, Sherlocks investigates and figures out the next course of action.
Within seconds, the platform provides:
- A real-time narrative of what occurred
- The most probable root cause
- Historical incidents with similar signatures
- Suggested next steps
- Context-aware insights tailored to the service owner
What typically takes hours of analysis becomes instant.
Companies using Sherlocks report up to a 70% reduction in MTTR, a metric that directly impacts SLAs, churn risk and customer satisfaction.
“Every minute in an outage carries a cost, financial, reputational and emotional. For B2B Saas Companies, this also means churn,” Toshniwal explains. “Sherlocks AI exists to collapse every minute between detection and resolution.”
Why Slack/Teams Matters More Than Vendors Realize
Sherlocks’ design philosophy rejects the idea of sending engineers to yet another standalone tool. Instead, it delivers all intelligence inside Slack, the place where engineering teams already coordinate, escalate, and respond.
The platform automatically joins incident channels and behaves like a highly trained SRE:
- Posting the evolving diagnosis in real time
- Surfacing logs and traces without manual querying
- Highlighting recent deployments connected to anomalies
- Identifying whether this problem has occurred before
This Slack-native approach lowers the cognitive load on teams and ensures insights are never lost in a mountain of dashboards.
The result? Engineers move from searching for information to acting on it.
The Knowledge Problem No One Has Solved,Until Now
Beyond real-time diagnosis, Sherlocks AI tackles a deeper operational weakness: knowledge loss.
Post-mortems are created, stored and then forgotten. Engineers with years of tribal wisdom leave the company. Incident history becomes scattered across documents, screenshots and half-written Slack messages.
Sherlocks solves this by functioning as institutional memory, retaining every incident, every RCA and every behavior pattern. The platform maps dependencies across services and learns from every outage, meaning it never forgets what engineers often do.
This is more than a convenience. For companies scaling engineering teams or dealing with turnover, it becomes a competitive advantage.
Flexible Deployment for Teams in Highly Regulated Environments
Security and compliance are increasingly influencing the tools companies can adopt. Sherlocks AI offers three deployment options to meet varying levels of governance:
- SaaS: Fully managed, with Sherlocks’ lightweight Watson agent deployed inside a customer’s VPC.
- Self-Hosted: The full stack runs inside the customer’s infrastructure, ideal for finance, healthcare, and enterprise-grade compliance needs.
- Hybrid(bring your own model / LLM): A blend that keeps sensitive telemetry in-house while still benefiting from Sherlocks’ cloud intelligence.
This flexibility allows Sherlocks to operate in environments where traditional monitoring vendors often struggle to gain approval.
Why AI SRE Is Becoming a Board-Level Priority
Reliability used to be the responsibility of SRE leaders alone. Not anymore.
With companies losing millions from even small outages and customer expectations approaching near-zero tolerance, boards and executive teams are now demanding:
- Faster response to incidents
- Predictable reliability metrics
- Reduction of operational burnout
- Deeper insights into systems without adding headcount
AI SRE platforms like Sherlocks AI shift reliability from reactive to proactive, enabling teams to address issues before they cascade into customer-facing failures.
“Companies have reached a breaking point,” Toshniwal says. “They can’t scale human effort to match system complexity. The only path forward is intelligent automation.”
The Future of Reliability Engineering Is Autonomous
As AI continues to transform every part of the software lifecycle, from code generation to QA to customer support, the reliability layer is emerging as one of the most impactful areas for automation.
Sherlocks AI isn’t replacing SREs, it’s amplifying them. And in most companies – the responsibility of reliability is not of SREs alone. It’s a shared responsibility of SRE and broader engineering org, including infrastructure, devops and product engineering.
Hence for teams that don’t have SRE function or shared SRE function – this means they are able to move even faster since the time of builders is now available to build.
By eliminating the manual, repetitive and high-pressure components of incident response, Sherlocks allows engineers to focus on architecture, performance and strategic improvements instead of firefighting.
In a world where even small outages can go viral, reliability is no longer a back-office function. It is a brand promise.
And platforms like Sherlocks AI are quietly becoming the backbone that keeps that promise intact.
