The AI SRE that runs your L1 and L2. So your engineers can run your business.
Sherlocks deflects 70% of L1 tickets, cuts MTTR by 60%, and frees up 5–6 engineers per 20 customers. Same revenue. Higher margin. No new hires.
Your business runs on engineering leverage. Everything else is friction.
Every MSP runs into the same wall. The book grows linearly. The headcount grows linearly with it. Margins compress because the only way to absorb more alerts is to staff for them. The engineers you wanted to redeploy into solution selling or new logos are stuck in ticket queues at 2am.
Alert volume scales with the book
Every new customer adds tickets. Most of them are L1 noise. Your senior engineers spend their week triaging the same five patterns.
MTTR is a margin tax
Slow resolution means SLA penalties, churn risk, and a CSAT line your CFO doesn't want to see.
You can't hire your way out
The L1 / L2 talent market is finite, expensive, and burns out fast. Headcount-led growth has a ceiling.
Sherlocks is the operations layer that scales when your customers don't sleep.
Sherlocks is an AI SRE. It connects to your customer's observability and cloud stack, builds a live knowledge graph of their environment, and runs autonomous investigation on every alert. It deflects the noise, drafts the RCA on real incidents, and acts on the runbooks you've approved. Your team gets out of the queue and back into the work that compounds.
Investigate everything
Sherlocks reads metrics, logs, traces, deploys, and code on every alert. Same investigation your best SRE would run, executed in seconds.
Deflect the noise
70% of L1 tickets never reach a human. The remaining 30% reach your engineer with the RCA already drafted.
Act on what you approve
Bounded autonomy. The agent acts only inside the runbooks you've signed off on. Every action is logged, reversible, and auditable.
Reduce MTTR by 60%
Median across Sherlocks customers. The lift on SLA credits avoided is usually visible inside the first quarter.
What Sherlocks does to an MSP P&L.
Model based on a representative book of 20 customers at $5,000 MRR (=$100K monthly revenue). Flex assumptions below to see the impact on your specific book.
Flex Assumptions
Side-by-Side P&L Comparison
| P&L Metric | Baseline | With Sherlocks |
|---|---|---|
| Customers Under Management | 20 | 20 |
| Monthly Book Revenue | $100,000 | $100,000 |
| Engineering Headcount | 13 FTE | 7 FTE |
| Engineering Headcount Cost / mo | $37,000 | $25,000 |
| SLA Penalty Cost / mo | $2,000 | $500 |
| Estimated Sherlocks Platform Fee / mo * | — | $6,600 |
| Total COGS / mo | $52,000 | $45,100 |
| Gross Profit / mo | $48,000 | $54,900 |
| Gross Margin | 48.0% | 54.9% |
Live in your customer environments in 90 days.
Integrate and graph
Watson deploys into your customer's VPC. Read-only access to observability + cloud. The knowledge graph builds itself in days, not months.
Deflect and resolve
Top 20 alert classes profiled. First auto-RCA runs. First deflection runbooks ship behind your approval gate.
Measure and scale
Public deflection rate, MTTR per service, and tickets-per-engineer dashboard. Measurable margin lift in the quarterly review.
What you'll show your CIO at the 90-day review.
Tickets-per-engineer, MTTR by service, deflection rate by alert class. The same dashboard, every quarter, going in the right direction.
Not a chatbot. Not an LLM wrapper. A real SRE.
Generic AI ops chatbot
Summarizes the ticket. Hallucinates services. Asks the same five questions every incident.
LLM-on-logs tool
Re-ingests everything every time. Loses context across incidents. Can't reason about service-to-service blast radius.
Sherlocks knowledge graph
Services, deps, deploys, owners, recent changes — kept current automatically. Investigation has memory.
Sherlocks agent
Walks the graph, picks the right tools, runs the right queries. Same playbook your best SRE would run, every time.
Best-fit MSPs.
Sherlocks gives the most leverage to MSPs whose business model is built on uptime and gross margin, not billable hours.
You're a fit if you:
- Run a managed services or managed cloud business with 20+ customers
- Have a named NOC or SOC with 10+ engineers
- Carry SLAs with credits for breaches
- Sell on AWS, Azure, GCP, or hybrid cloud
- Already use Datadog, New Relic, Dynatrace, Splunk, Grafana, or ServiceNow
- Want to scale customers without scaling headcount proportionally
× Sherlocks won't move the needle if you:
- Run a body-shop / staff-augmentation model (the wedge is leverage, not bodies)
- Have fewer than 20 customers under management today
- Have no observability stack in place
- Sell hours, not outcomes
We work with MSPs three ways.
Resell
Add Sherlocks to your services catalog. Standard MSP partner margins apply.
Bundle
Roll Sherlocks into your managed-services offering. Pricing is invisible to your customer; you keep the margin uplift.
Co-deliver
Sherlocks runs the AI SRE layer; your team runs the customer relationship. Joint-managed engagements for your largest customers.
Built for the security teams your customers answer to.
SOC 2 Type 2
Independently attested. Latest report (Jun-Oct 2025) available on request.
Deploy in customer VPCs
Watson sits inside your customer's cluster. Raw telemetry never leaves. The Platform sees only redacted metadata.
Audit-ready
Every agent action is logged. Pre-answered vendor assessment Q&A for IPO-bound and regulated customers (BFSI, insurtech, fintech, healthcare).
Stop paying L1 engineers to ack and re-route. Pay them to think.
30-minute walkthrough. We'll show the agent investigating a real incident on a sample environment, then talk about what your queue looks like.