AI for everything after code
Your platform for the secure, observable software lifecycle.
7C Labs brings DevSecOps, SRE and AI usage monitoring together, across your cloud platforms, with security built into every step.
- Plan to production
- One lifecycle, end to end
- Security at every stage
- Not a review at the end
- AI usage, visible
- Cost, latency, risk
- Multi-cloud
- AWS, Azure, Google Cloud
Ship confidently
Secure Delivery
A delivery pipeline where scanning, signing and approvals are part of every change, from first commit to production.
Shift-left by default
Secrets, dependency and code checks run on every commit, so problems are found while they are cheap to fix.
Policy as code
Release rules are written once and enforced in the pipeline, with visible, expiring exceptions.
Signed, traceable artifacts
Every release can be traced back to its source, its checks and the person who approved it.
- Secrets scanning
- SAST and dependency checks
- Container scanning
- Artifact signing
- Policy gates
- Secrets scan● Running
- Dependency auditQueued
- Code analysisQueued
- Container scanQueued
- Policy gateQueued
- Sign artifactQueued
Stay reliable
Resilience
Services that fail safely. Objectives, progressive rollouts and rehearsed recovery keep incidents small.
Objectives users can feel
Service-level objectives and error budgets decide when to ship and when to slow down.
Progressive rollouts
Canary releases move traffic in steps and roll back on their own when signals turn bad.
Rehearsed recovery
Runbooks, on-call workflows and failure drills are tested before an incident, not during it.
- SLOs and error budgets
- Canary and rollback
- Runbooks
- Chaos drills
- Incident timelines
Canary rollout
0% of traffic on new version
Error budget remaining (SLO 99.9%)
82% remaining
✓ Automatic rollback armed
Know your AI
AI Monitor
A short list of signals that show who uses AI, what it costs, how it performs and whether it is leaking data.
Usage by team and service
See which models are called, by whom, and how often, tied to the service that owns them.
Cost and latency together
Spend and response time sit beside your own objectives, with alerts for spikes.
Data and quality guardrails
Prompts and outputs are checked for sensitive data, and quality drift is flagged after model changes.
- Usage tracking
- Cost per request
- Latency SLOs
- Data exposure checks
- Quality drift
Requests
24,457
Spend
$81.40
Live
| Service | Model | Requests | p95 (SLO 4s) | Cost | Status |
|---|---|---|---|---|---|
| support-assistant | Model A | 12,480 | 1.8 s | $41.20 | Healthy |
| code-review-bot | Model B | 3,902 | 3.1 s | $18.75 | Degraded |
| doc-summariser | Model A | 7,115 | 1.2 s | $12.40 | Healthy |
| ops-copilot | Model C | 960 | 4.4 s | $9.05 | Breaching |
Operate efficiently
CloudOps
Landing zones, infrastructure as code, identity and cost controls applied the same way on every cloud.
Infrastructure as code
Environments are defined in code, reviewed like software, and rebuilt on demand.
Identity and network guardrails
Least-privilege access and network rules are part of the baseline, not an afterthought.
Cost and capacity visibility
Spend is broken down by team and service, with anomaly alerts before the invoice arrives.
- AWS
- Microsoft Azure
- Google Cloud
- Landing zones
- Cost anomaly alerts
- payments-apiNormal
- ml-inferenceNormal
- data-pipelineNormal
- web-frontendNormal
Illustrative sample data.
One connected platform
Every signal in one graph, so you can see the cause behind a risk or a cost.
Builds, deployments, scans, cloud spend and AI usage feed a shared view. That is what lets a recommendation point to the real change behind it.
- Code and commits
- Pipelines and releases
- Cloud resources
- AI usage
7C Labs
Signal graph
One shared view that links a change to its risk, its cost and its owner.
- Risk and cost insights
- Recommended actions
- Audit evidence
Risk and governance
AI recommends. You decide how much it can do.
Choose the level of autonomy for each workflow, and change it any time.
- Level 1
Suggest
AI summarises, correlates and recommends. People do the work.
- Level 2
Act with approval
AI prepares the change. A named person approves before it reaches production.
- Level 3
Act within guardrails
AI handles routine, low-risk actions inside limits you define, with a full audit trail.
- Humans stay accountable
- AI proposes. People approve changes to production.
- Least privilege
- Automation gets only the access it needs, for as long as it needs it.
- Evidence by default
- Every check and decision is recorded so you can show a customer or auditor.
Services
Software is one half. Our engineers are the other.
Work with us at the depth that suits you, from a first assessment to running operations together.
- 01
Assess
A review of your delivery pipeline, security posture and AI usage, with a prioritised plan.
- 02
Implement
We design and build the pipelines, infrastructure and monitoring with your team.
- 03
Operate
Ongoing support for reliability, on-call and cloud operations, as much or as little as you need.
- 04
Enable
Hands-on training so your engineers can run the platform and the practices themselves.
Get started
Tell us what you are building.
Request a demo or a first assessment. We will reply with how the platform would fit your delivery and operations.