AWS Infrastructure AI Assistant
A read-only AI assistant turns complex AWS infrastructure data into fast, evidence-backed answers in plain English.

Infrastructure questions are answered in under 10 seconds instead of 20-40 minutes of manual console work.

TechnologiesAWSAmplifyServerlessRAGBedrock

ServicesCustom Software Web MVP SaaS DevOps

Context

As AWS environments grow, infrastructure knowledge becomes fragmented across services, configurations, and individual team members. Answering even a straightforward question can require engineers to move between AWS Config, Security Hub, Cost Explorer, CloudWatch, and other tools before manually connecting the findings.

We built an AI infrastructure assistant for DevOps and security teams managing complex AWS environments. Instead of navigating multiple consoles or writing specialized queries, engineers can ask infrastructure questions in plain English and receive evidence-backed answers based on actual AWS Config data without giving the assistant permission to modify production resources.

Results Based on internal use and testing across real AWS environments.	Before	After
Infrastructure investigation time	20-40 minutes of manual AWS Console investigation	Under 10 seconds for infrastructure answers
Security audit preparation	Findings manually collected and summarized across infrastructure tools	~60% less preparation time
Infrastructure discovery	Engineers navigated multiple AWS services and query interfaces	Natural-language questions return consolidated, evidence-backed answers
New engineer onboarding	Infrastructure understanding depended on tribal knowledge and manual console walkthroughs	Engineers can query existing infrastructure context directly
Incident triage	Relevant resource state discovered manually during investigation	Relevant infrastructure state surfaced immediately from available snapshots

Challenges

Solutions

Infrastructure questions required manual investigation across AWS tools
Understanding resource state, security exposure, configuration changes, or unused infrastructure could require engineers to navigate several AWS services and manually combine their findings. A single investigation could take 20-40 minutes.
Natural-Language Infrastructure Queries
We built a conversational layer over AWS infrastructure data. Engineers can ask questions in plain English, while the assistant retrieves relevant configuration context, reasons over it, and streams an actionable answer. Internal testing reduced common infrastructure investigations from 20-40 minutes to under 10 seconds.
AI answers about infrastructure needed to be verifiable
A plausible but incorrect answer about resource state can create security and operational risk. The system therefore could not rely on unsupported LLM reasoning.
RAG Grounded in AWS Config Snapshots
The assistant retrieves information from normalized and indexed AWS Config exports. Responses include traceable source evidence and confidence cues, while guardrails prevent the model from claiming resource states it cannot verify.
Findings needed to lead to action, not another investigation
Identifying a potentially exposed resource or unnecessary cost is only the first step. Engineers still need to understand why the finding matters and what should happen next.
Contextual Remediation Guidance
When the assistant identifies an issue, it explains the finding, its relevance, and a suggested remediation, reducing the gap between infrastructure discovery and the next engineering decision.
Infrastructure analysis could not introduce production risk
An AI assistant connected to cloud infrastructure creates an additional concern if it has the ability to modify resources, particularly during security investigations or incidents.
Read-Only, Snapshot-Based Architecture
The assistant operates on time-bounded AWS Config snapshots rather than using write permissions or live production API calls. It can analyze infrastructure state and recommend actions, but it cannot change the environment itself.

What We Learned Building This

Infrastructure AI needs evidence more than confidence
For DevOps and security workflows, every answer should be traceable to actual resource data rather than relying on a model to produce a plausible explanation.
Natural language can remove the expertise bottleneck around infrastructure discovery
Engineers still need AWS knowledge to make decisions, but they should not need to remember which console and query model contains every piece of information.
Read-only AI can deliver significant value without being allowed to take action
Separating analysis from execution makes the assistant useful during audits and incidents without introducing another system capable of changing production infrastructure.
Infrastructure relationships matter as much as individual resources
Understanding dependencies between resources gives the assistant more useful context than searching isolated configuration records.
Freshness should match the operational use case
Snapshot-based analysis creates a clear boundary around what the assistant knows, while configurable ingestion frequency allows teams to balance freshness with their actual infrastructure requirements.

Get Answers From Your AWS Infrastructure in Seconds

Managing an AWS environment that has become too complex to investigate manually? We can help turn your infrastructure data into a secure, evidence-backed AI assistant for DevOps and security teams.

Our Cases

Innovation isn’t just about ideas - it’s about execution, turning vision into reality, and creating solutions that truly make an impact. See what we’ve built and how it works: