Kernel Behind the Future

C-accelerated. Science-ready. SEO-dominant. Launch your advantage in milliseconds.

Skip to main content

Technical Details

We trace real execution paths, identify risk in hot paths, and deliver tangible output—not slideware.

Talk to an EngineerRead FAQ

What we actually do inside your systems

Map real execution paths end to end

We trace real traffic through your systems to understand actual behavior.

Identify risk in hot paths

We look for patterns that cause recurring issues and deployment friction.

Propose minimal, high-leverage changes

We focus on the smallest set of changes that unlock stability and speed.

Deliver tangible output

The result is not slideware—it's implemented changes in your codebase.

AWS services in scope

We focus on real-world AWS serverless and AI workloads

Primary Focus
Primary

AWS Lambda

Serverless compute for your backend functions

Primary

Amazon Bedrock

AI/ML integration and inference

Supporting Services

Amazon RDS / Aurora

Managed relational databases

Amazon Kinesis

Real-time data streaming

Amazon EC2

Message queuing service

Any AWS usage from resources created or adjusted during the engagement is billed directly to your AWS account under standard pricing.

How we select systems for the engagement

We target systems where focused changes unlock outsized improvements

High user impact

Recurring incidents or noisy alerts that affect real users and business outcomes.

Long lead time for changes

Tangled flows and cross-team dependencies that slow down deployments.

Hard-to-reason-about AI calls

AI integrations that are difficult to monitor or recover from when they behave unexpectedly.

Typical use cases

Use Case

Stabilizing a Lambda-heavy backend before a major release

A team has a core backend built on AWS Lambda and API Gateway that has accumulated years of hotfixes and special cases. Deployments require freeze periods, and on-call engineers avoid touching certain handlers. We trace the hot paths, remove unnecessary branching, isolate risky steps, and leave the service in a state where new features can be shipped without fear.

Use Case

Making a Bedrock-powered feature operationally safe

A product team added an AI assistant using Amazon Bedrock. It works most of the time, but failures are hard to detect, and error handling is inconsistent. We make the integration observable, clarify error paths, and ensure that AI behaviour can be reasoned about, tested, and rolled back when needed.

Use Case

Untangling a queue-driven internal workflow

An internal workflow is triggered by events, pushes messages through EC2 and Kinesis, and updates multiple databases. Failures are intermittent and hard to reproduce. We map the full pipeline, reduce hidden cross-dependencies, clarify which service owns which step, and improve how failures and retries are handled.

Summary: what you get and how it's proven

ItemDescriptionProof of Completion
Code ChangesRefactors, guards, clearer event models, safer retries and timeouts.Merged or merge-ready PRs with tests, comments, and change notes.
Infrastructure UpdatesIaC for queues, topics, state machines, IAM scopes, concurrency, and dead-letter policies.Reviewed plans and applied changes in your pipelines and accounts.
Operations NotesFailure modes, SLO signals, alert actions, and roll-back steps.Short docs stored with the code and walked through in the hand-over session.

Ready to learn more?

Explore how we work with your team, see answers to common questions, or get in touch to discuss your specific systems.

How We WorkView FAQContact SupportAWS Marketplace
Target Insight Function Logo

Target Insight Function – AWS Lambda & Bedrock Production Consulting

EngagementFAQSupportContact

© 2026 Work Target Insight Function. All rights reserved.