Production engineering for AI on AWS

Move AI systems from prototype to production.

Akarui Cloud helps engineering teams move AI from prototype to production on AWS. We build agentic systems, and we take on focused serverless, modernization, migration, and FinOps work.

Discuss a project How a project runs

Small team, direct contextAWS-nativeUS and LATAM

eval-suite / regression

passdeclines an out-of-scope tool call

passanswers only from retrieved records

failcites a record the caller cannot read

Release blocked: 1 failing case.

Illustrative run written to show the method. It is not a customer benchmark.

Best fit

Built for product teams under a production mandate.

The strongest fit is an AWS-based engineering organization with a valuable AI use case, executive urgency, and a gap between a promising prototype and an operable product.

You likely need Akarui when

The prototype works, but nobody trusts its quality under real conditions.
Security, data access, latency, or model cost now block the roadmap.
Your team needs senior implementation capacity without a long staffing ramp.
Claude Code adoption is happening faster than governance and engineering standards.

What we do not sell

Open-ended strategy programs with no path to implementation.
Generic AI demos disconnected from a production owner.
Large rotating teams where context disappears between handoffs.
Model claims without evals, operating signals, or failure boundaries.

Three engagements

A bounded way to assess, build, or adopt.

These three engagements focus on AI assessment, delivery, and adoption. We also take focused AWS Production Engineering work across serverless systems, migration and modernization, and FinOps.

AI production assessment

Typical format 1–2 weeks

Turn an AI mandate or prototype into a decision-ready production plan. We examine the use case, data, evaluations (evals), security, architecture, and operating constraints before recommending what to build.

Risk map
Evaluation plan
Architecture decisions
Prioritized roadmap

Agentic system build

Typical format 4–8 weeks

Design and deliver one bounded, high-value workflow with production controls from the start. The engagement ends with running software, operational documentation, and an explicit ownership transfer.

Working workflow
Evals
AWS infrastructure
Runbooks and handoff

Claude Code adoption

Typical format 3–4 weeks

Create a safe operating model for Claude Code across an engineering team: repository context, permissions, workflow design, review standards, and adoption measures tied to quality—not prompt volume.

Adoption baseline
CLAUDE.md patterns
Permission model
Team sessions

Explore scope and fit

Delivery model

Evidence is part of the build.

We do not use invented customer metrics or decorative demos as proof. Delivery produces concrete artifacts your team can inspect, operate, and extend.

Frame the production question

Define the user outcome, unacceptable failures, data boundaries, and how success will be measured.

Build the evaluation system

Turn representative cases into repeatable checks before optimizing prompts, models, or orchestration.

Integrate and harden

Connect real systems, implement identity boundaries, expose operating signals, and test failure paths.

Transfer ownership

Document decisions, run incidents, and work with the client team until they can operate the system confidently.

Accountable from scope to handoff

The engineers who scope the work stay accountable through architecture, implementation, and handoff.

Evidence before scale

We define evals, failure boundaries, and operating signals before increasing model autonomy or traffic.

Your team keeps the system

Architecture decisions, runbooks, and working sessions are part of delivery—not an optional final phase.

The handoff is a document your team can argue with.

Every engagement ends with decisions written down: what was chosen, what was rejected, and what fails first. An architecture decision record (ADR) is one page of that.

In plain terms, this one says the AI can only see what the person asking could already see. We do not publish client work or invented metrics, so this is a sample written to show the structure.

architecture/adr-007.md · sample

ADR-007 · Data access boundary for the retrieval agent

Status: Accepted
Context: The agent drafts replies from internal records. Direct model access to the data store would widen the blast radius of any bad output.
Decision: Retrieval runs under the caller’s IAM identity. The model receives results, never credentials.
Failure boundary: A regression in the eval suite blocks release. Unknown tool calls are refused and logged.
Consequence: Slower first query. Every access is traceable to a person and a trace ID.

Sample excerpt. It is not a client record.

Bring the production constraint, not a polished brief.

Send the constraint you are working against. We reply within one business day and, if it fits, schedule a 30-minute technical scoping call to decide what should happen next.

Discuss a project