AI Services

One partner. AI across your stack.

Triangu is an AWS Advanced Tier Services Partner and is Certified in the Claude Partner Network. We implement generative AI on Amazon Bedrock, from a fixed-scope proof of concept to production, and bring AI into the tools your teams already use: Claude, Atlassian Rovo, StackBooster and GitLab.

Amazon Bedrock Amazon Bedrock
claude Certified
rovo Atlassian AI
GitLab Duo GitLab Duo
Services

What we do in AI

Eight generative AI services on Amazon Bedrock, from a first proof of concept to production.

Generative AI Proof of Concept

Pick one use case and test it before you commit budget. We offer five ready proof of concept tracks: intelligent document processing, an enterprise RAG knowledge assistant, AI test case generation from Confluence, an AI agent on Amazon Bedrock AgentCore and AI-assisted FinOps. Fixed scope, weeks not months, with a costed path to production.

  • Amazon Bedrock
  • Proof of Concept
  • Guardrails
  • Terraform
Generative AI Proof of Concept

A proof of concept gives you proof on your own data before you commit budget, and the prototype is not thrown away.

Best for: teams with a specific AI use case in mind that need proof on their own data before they commit budget.

  • • Scope and data: kickoff workshop, one use case, success metrics and 20–50 reference items from your own data
  • • Prototype on Amazon Bedrock with Terraform, Amazon Bedrock Guardrails and Amazon CloudWatch observability
  • • Foundation models compared on quality, latency and cost through the Amazon Bedrock Converse API
  • • Production architecture and an AWS Pricing Calculator estimate
  • • Path-to-production plan, source code handover and a readout for your stakeholders
  • Amazon Bedrock
  • Proof of Concept
  • Guardrails
  • Terraform

Claude on Amazon Bedrock Readiness Assessment

Before you open Claude to your teams, we assess where it fits and what it needs: the use cases worth starting with, how your data is classified, your security requirements, and whether your AWS accounts can use Claude models. You get a target architecture and a cost estimate, so the cost is known before you commit.

  • Claude
  • Amazon Bedrock
  • Readiness Assessment
  • Architecture
Claude on Amazon Bedrock Readiness Assessment

Start with an assessment, then a proof of concept, then production. Or go straight to production if the use case is already clear.

Best for: teams that have chosen Claude but cannot enable it yet, and IT, security and legal teams that need answers on data residency and cost.

  • • Use cases and data classification
  • • Security requirements and a check that your AWS accounts can use Claude models
  • • Target architecture
  • • Cost estimate
  • • A plan for the first one or two use cases to prove in your AWS account
  • Claude
  • Amazon Bedrock
  • Readiness Assessment
  • Architecture

Amazon Bedrock Landing Zone

Teams want Amazon Bedrock, security wants private connectivity and audit logs, finance wants a cap on spend. We deliver all three as one Amazon Bedrock landing zone, built as Terraform and rolled out to every account in your AWS Organization.

  • Amazon Bedrock
  • PrivateLink
  • Governance
  • Cost Control
Amazon Bedrock Landing Zone

Everything is native AWS, delivered as code, with no third-party gateway in the request path.

Best for: organizations rolling Amazon Bedrock out to several teams, where security needs private connectivity and audit logs, and finance needs a cap on spend.

  • • Private connectivity: AWS PrivateLink interface endpoints for the Amazon Bedrock runtime, agent and knowledge base APIs
  • • Model access governance with IAM and service control policies, plus per-team roles
  • • Per-team cost attribution with application inference profiles, AWS Budgets and Cost Anomaly Detection
  • • Model invocation logging and a baseline of Amazon Bedrock Guardrails
  • • Terraform modules, a permission matrix, a cost dashboard and an onboarding guide
  • Amazon Bedrock
  • PrivateLink
  • Governance
  • Cost Control

AI-Assisted FinOps on Amazon Bedrock

The agent reads your AWS Cost and Usage Report, Cost Explorer, Compute Optimizer and Trusted Advisor data. Triangu FinOps engineers review every finding before you get a prioritized top-10 savings plan. Start with a fixed-scope proof of concept, or go straight to production if the use case is already validated.

  • FinOps
  • Amazon Bedrock
  • Cost Optimization
  • Read-Only
AI-Assisted FinOps on Amazon Bedrock

Read-only by design: a scoped IAM role and no write actions in your account during the proof of concept.

Best for: finance and cloud teams that want a reviewed list of AWS savings without giving anyone write access to their account.

  • • Baseline spend report and tagging assessment
  • • Anomaly and recommendation register with owner, expected saving, effort and risk
  • • Prioritized top-10 savings plan with a 30-60-90-day sequence
  • • Agent deployed read-only with Terraform, with Amazon Bedrock Guardrails
  • • In production: weekly anomaly digests and Jira tickets with owners
  • FinOps
  • Amazon Bedrock
  • Cost Optimization
  • Read-Only

AI Agent Development on Amazon Bedrock AgentCore

We build and deploy an AI agent on Amazon Bedrock AgentCore for one defined workflow, with your APIs connected as tools, and measure it on completion rate and cost per task.

  • AgentCore
  • AI Agents
  • Amazon Bedrock
  • Automation
AI Agent Development on Amazon Bedrock AgentCore

Start with one workflow and measure it before you scale.

Best for: teams with one repetitive, well-defined workflow that runs through their own systems and APIs.

  • • One workflow automated by an agent on Amazon Bedrock AgentCore
  • • Your APIs connected as tools for the agent
  • • Measured on completion rate and cost per task
  • • Amazon Bedrock Guardrails and observability from the start
  • • Production architecture and a path-to-production plan
  • AgentCore
  • AI Agents
  • Amazon Bedrock
  • Automation

Enterprise RAG Knowledge Assistant

We build an enterprise RAG (Retrieval-Augmented Generation) knowledge assistant on Amazon Bedrock over your own documentation for a pilot group, and measure it on search time per task before and after.

  • RAG
  • Amazon Bedrock
  • Knowledge Assistant
  • Confluence
Enterprise RAG Knowledge Assistant

A knowledge assistant is only useful when it answers from your own documents and saves your team real time.

Best for: teams that lose time searching Confluence, SharePoint or shared drives for answers.

  • • One assistant over Confluence Cloud, SharePoint Online or Amazon S3
  • • Answers based on your own documents, built on Amazon Bedrock
  • • Rolled out to a pilot group first
  • • Measured on search time per task before and after
  • • Production architecture and a path-to-production plan
  • RAG
  • Amazon Bedrock
  • Knowledge Assistant
  • Confluence

Intelligent Document Processing

We process your documents with Amazon Bedrock Knowledge Bases, delivering cited answers and extraction over contracts, invoices, claims or manuals, and measure the result on answer quality and cost per query.

  • Document Processing
  • Amazon Bedrock
  • Knowledge Bases
  • Extraction
Intelligent Document Processing

Document-heavy processes are a good first use case: the inputs are clear and the results are easy to measure.

Best for: operations, legal and finance teams that handle large volumes of contracts, invoices, claims or manuals.

  • • Cited answers over your own documents
  • • Extraction from contracts, invoices, claims or manuals
  • • Built on Amazon Bedrock Knowledge Bases
  • • Measured on answer quality and cost per query
  • • Production architecture and a path-to-production plan
  • Document Processing
  • Amazon Bedrock
  • Knowledge Bases
  • Extraction

AI Test Case Generation from Confluence

We generate test cases from your Confluence requirements into Jira for two or three real epics with Amazon Bedrock, and measure the result on QA hours saved and the acceptance rate of the generated cases. Every generated case is reviewed by a person before it is used.

  • AI QA
  • Confluence
  • Jira
  • Amazon Bedrock
AI Test Case Generation from Confluence

Requirements you already keep in Confluence become test cases your QA team can review and accept in Jira.

Best for: QA and engineering teams that keep requirements in Confluence and track testing in Jira.

  • • Test cases generated from Confluence requirements
  • • Delivered into Jira for 2–3 real epics
  • • Built on Amazon Bedrock
  • • Always reviewed by a person: your QA team accepts or rejects each case
  • • Measured on QA hours and acceptance rate
  • • Production architecture and a path-to-production plan
  • AI QA
  • Confluence
  • Jira
  • Amazon Bedrock
AI across your stack

AI inside the tools you already use

Claude on Amazon Bedrock, Atlassian Rovo, StackBooster and GitLab, implemented by one team.

Claude_Code_loGO

Claude on Amazon Bedrock

We set up Claude on Amazon Bedrock so teams get the productivity of modern AI models inside the controls your security team already trusts: IAM, private connectivity, guardrails, audit trails and cost control per application. Triangu is Certified in the Claude Partner Network.

  • Claude
  • Amazon Bedrock
  • Security
  • Developer Productivity
Claude on Amazon Bedrock

Claude runs inside Amazon Bedrock. Per the Amazon Bedrock documentation, Anthropic has no access to your prompts or responses.

Best for: companies that have chosen Claude, and engineering teams whose policy does not allow third-party AI endpoints.

  • • Access through IAM, private connectivity with AWS PrivateLink, and audit trails in your own account
  • • Amazon Bedrock Guardrails for personal data and topic filters, defined as code
  • • Governance with service control policies, encryption with your own AWS KMS keys
  • • Developer productivity: Claude Code through Amazon Bedrock in your AWS account, and automated code review of every merge request in your CI pipeline
  • • Production controls: evaluations on a reference set of real questions, prompt management, observability and model routing
  • • Cost control from day one: per-application cost tracking, AWS Budgets and Cost Anomaly Detection
  • Claude
  • Amazon Bedrock
  • Security
  • Developer Productivity
rovo

Atlassian Rovo Implementation

Rovo is Atlassian's AI teammate: search across your tools, chat in natural language and agents that act on tickets. We map the use cases, check permissions and data, configure the agents, train your team and roll out in steps. As an Atlassian Platinum Solution Partner, we build Rovo on top of a well-run Jira Service Management and Confluence.

  • Atlassian Rovo
  • Jira Service Management
  • Confluence
  • AI Agents
Atlassian Rovo Implementation

Rovo respects your Atlassian permissions. The quality of an agent depends on the quality of your Confluence, so content review is part of every project.

Best for: IT service desks and support teams on Jira Service Management and Confluence with a high volume of tickets.

  • • Smart Ticket Triage: the agent classifies, prioritizes, fills fields and routes new tickets, and leaves a comment explaining its decision
  • • Incident Response Assistant: similar past incidents, runbooks and JSM Playbooks surface in the ticket, and a draft incident report is ready for review
  • • Knowledge Base Assistant: answers repeat questions from Confluence, and escalates to your team below a confidence threshold
  • • SLA Risk Monitor: Jira Automation finds tickets at risk, Rovo explains why they are stuck and recommends the next step
  • • Change and Release Assistant: change quality and risk flags, similar past changes and a brief for your change board
  • Atlassian Rovo
  • Jira Service Management
  • Confluence
  • AI Agents
stackbooster

StackBooster

StackBooster is an AI-driven automation platform for Kubernetes on AWS. It collects live metrics from your EKS nodes, pods and AWS services, predicts demand and spot interruptions, and adjusts the cluster without manual tuning. Triangu helps you deploy, operate and scale it.

  • StackBooster
  • Kubernetes
  • Amazon EKS
  • FinOps
StackBooster

Several autonomous components work together, so cluster cost and capacity follow real workload demand.

Best for: teams running Kubernetes on Amazon EKS that want cluster cost and capacity to follow demand without manual tuning.

  • • ClusterPilot: scales AWS workloads from live application behavior and selects instance types during scale-up
  • • SpotPilot: predicts spot instance terminations and moves workloads before an interruption
  • • ClusterDefragmentation: consolidates pods onto fewer, better-utilized nodes
  • • StackSizing: adjusts pod CPU and memory requests to observed usage
  • • TopologyAwareScheduler: places communicating pods closer together to cut latency and cross-zone traffic cost
  • • ZeroKube: a production-ready Kubernetes cluster in minutes
  • StackBooster
  • Kubernetes
  • Amazon EKS
  • FinOps
GitLab

GitLab DevSecOps with AI

We deploy GitLab (SaaS or self-managed), build CI/CD pipelines with SAST and DAST security checks and quality gates, and bring AI-assisted workflows into your DevSecOps process. Triangu is a GitLab Select Partner.

  • GitLab
  • GitLab Duo
  • DevSecOps
  • CI/CD
GitLab DevSecOps with AI

AI helps most where the pipeline is already solid, so we start with the delivery foundation.

Best for: engineering teams where merge requests wait for reviewers or security checks happen late in the pipeline.

  • • CI/CD acceleration and pipeline automation
  • • SAST and DAST security checks and automated quality gates in the pipeline
  • • GitLab deployment, configuration and governance, as SaaS or self-managed
  • • Integrations with Jira, TestRail and other DevOps tools
  • • AI-assisted workflows with GitLab Duo, where they fit your process
  • GitLab
  • GitLab Duo
  • DevSecOps
  • CI/CD
Business benefits

What you gain with Triangu in AI

Proof on your own data, controls built in and one partner across the stack.

Proof before budget.

Measured on your data

Every proof of concept is measured on your own data, with a costed path to production.

  • 2-3 weeks
  • Fixed fee
  • Runbook included
Control built in.

Governed by design

Private connectivity, model access rules, Guardrails and cost controls, delivered as code.

  • 2-3 weeks
  • Fixed fee
  • Runbook included
One partner.

AI across your stack

Amazon Bedrock, Claude, Rovo and StackBooster from one team.

  • 2-3 weeks
  • Fixed fee
  • Runbook included

AI projects we delivered

Enterprise IT

Intelligent IT Support Automation for a 5,000-User Enterprise

rovo
Atlassian

A leading international IT services company required a fundamental upgrade to its internal IT support system. The existing setup relied on a large, manually intensive dispatcher team to handle classification, routing, and data validation for a massive user base. The client required a next-generation solution to minimize human intervention.

Triangu implemented an intelligent system on Jira Service Management and Rovo AI. Rovo agents classify every ticket, validate fields automatically, enrich asset data and route each request to the right team, automating the lifecycle end to end.

More than 90% of tickets are now classified automatically, with fully validated data and under 10% manual touch, saving 40+ hours of manual triage a week.

Explore Solution
AI Automation Jira Service Management Rovo AI
Automated Ticket Classification
>90%
Supported Users
5,000+
Data Validation Integrity
100%
Commercial Banking

Amazon Bedrock Proofs of Concept

aws
Amazon Bedrock

A commercial bank asked Triangu to prove generative AI on real banking work with Amazon Bedrock. EU data residency and GDPR were design requirements from the start, so each proof of concept had to fit the bank's compliance constraints as well as prove its use case.

Triangu delivered two Amazon Bedrock proofs of concept. The first covers transaction classification. The second is a compliance assistant built to answer questions from the bank's regulatory documents, so people can work from the source text instead of searching for it manually.

Our proof-of-concept approach agrees one use case and its success criteria upfront, builds the prototype on Amazon Bedrock with Guardrails and observability, and evaluates the results before any production decision. Both proofs of concept were built in line with the bank's data residency and GDPR requirements.

Explore Solution
Amazon Bedrock Banking Compliance GDPR
Amazon Bedrock Proofs of Concept
2
Design Requirements
EU + GDPR

 

How we work

From proof of concept to production

  1. Scope

    One use case, success metrics and 20–50 reference items from your own data.

  2. Build

    Prototype on Amazon Bedrock in your AWS account, deployed with Terraform.

  3. Evaluate

    Results per foundation model on quality, latency and cost, plus a production plan.

  4. Operate

    Production implementation and managed operations by our AWS Cloud Operations practice.

Validated by AWS, built with Claude

Triangu is an AWS Advanced Tier Services Partner holding the AWS Cloud Operations Services Competency and the AWS Networking Consulting Competency, and is Certified in the Claude Partner Network.

We are also an Atlassian Platinum Solution Partner and a GitLab Select Partner, so AI lands inside the tools your teams already use.

Claude Partner
AWS Advanced Tier Services Partner
Atlassian Platinum Solution Partner
Track record

AI by the numbers

Ready proof of concept tracks, a fixed scope and a team that runs what it builds.

5

Ready proof of
concept tracks

Weeks

Not months to a working prototype

50+

Skilled engineers

350+

Projects delivered across
all practices

Partner Certifications
FAQ

Frequently asked questions

What teams usually ask before starting with AI.

Where does our data go?

Everything is built in your own AWS account, and your data stays in your AWS environment, in the Regions you choose. Prototypes run with Amazon Bedrock Guardrails and observability in Amazon CloudWatch, and cost analysis runs read-only: a scoped IAM role and no write actions in your account.

Which foundation models can we use?

All inference goes through the Amazon Bedrock Converse API, so the foundation model is a configuration setting. We compare two or more models, such as Amazon Nova, Anthropic Claude, Meta Llama and Mistral, on quality, latency and cost.

How long does a proof of concept take and what do we get?

Weeks, not months, with a fixed scope. You get a working prototype in your AWS account, an evaluation report, a production architecture with an AWS Pricing Calculator estimate, a path-to-production plan and a readout for your stakeholders.

What happens after the proof of concept?

The prototype is built on production-grade services, so it is not thrown away. The production implementation of each track is a fixed-scope offer, followed by managed operations from our AWS Cloud Operations practice if you want them.

Can we buy Triangu's AI services through AWS Marketplace?

Yes. Our generative AI services are listed in AWS Marketplace as professional services sold by Triangu. You request a private offer, we hold a scoping call and issue a fixed price for the fixed scope, billed through AWS Marketplace on your AWS account.

Do you also bring AI into Jira, Confluence and GitLab?

Yes. Besides Amazon Bedrock and Claude, we implement Atlassian Rovo agents inside Jira Service Management and Confluence, StackBooster for Kubernetes automation on AWS, and AI-assisted workflows in GitLab, so AI fits the infrastructure, workflows and pipelines you already run.

Which AI use case should you test first?

Tell us what you want to automate or understand better, and what data you have. We reply within one business day.

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Prefer to talk? Call us at 818.251.0998.