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AiRedHQ

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  1. Home
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  3. /AI Services

AI Services

AI that improves real work.

Custom AI assistants, workflow automation, RAG systems, LLM integrations and intelligent applications.

Start a ProjectView Capabilities
Product system

Knowledge Ops

Overview
Sources
Evaluations
Activity

Assistant workflow

Policy answer with human review

Active
01

Question

02

Retrieve

03

Generate

04

Approve

Grounded response

The parental leave policy provides 16 weeks of paid leave for eligible team members.

Policy handbookHR update
GroundingVerified
RiskLow
Human reviewRequired

Where it creates value

Practical use, not a capability checklist.

Move from an AI idea to a dependable product capability grounded in your workflow, data and users.

Knowledge assistant

Answer role-specific questions from governed company information with citations.

Document intelligence

Extract, classify and review high-volume documents with human approval.

Operational automation

Connect AI decisions to existing tools, policies and accountable workflows.

Capabilities

Built around the product.

Our work on hiARed informs how we design explainable AI workflows, human review and multi-role decision systems.

AI Assistants

Task-focused assistants designed around real roles, permissions and business context.

RAG Systems

Retrieval pipelines that give models relevant, governed and traceable context.

Workflow Automation

Human-aware automation for repetitive operational and knowledge workflows.

LLM Integration

Model capabilities embedded into products with evaluation, safeguards and observability.

Delivery path

A clear path from decision to delivery.

  1. Use case

  2. Data

  3. Prototype

  4. Evaluate

  5. Integrate

  6. Monitor

Technology

Chosen for the product.

The stack follows the experience, security, delivery and ownership needs of the product.

OpenAI
Gemini
LangChain
Node.js
PostgreSQL
Redis

Relevant industries

Applied in real product contexts.

Recruitment example

Recruitment

A practical context for ai that improves real work.

Common questions

Before we begin.

Where should an AI project begin?

With a clear workflow, user need and success condition. Model selection follows those decisions.

Can you work with private company data?

Yes. Architecture is shaped around access controls, data boundaries, retention and the sensitivity of the use case.

How do you reduce unreliable AI output?

We combine grounded context, structured outputs, evaluation, human review and monitoring based on the risk of the workflow.

Work with AiRedHQ

Let's turn the right idea into a real product.

Start a Project