NeedToKnow Tech — AI Workflows, Agents & Private AI

Private AI

Run Models Locally

Own your data. Keep prompts and outputs inside your network.

Workflows

Chain Tools into Outcomes

Orchestrate multi step tasks across your stack with guardrails.

Agents

Goal Driven Automation

Agents plan, call APIs & adapt with feedback to hit objectives.

Security

Privacy by Design

Minimise exposure; align with data residency & compliance.

Identity

IAM Aware Automation

Joiner/Mover/Leaver flows with approvals & least privilege.

Ops

Observability Built in

Trace every step with metrics, logs and human in the loop.

Flexibility

Right Model for the Job

Swap models per task; mix proprietary & open source locally.

Latency

Fast, Predictable Response

Local inference reduces hops and removes token surprises.

Top AI Workflow & Agent Use Cases

Common, high impact patterns we implement first.

1) Ticket triage & routing

Turn emails/Slack into tickets, auto classify, reply, and route with context.

2) Knowledge base Q&A

Ask questions over docs, wikis & tickets with citations and access controls.

3) Document intake

OCR → classify → extract fields → push to CRM/ERP with validation.

4) Meeting follow ups

Summaries, decisions & action items with auto tasks and nudges.

5) Sales assist

Draft proposals, update CRM, suggest next best actions from activity.

6) Marketing ops

Generate/review content, A/B variants, schedule with approvals.

7) DevOps copilot

Summarise logs, create issues, roll back or runbooks with guardrails.

8) RPA hand offs

Agents drive browsers, fill forms, reconcile systems when APIs fall short.

9) SecOps enrichment

Triage alerts, enrich IOCs, propose responses; keep humans in control.

10) IAM workflows

Joiner/Mover/Leaver automation with approvals and least privilege.

What & Why: AI Workflows, Agents & Local Hosting

Short, factual points rotate below. These also work well as social proof banners.

AI workflows orchestrate multiple steps (ingest → reason → act) reliably across tools and data sources.

An agent is a system that plans, calls tools/APIs, observes results, and iterates until the goal is reached.

Local hosting keeps prompts, context and outputs on your infrastructure, reducing third party data exposure.

Local models provide predictable cost (hardware bounded) instead of variable per token fees.

Running inference close to data lowers latency and avoids external rate limits or outages.

Open source models can be customised (fine tuned, adapters) to your domain and compliance needs.

Workflows add guardrails , approvals, policy checks, and audit logs around every automated action.

Agents integrate with existing systems (IDAM, ticketing, CRM, RPA) to reduce swivel chair work.

Private AI improves compliance with data residency (e.g., AU/EU), retention and classification policies.

Observability, capture traces & metrics so humans can review, override, and continuously improve.