# Technology and software companies

> In technology and software companies, AI usually proves itself first in support, proposals and documentation. Corrplus builds ticket triage workflows, RAG assistants that answer from your technical docs, and LLM integrations for product teams at IT service providers, software houses and cybersecurity firms, with data flows designed for client contracts and Türkiye's data protection law, KVKK.

URL: https://corrplus.net/industries/technology-software/
Updated: 2026-10-22
Corrplus Team

## Challenges

- Tickets arrive by email, portal and WhatsApp, and it takes hours to reach the right engineer.
- Technical knowledge lives in a few senior heads and scattered Confluence and SharePoint pages.
- Preparing licence, hardware and service proposals eats a week of the sales team's time.
- Clients ask about AI; the team tried adding an LLM to the product but has no security or cost controls in place.
- Client confidentiality clauses, GDPR and KVKK slow down every AI experiment that touches customer data.

## Where it helps

- **Ticket triage**: A workflow that tags incoming tickets by topic, priority and contract, assigns them to the right team and drafts a first reply.
- **Technical documentation assistant**: A RAG assistant over install guides, runbooks and past resolutions that cites its sources.
- **Proposal and RFP drafts**: A helper that reads the client's requirements and builds a first draft from your product list, scope templates and past proposals.
- **LLM features in your product**: Summaries, search or an assistant inside your SaaS using OpenAI, Anthropic Claude or Azure OpenAI, with usage and cost tracking.
- **Internal operations agent**: An AI agent that tracks licence renewals, certificate expiry and contract dates and alerts the owner in time.

## Clients in this sector

- ITSTACK Bilgi Sistemleri
- Era Defence
- UnifyTech NL
- UnifyTech TR
- Reditim
- MBR Teknoloji
- Big4TR
- FormData
- InfinitumIT
- Profectsoft
- Mikro Entegre
- Logpusher

## Where does AI start in a technology company?

Technology companies explain AI to their clients but often use it last in-house. The support queue, proposal writing and scattered technical knowledge are the three bottlenecks we see most. They share a pattern: the input is text, the rules are partly known, and mistakes are costly.

We usually suggest ticket triage as the first step. Tickets already sit in one system, you can measure whether they reach the right team, and an engineer can approve each suggestion. Once that workflow runs, moving on to a documentation assistant built on the same data is much easier.

The measures are clear too: time for a ticket to reach the right person, how often tickets are reassigned and how often an engineer corrects the suggestion. We set these up from day one, so you judge the workflow on your own data rather than on impressions.

## Which approach fits which need?

| Need | Approach | Typical tools |
|---|---|---|
| Route tickets to the right person | Classification and routing workflow | n8n, OpenAI or Claude, Jira or Zendesk |
| Sourced answers to technical questions | RAG assistant | pgvector, Qdrant, Azure OpenAI |
| Proposal and RFP drafts | Template-based LLM helper | Microsoft 365 Copilot, Copilot Studio |
| AI features in your product | LLM integration with cost tracking | Anthropic Claude, OpenAI, Supabase |
| Repetitive internal follow-ups | AI agent | n8n, MCP, calendar and email connectors |

The table is a starting point. We choose the architecture based on where your data lives and what your client contracts allow.

## How do we handle security and data?

Clients of IT and cybersecurity firms rightly ask where their data goes. So every project starts with a data flow diagram: which fields reach the model, which are masked, where logs are kept and for how long. If a step involves transferring personal data out of Türkiye or the EU, we settle it during design with your legal counsel, under GDPR and KVKK.

We apply the same approach in our own products. Promptor, our WordPress AI assistant, leaves it to the site owner to decide which content the assistant may use. In client projects, control stays with you as well.

A free discovery call is enough to begin. When you need a broader roadmap, we start with the two-week [AI discovery sprint](/ai-discovery-sprint/).

## Frequently asked questions

### We already have engineers. Why bring in outside help?

Engineers busy with client work rarely get time for internal processes. We build and document the first workflows, then hand them to your team, or run them for you as a managed service.

### Would sending client data to a language model breach our contracts?

That depends on the contract and the architecture. During design we review options with your legal counsel: masking data, using region-pinned services such as Azure OpenAI, or running models on your own servers.

### We use Microsoft 365. Isn't Copilot enough?

For documents and email it is often a good start. Features embedded in your own product, multi-source RAG or agents tied to your ticketing system need additional development.

### Can you work with a team outside Türkiye?

Yes. We work remotely in English and Turkish, our working hours (GMT+3) overlap with European office hours, and we can join your existing tools and sprint rhythm.