Services · frontzen.com
AI integration
Practical AI adoption for your business — from automating routine work to AI assistants over your own data and team training.
- AI process automation
- Assistants over company data (RAG)
- Claude / OpenAI API integration
- Team training
AI where it actually saves hours
I don’t sell AI magic. I find the processes in your company where large language models demonstrably save hours — inquiry processing, document triage, drafting replies, internal search — and build solutions that plug into the tools you already use.
You work with the engineer who builds it. The audit, the architecture, the code and the training are all me, so nothing gets promised in a meeting and discovered to be impossible three weeks later.
What I typically build
- AI assistants over company data — employees ask in natural language and get answers from your policies, contracts, and documentation (RAG).
- Routine-communication automation — drafted replies to emails and inquiries, summaries of long threads, data extraction from attachments.
- AI steps inside processes — categorization, translation, document checking, and first-draft generation wired directly into workflows (Claude or OpenAI API).
- Team training — hands-on workshops on using AI safely and effectively in day-to-day work.
AI drafts it, I stand behind it
AI is no longer an experiment — it is in production at the companies you buy software from every day. The difference is who stands behind the result: every solution I ship, I personally read, test, and sign off. You judge it on the business outcome; getting the engineering right is my job.
Pilots start at $15,000 and go to production, not to a slide deck.
Data safety
Every proposal includes a data map: which data may go to which model, what runs in an isolated environment, and how regulated data is handled. I build to work within the obligations you already carry — HIPAA-covered records, SOC 2 controls your auditor will ask about, CCPA and GDPR rights requests — by keeping regulated categories out of third-party models by default and scoping retention explicitly. I use APIs in no-training mode. I don’t hold a SOC 2 report myself and won’t claim one; what I deliver is designed to pass through yours.
When it makes sense
- Your team spends hours on repetitive reading, writing, and sorting
- You process a high volume of inquiries or documents
- You want AI but need a business case before you commit budget
How this works
- 01
Process & data audit
Where the hours go, what repeats, where the data lives. Without this, AI is a toy.
- 02
Pick a pilot with ROI
One process with clear payback. I measure today's cost — the baseline to beat.
- 03
Pilot in a few weeks
Claude/OpenAI API integration, a workflow shaped to your process, wired into your tools.
- 04
Measure vs. baseline
After a month we compare numbers. Works → expand. Doesn't → adjust or stop.
- 05
Expand & train
More processes, team training, data rules. AI as part of operations, not a pilot that never ended.
What you get
- Working AI workflow in production
- Data map (what may go where, under your privacy obligations)
- Savings evaluation vs. baseline
- Hands-on team training
- Documentation and handover
FAQ
Where does AI actually pay off in a company?
Wherever people repeatedly read, write, or sort: answering emails and inquiries, summarizing documents, drafting standard write-ups, categorizing data. I start from the process, not the technology.
Will AI leak our company data?
No. I build on APIs with no-training modes, and sensitive documents are processed in an isolated environment. Every proposal includes a data map — what data may go where, and which regulated categories never leave your infrastructure.
Do we need our own IT team?
No. I build the tool, deploy it, connect it to your systems, and train your team. I can also run and maintain it for you.
How do we know it was worth it?
Before deploying I measure what the process costs today, and compare after. The baseline is written into the proposal, so the result is checkable against a number you agreed to in advance.
How much does an AI integration cost?
AI integrations start at $15,000 for a pilot in production — process audit, working workflow, measurement against the baseline, and team training. Fixed price against a written scope, billed across milestones.