Good fit
You want to understand whether Copilot, Gemini, Claude, ChatGPT, or another tool is actually useful for your team.
Not a fit
You want a tool reseller pitch or a recommendation based on one preferred vendor.
Practical AI advice for real-world businesses.
We help you figure out where AI fits in your organization, what tools are worth considering, and what to avoid before buying software or building something custom.
Grounded advice, clear tradeoffs, and small next steps. No big platform pitch or AI hype.
For teams sorting out what AI is worth using, avoiding, or building.
Good fit
You have repetitive spreadsheet, reporting, email, documentation, or admin work that may be worth improving.
Not a fit
You expect AI to automate everything or replace staff.
Good fit
Your staff are already trying AI, but you need shared rules, review habits, and privacy guidance.
Not a fit
You want vague AI strategy without looking at the real work your team does.
Good fit
You are considering AI in a product, internal tool, or customer-facing workflow.
Not a fit
You want a large custom software project by default.
Good fit
You want a practical second opinion before buying tools, training staff, or building something custom.
Not a fit
You are looking for a forced software migration or a complete rebuild of your current tools.
We review where AI can improve your daily workflows
The goal is to look at real work your team already does and decide where AI could help, where it needs guardrails, and where it should be left alone.
Office and admin work
Spreadsheets, reporting, customer messages, lead notes, quote follow-up, summaries, SOPs, email drafts, and handoffs.
Microsoft Copilot and Google Gemini
How to use AI inside tools teams may already pay for, including Excel, Sheets, Docs, Gmail, Outlook, and Teams.
Claude, ChatGPT, and AI assistants
Prompting, repeatable processes, internal templates, safe usage practices, and team guidelines.
Product and technical questions
If relevant, we can review AI-assisted development practices, product feature ideas, prototype planning, and developer handoff.
Internal automation opportunities
Where AI can support an existing process without replacing the person, team, or whole system.
AI readiness and risk review
Data privacy, quality control, review steps, human approval, and adoption concerns.
See a messy call note become a clean next step.
This is the kind of small workflow we look for first: take a rough note, pull out the useful details, flag what needs review, and make the next action easier to see.
Messy call note
Sarah called. Furnace acting up again, wants someone before Friday if possible. Mentioned it is making a weird noise and house is getting cold at night. Need to call back. Maybe under warranty? Ask about model and address.
Cleaned version
Click the button to see the same note organized into fields your team can review.
How Our Process Works
Book a Discovery Call→01Start with a discovery call
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Bring one process, tool question, product idea, or AI concern. We use the call to understand the situation and decide whether a deeper review makes sense.
02Map the real work
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If there is a fit, we look at what your team does today, what tools are already in place, and where AI is being considered.
03Separate useful ideas from risky ones
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We sort realistic use cases from ideas that are not worth doing yet, need tighter review, or should stay with a person.
04Recommend the next sensible step
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This may be using an existing tool better, setting team guidelines, running a small test, training staff, or scoping implementation support.
You leave with clear advice your team can actually use.
The discovery call is the starting point. If there is a fit, a consulting review gives you a useful path forward, whether that means using existing software better, running a small test, training the team, or deciding not to build something yet.
AI Report Summary
Practical review of your tools, risks, and next step.
ToolsRisksUse casesPilot plan
Current recommendation
Use existing tools first, test one focused workflow, and only build custom support if there is a clear reason.
Where AI could help
A plain-English summary of useful opportunities in your current process.
Where AI should wait
Privacy, quality, or reliability concerns to watch before using AI.
Recommended tools
Tool options matched to your current setup, existing software, and team habits.
Use cases worth testing
A short list of practical ideas that are specific enough to pilot.
Next step
A simple pilot plan, training plan, or decision not to build yet.
Work with someone who builds with these tools every day
When you book a call, you are not getting passed to a sales team or a faceless agency. You are talking directly with Tristan about your process, your tools, and whether AI is actually useful for the problem in front of you.
Professional software engineering background
Alchemized is run by Tristan Deane, a professional software engineer in Edmonton with 5+ years of experience building production software, internal tools, integrations, automations, and customer-facing applications for thousands of users.
Hands-on AI experience since late 2022
Tristan has been a power user of AI tools since they became widely available in late 2022. Outside of work, he regularly builds custom AI agents and workflows for fun, so the advice comes from real testing instead of hype.
Small steps before large projects
We look for the smallest useful test first. If AI is not the right fit for a process, we will say that instead of forcing a build.
Start with the tools your team already recognizes.
Sometimes the best first step is not another platform. It may be using the software your team already pays for in a more consistent way.
- Microsoft Copilot
- Google Gemini
- Claude
- ChatGPT
- Claude Code
- Excel
- Google Sheets
- Outlook
- Gmail
- Microsoft Teams
- Internal CRMs, forms, spreadsheets, and docs
Let’s review your workflow.
Bring the process, tool question, product idea, or privacy concern. We will help you sort out the next sensible step.
FAQ
AI consulting questions
Do we need to already know what AI tool we want?
No. A common starting point is sorting out whether you need Copilot, Gemini, Claude, ChatGPT, a simple process change, or no new tool at all.
Can you help us choose between Copilot, Gemini, Claude, and ChatGPT?
Yes. We can compare the tools against the work your team actually does, the software you already use, and the privacy or review requirements involved.
Can you train our team on day-to-day AI use?
Yes. If training is the right next step, we can help your team build repeatable prompts, review habits, and simple guidelines for common tasks.
Do you build AI features into products?
We can help review the product idea, identify useful AI features, plan a small prototype, or scope the implementation. The first step is deciding whether AI belongs there at all.
Can you create custom AI solutions or workflows?
Yes, if there is a clear need for it. During the discovery call, we can talk through the process, the tools you already use, and whether a custom workflow, small automation, or simpler setup makes the most sense.
Can this work with spreadsheets and existing office tools?
Yes. Many common use cases start with Excel, Google Sheets, Docs, email, reporting, meeting notes, or internal admin tasks.
Is this only for technical teams?
No. Many conversations are about office processes, customer messages, spreadsheets, reporting, documentation, or team guidelines. Technical and product questions are available if they are relevant.
Do we have to move off our current tools?
Usually not. We start with the tools your team already uses and only recommend a new tool if there is a clear reason for it.
Can you help implement your recommendation?
Yes, if there is a good fit. The discovery call helps decide whether the next step should be a review, training, a small pilot, or implementation support.
Are you tied to one AI tool or vendor?
No. We are tool-agnostic. Our recommendation depends on your process, existing software, privacy needs, and how your team will review the output.
How much time does this take from our team?
The discovery call is meant to be straightforward. A deeper review usually needs one or two focused conversations with the people who understand the work, plus examples of what you want to improve.
How do you handle privacy and sensitive business data?
We treat privacy and review steps as part of the recommendation. Some tasks are fine for AI assistance, while others need tighter controls or should stay human-reviewed.
What happens after the discovery call?
If there is a fit, we can recommend a small next step such as tool setup, team training, a pilot, product discovery, or implementation support.