Fig. 07AI Consulting
AI consulting.
Practical AI uses language models and automation to do real work — things like customer-support assistants, document and email automation, data extraction and sorting, and internal copilots. We help you find the ones actually worth doing.
AI isn't always the answer — and if a simple rule or a better process beats a model, that's what we'll recommend. That honesty is the whole point of bringing us in.
For owners who keep hearing 'AI' everywhere and want a practical read: we look at your real workflows, find the spots where AI genuinely saves time or money, and build something that proves it before you commit to more.
What you get
What's included.
- 01
An audit of your workflows to find real automation opportunities
- 02
A prioritized roadmap — biggest payoff and lowest risk first
- 03
A working proof of concept on your actual data, not a demo
- 04
Implementation of the things that pass the test
- 05
Guardrails and monitoring so the AI behaves and stays accurate
- 06
Training so your team can use and trust the tools
- 07
Honest go / no-go calls at every step
How we work
The process.
- 01
Audit
We sit with how you actually work and flag where AI could help — and, just as often, where it can't.
- 02
Prioritize
We rank the opportunities by payoff and risk so you spend first where it matters most. Fixed price, quoted on a call.
- 03
Prove it
We build a small proof of concept on your real data. If it doesn't earn its keep, we say so and stop.
- 04
Implement with guardrails
We roll out what works with monitoring, fallbacks, and evals — and train your team to run it.
Tools we reach for
The stack.
- Claude API
- OpenAI API
- RAG
- Function calling
- Agents
- Vector stores
- Evals
When it's not the right call
AI isn't always the answer. If a simple rule, a cleaned-up spreadsheet, or a better process beats a model — cheaper and more reliable — that's what we'll recommend. We'd rather lose the bigger project than sell you a model you don't need.
Questions
What people ask.
- What happens to my data?
- We use providers that don't train on your data through their APIs, and we keep sensitive information out of prompts where we can. We'll walk you through exactly what's sent where before anything goes live.
- Which model should I use?
- Whichever fits the job — we're not loyal to one vendor. We test Claude and OpenAI models against your actual task and pick the one that's accurate enough at the lowest cost. Sometimes the smaller, cheaper model wins.
- What kind of ROI should I expect?
- We won't promise a number we can't back up. The proof of concept exists precisely to measure it — hours saved, errors avoided, cost removed — before you invest in a full build.
- What's the ongoing cost?
- Two parts: our work (fixed, quoted on a call) and the model usage (paid at cost to the provider, with no markup from us). We'll estimate the running cost up front so there are no surprises.
Next step
Tell us what you're trying to build. We'll tell you the honest way to build it.