AI Consulting
Where AI fits in your ecosystem, and what it takes to run it well — decided before anything is built.
The problem
Most AI initiatives pick a tool before they have decided what problem it is solving, whether it should run locally or in a vendor cloud, or who is accountable when it is wrong.
The outcome
A written 90-day roadmap: where AI fits across your systems, a local-vs-cloud decision grounded in your data, and a governance and accountability plan you can act on.
- This is how we started: evaluating vendor software for clients before we ever built our own — that same readiness-first thinking now applies to AI.
- Grounded in the same commitments our own research holds itself to: reliability, running locally, and accountability to a human.
What's included.
- Readiness and use-case discovery across your systems.
- A local-vs-cloud decision, grounded in your data sensitivity and hardware.
- A data and privacy posture assessment.
- A governance and accountability plan: human-in-the-loop policy and an audit approach.
- A written 90-day roadmap.
How this actually runs.
Readiness & use-case discovery
A paid discovery sprint across your systems — where AI could genuinely help, and where it can’t yet.
Local vs. cloud
A clear-eyed decision on what should run locally and what can run in a vendor cloud, grounded in your data sensitivity and hardware.
Data & privacy posture
Where your data can go, where it must not, and what that means for architecture.
Governance & accountability
A human-in-the-loop policy and an audit approach, so someone is always accountable for what the system decides.
90-day roadmap
A written plan you can act on — with us, or on your own.
Built on.
- GGUF / llama.cpp
- OpenAI-compatible & Anthropic APIs
- On-prem GPU & Apple Silicon inference
See the products this work is grounded in.
All products
CrestBid AI, Orchestrator Studio, and Vexilo — three local AI products built by the same team.
Questions worth asking.
No — readiness and use-case discovery is the first step, not a prerequisite. Part of the engagement is telling you honestly where AI fits and where it doesn’t.
No. The roadmap is built around your systems and constraints; where a local-first deployment or a fine-tuned model make sense, we say so — and where they don’t, we say that too.
This is where those start: Consulting decides what’s worth building and how. LLM Engineering & Evaluation builds and proves a model works on your data. Local AI Deployment gets it running on your hardware. Many engagements move through more than one.
Ready to talk about ai consulting?
Where AI fits in your ecosystem, and what it takes to run it well — decided before anything is built.