AI Consulting
Adopt AI without the guesswork.
Most AI projects fail because they start with the technology instead of the workflow. We find the handful of places where AI pays for itself in your business, then deploy it safely — hosted or entirely on your own hardware — with your data under control.
Book a free consultationEnthusiasm is not a strategy
Your team is already using AI tools, whether or not you've approved them. The question is no longer if you adopt AI, but whether adoption happens deliberately — with the right use cases, the right guardrails, and a clear line around what your data is allowed to touch.
- A ranked shortlist of use cases with real ROI
- Working pilots instead of indefinite evaluation
- Data governance decided before anything is exposed
- Integration with the systems you already run
- An acceptable-use policy your staff will actually follow
- A deployment model that matches your data sensitivity, on-prem included
What we do
AI readiness assessment
Where your data, processes, and team stand today, and what has to be true before AI helps.
Use-case discovery
We shadow real workflows to find the tasks worth automating — and the ones that aren't.
AI coworking & daily use
Getting AI into the actual working day — drafting, research, analysis, and review — with the prompting practice and shared patterns that make it stick past week two.
Agents & autonomous workflows
Agents that take multi-step work off your team: triage, reconciliation, reporting, and research runs, scoped with clear boundaries, approval gates, and an audit trail.
Platform & model selection
Claude, ChatGPT, and Gemini each win at different things. We match model to workload on capability, cost, latency, and where your data is allowed to live — and we stay vendor-neutral about it.
Workflow automation
Document processing, support triage, reporting, and internal search, built and measured.
LLM integration
Assistants and retrieval systems wired into your actual data sources, with evaluation built in.
Local & self-hosted LLMs
Open-weight models running on your own servers or private cloud. Nothing leaves your network — the right answer for proprietary IP, client confidentiality, and regulated data.
Data governance
Classification, retention, vendor terms, and privacy controls so nothing leaks into a model.
Technologies we deploy and manage
- Claude
- ChatGPT
- Gemini
- Llama
- Mistral
- Qwen
- Ollama
- vLLM
- LM Studio
- Hugging Face
- LangChain
- LlamaIndex
- DeepSeek
- Pinecone
- Weaviate
- n8n
- Cursor
- Lovable
Product names are trademarks of their respective owners. Listing them indicates the technologies we work with, not endorsement or affiliation.
What working together looks like
- 01
Audit
Current tool usage, data readiness, and the workflows that consume the most time.
- 02
Prioritize
Use cases scored on impact, effort, and risk — with the weak ones cut.
- 03
Pilot
A narrow production pilot with defined success metrics, typically in weeks.
- 04
Scale
Roll out what worked, with governance, monitoring, and training in place.
Common questions
- Will our data be used to train someone's model?
- Not under the architectures we recommend. Data handling and vendor terms are settled at the start of every engagement, before anything is connected.
- Do we need a data team first?
- No. Many high-value use cases run on documents and processes you already have. The readiness assessment tells you honestly what's in reach.
- Which AI platforms do you work with?
- Primarily Claude, ChatGPT, and Gemini, plus open-weight models such as Llama, Mistral, and Qwen for self-hosted work. We are deliberately vendor-neutral and pick per use case rather than standardizing on whatever we happen to resell.
- Can we run AI entirely in-house, with nothing leaving our network?
- Yes. Open-weight models run on your own hardware or private cloud, with no external API calls. It costs more up front in infrastructure and needs realistic expectations — the strongest self-hosted models trail the leading hosted ones — but for proprietary IP, privileged material, or regulated data it is often the only acceptable architecture. We size the hardware, deploy the stack, and hand over documentation.
- What does an AI agent actually do for us?
- The useful ones handle bounded, repetitive multi-step work — triaging inbound requests, reconciling records across systems, compiling recurring reports. We scope them narrowly, put approval gates on anything consequential, and log every action so you can audit what happened and why.
- How do you measure whether it worked?
- Every pilot ships with baseline metrics — time saved, error rate, throughput — agreed before we build anything.
Let's talk.
Free consultation, a straight answer about whether we're the right fit, and a detailed quote if we are.
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