Services
AI implementation and automation consulting.
For teams that have decided AI should be doing real work — not running another proof of concept.
Three ways teams engage
Most engagements start with one workflow and expand once it is demonstrably saving time. Each area below can stand alone or combine.
AI Implementation & Consulting
Getting large language models doing real work inside the systems you already run — scoped, evaluated, and shipped to production rather than stuck in a pilot deck.
- LLM integration
- Custom agents
- Evaluation & guardrails
Business Process Automation
Removing the repetitive work between your tools: data pipelines, CRM hygiene, multi-channel outreach, reporting, and the internal handoffs nobody owns.
- Workflow automation
- CRM & data pipelines
- Internal tooling
Custom MCP Server Development
Giving Claude, ChatGPT, Cursor, and Copilot safe, governed access to your internal APIs and databases through Model Context Protocol servers built for your stack.
- MCP servers
- Auth & permissions
- Agent-ready APIs
How engagements work
The goal of the first engagement is not a strategy document. It is one workflow running in production that you can measure.
- 01
Discovery
Map where the hours actually go. Interview the people doing the work, look at the systems involved, and rank candidate workflows by volume, rule-clarity, and API access. Output is a short list with rough effort for each.
Typically 1–2 weeks
- 02
Pilot
Build the highest-value workflow end to end against real data — including the unglamorous parts: error handling, retries, logging, and the human handoff when the system is unsure. It runs in production, not in a demo environment.
Typically 2–6 weeks
- 03
Production & expansion
Add monitoring and alerting, hand over documentation and code, and train whoever maintains it. Once the first workflow is provably stable, the same plumbing makes each additional one cheaper.
Ongoing or fixed-scope
A good fit when
- A repetitive process runs often enough that people complain about it.
- The systems involved have APIs, or at least exportable data.
- Someone internally can say yes without a six-month committee.
- You want the thing built, not a slide deck describing it.
Probably not a fit when
- You need a vendor with a 40-person bench and a formal RFP process.
- The work is rare, highly contextual, or different every single time.
- The underlying process is broken — automation would just speed up the mess.
- Nobody internally owns the outcome the automation is meant to improve.
Questions buyers actually ask
What size company is this built for?
Mid-market organizations — roughly 50 to 1,000 people — where processes are big enough to be expensive but the company does not have an internal AI platform team. Larger enterprises engage for scoped work on a single department or workflow rather than company-wide transformation.
How does an AI consulting engagement usually run?
Three stages. Discovery maps where hours actually go and identifies the one or two workflows worth automating first. A pilot builds that single workflow end to end, in production, against real data, typically in two to six weeks. Rollout hardens it with monitoring and handles the next workflows once the first is demonstrably working.
Who does the actual work?
You work directly with the person writing the code. There is no account manager layer and no junior team learning on your engagement. That is the trade-off: less parallel capacity than a large consultancy, and far more senior attention per hour than one.
What happens to our data?
Automations run inside your existing environment and against your own accounts wherever possible. Model calls can be routed to providers with zero-retention terms or to models running in your own cloud when policy requires it. Data handling and retention are defined during scoping, before anything is built.
Do we have to replace the tools we already use?
Almost never. Most of the value comes from connecting the CRM, databases, spreadsheets, and messaging platforms already in place. A good automation slots into the existing process invisibly — the same people work in the same tools, with the repetitive steps handled in the background.
What do we own at the end?
You own the code, the infrastructure, and the credentials. Everything is delivered into your repositories and cloud accounts with documentation, so your team or another vendor can maintain it. There is no proprietary platform holding your automations hostage.
When is automation the wrong answer?
When a process is rare, highly contextual, or changes shape every time it runs, automating it costs more than it saves. The same applies when the underlying process is broken — automating a bad workflow just produces bad outcomes faster. Those cases get flagged during discovery rather than quoted.
Start with the workflow that hurts most
Tell me where your team is losing hours. If it can be automated, I’ll tell you how and roughly what it takes. If it can’t, I’ll tell you that too.