AI implementation advisory
Get AI working across your company, not stuck in a pilot.
I'm Rich Beams. I help small and mid-market companies find where AI actually pays across the business, in operations, finance, sales, support, and legal as much as in IT. Then I build it and keep the running cost under control.
28 years building production software. Five AI systems built and documented in the last eight months, plus two concepts. You work with me directly, not a delivery team.
The short version
Most companies do not have an AI strategy problem. They have an adoption problem: nobody owns it, nobody has ranked where it pays, and nobody is watching what it costs.
I fix that in a defined order: audit, build, then govern. I do both the advising and the building, so the plan stays limited to what I am willing to be accountable for shipping.
- Every department in scope, including the non-technical ones
- Fixed fees and fixed scope on the first two stages
- You keep the roadmap either way
The problem
The pilot works. Then nothing happens.
Across the market, most AI pilots never make it into production, and the reported causes are rarely the model. They are organisational: unclear ownership, data that was never ready, integration nobody scoped, and staff who were handed a tool without being shown why it helps them.
Nobody owns it
AI sits with whoever is most enthusiastic. There is no roadmap, no decision-maker, and no standard for what is allowed, so progress stops the moment that person gets busy.
No line of sight to value
Nothing has been ranked. Teams pick projects that are interesting rather than the ones that return money, and there is no baseline to measure against afterward.
Cost quietly compounds
Per-seat licences scale with headcount, every task gets routed to a premium model whether it needs one or not, and the bill arrives without anyone able to attribute it.
Where AI pays
This is a whole-company engagement, not an IT project.
The audit covers every function below and ranks the opportunities against each other, because the best first project is usually not the one people expect. Sometimes it is invoice processing. Sometimes it is support triage. It is rarely the chatbot.
How it works
Audit, build, govern.
Three stages, in order. You can stop after any one of them, and plenty of clients only need the first.
Engagements
Prices, stated plainly.
No discovery call required to find out what things cost. If the numbers do not fit your business, say so in the form and I will tell you straight whether I can help.
A specialty, not a slide
Most companies overpay for AI by defaulting every task to the most expensive model.
Legal review, a support reply, and a marketing draft do not need the same model, and they certainly do not need the same price per request. Matching each workload to the cheapest model that does that job well is one of the fastest returns available in most organisations.
I did not write that on a slide, I built it. AgentForge routes requests by department and complexity across marketing, legal, engineering, sales, support, and finance, with per-request cost attribution. It is deployed, and you can look at it.
Proof of work
Five systems built and documented. Two concepts.
Five systems I designed and built myself in the last eight months, each with a written case study. Two are live on public URLs you can open right now. One is a research proof of concept that ran a real study and reported a result I did not want. Two further entries are marked as concepts.
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Who you'd be working with
28 years of getting software into production.
I started at Accenture in 1997, building enablement software for an SAP R/3 rollout serving 5,000 users. Since then I have founded four technology companies, delivered work for roughly 70 clients, run a 500-seat CRM platform across multiple time zones, and spent two decades on business process automation, B2B integration, and enterprise security audits.
That history matters here for one reason. The hard part of AI adoption is rarely the model. It is legacy integration, data access, permissions, and convincing an organisation to change how it works. That is the job I have been doing since before AI was the reason for doing it.
Questions