Every engagement runs along the same path — but the stage you enter depends on where you are. Some clients start at Readiness. Others come to us already at Pilot, with an agent that isn’t behaving. Others are already at Scale and need the governance to stand behind what’s live.
Skipping Readiness doesn’t make the gaps disappear — it just moves them later, where they’re more expensive to fix. If you come to us already at Pilot or Scale, we’ll sometimes pause to close a gap first, rather than build on a foundation that puts the work at risk.
Is your data, your processes, your team, and your governance ready to support AI — and do you know which use cases to build first? We assess before we build.
A scoped build with a defined human-in-the-loop boundary, designed to fail safely.
Operationalize what works, with the governance and audit trail to stand behind it.
It’s a structured, 4–6 week engagement: we assess where your business actually stands today, then hand you a scored report, a prioritized roadmap for closing the gaps we find, and a recommended sequence for which use cases to build first. No build, no implementation — just an independent read on where you are.
This is what Readiness above actually means. We score your business across five pillars, independently, because you can be strong on four of them and still get stuck on the fifth:
Is your data actually usable by an AI system — organized consistently, accurate, and traceable back to where it came from? People can work around messy data using judgment. Software can’t. AI needs cleaner data than a person would.
Are your operations documented enough to insert AI predictably? The workflows worth automating are usually the ones leadership already feels the friction of.
Can the infrastructure actually store, move, and serve what an agent needs, at the scale you’re asking for? A modern platform doesn’t fix dirty data underneath it, and clean data doesn’t run on an engine that isn’t there.
Will the humans actually use it, trust it, and not quietly work around it? Adoption is a clarity problem before it’s a training one.
Who’s accountable, is there a business case, and will it work within the rules you already operate under? This is also where we decide which use cases to build first, with an ROI model behind each one. It’s the pillar many agentic deployments treat as optional — enough that it gets its own section, next.
Who is the agent allowed to talk to? What data can it touch? What can it do, on whose behalf, and where does the audit trail live when someone asks?
These aren’t future-state questions. They’re the difference between a deployment that holds up under review and one that quietly accumulates risk until somebody notices. We’ve seen this pattern for years in client systems, long before AI entered the picture. Get the access and governance right, and nobody ever thinks about it again. Get it wrong, and it surfaces eventually, usually as an audit finding. AI deployments don’t get a pass on that discipline. They need more of it.
Governance is easy to defer when you’re piloting. It’s expensive to retrofit once you’re scaling. Firms that get further along the curve don’t need governance less — they need it structured earlier, and they need it to travel with every new agent that gets deployed.
This is the next step after a Readiness Assessment. We also work with clients who come to us already at Pilot or Scale.
Custom agents scoped to a specific process, with a defined human-in-the-loop boundary. We design them to fail safely and stop where they should.
Automation that crosses systems, pulling from Salesforce, Jira, Confluence, Microsoft, and wherever the work actually lives.
That’s what the Readiness Assessment above is for. Start with a conversation and we’ll tell you where your business actually is. Talk to Us →
We’re working with a financial services firm operating across both sell-side and buy-side deal work. We’re designing the AI-augmented version of their deal workflow — analyst-grade work like research, modeling, document preparation, and first-pass diligence, the parts AI is good at once the data and process are set up correctly. The judgment calls and the negotiation stay with the senior team, where they belong.
Long before it had a service line attached, we were using AI inside our own work: writing Apex, building Flows, shipping Lightning Web Components, running migrations, generating the documentation nobody wants to write. That’s a practitioner’s view of where AI helps and where it doesn’t, not a deck about what it could theoretically do.
We start with assessing what’s already in your stack and whether we need to add or switch AI tools to bring you success. Tearing out a system to make room for AI is rarely the right move — we work where your data already lives, and we’ll tell you honestly which platform fits the workload instead of steering you toward one we’d rather sell.
If you’re already deep in Salesforce and the data an agent needs already lives there, Agentforce has a lot of advantages — native to the system your team already runs on, with the permissioning and audit trail Salesforce already provides.
In addition to Agentforce expertise, our team is highly trained in Claude and has experience with AI in other PSA solutions. Choosing the right tool is often a combination of your unique needs, the status and location of your data, and the cost-benefit analysis of different options — we walk you through this and help you decide the best fit. The same discipline applies regardless of the tool you use: fail safely, stay inside a defined human-in-the-loop boundary, and leave an audit trail behind.
4 to 6 weeks, start to finish. You get a scored report across five pillars, a roadmap for closing the gaps we find, and a read on which use cases to build first. No build, no implementation — just an independent look at where you stand.
Using an AI tool and deploying an AI agent inside your business are different things. A person using a chatbot can catch its mistakes. An agent acting directly on your data and workflows needs cleaner ground underneath it. The assessment tells you exactly where that gap is, if there is one.
That’s a useful answer, not a failed engagement. You leave with a prioritized roadmap for closing the specific gaps we found. We can support you through the next steps, or you can close the gaps yourself and return when you’re ready to build. The scope of our full engagement is in your hands.
Most AI vendors sell an agent first and find out if your data can support it later. We assess first, because that’s the order that actually holds up once something is live. We’re also not locked into one platform — we build on Claude, on Agentforce, or wherever your data already lives, and we’ll tell you honestly which one fits.