Capability 02 / 06
Applied AI, measured on outcomes — not demos.
Most AI pilots stall somewhere between a promising demo and a process people rely on. We build agents and automation into the workflows you already run — sales, service, finance operations — with the guardrails and evaluation that make them safe to depend on.
Sound familiar
Where this usually starts.
An AI pilot impressed in a demo, then never reached production.
People copy data between systems by hand because nobody owns the automation.
Leadership wants an AI strategy, but no one can say which process it should touch first.
You're not sure how to stop an AI feature from confidently getting things wrong.
Deliverables
What we deliver.
- AI agent design & prompt engineering
- Agents scoped to a specific job, with defined inputs, tools and hand-off points to a person — inside Salesforce or around it.
- Predictive workflows & scoring models
- Lead, opportunity and risk scoring tied to an action someone takes, so the score changes behavior instead of decorating a record.
- Intelligent process automation
- Repetitive work — intake, routing, data entry, reconciliation — automated end to end, with exceptions routed to people.
- LLM-backed internal tools
- Search, summarization and drafting tools grounded in your own data, built for the teams that use them every day.
- Guardrails, evaluation & observability
- Test sets, accuracy checks, cost tracking and logging, so you know a model has drifted before your customers do.
Engagement
How the engagement runs.
- 01
Scope
Pick one process and define success in numbers — time saved, error rate, cost per transaction — before anything is built.
- 02
Architect
Data access, model choice, guardrails and fallback paths designed up front, including what happens when the model is wrong.
- 03
Build
Iterative delivery against a test set, with accuracy and cost measured at every milestone.
- 04
Maximum
Production monitoring and an evaluation suite your team keeps running after handover.
Questions
Asked before we start.
- Do you build inside Salesforce or outside it?
- Both, depending on where the work happens. If the process lives in Salesforce, the agents and automation belong there. If it spans several systems, an LLM-backed service alongside the CRM is often the better fit. That decision is made during scoping, with the reasoning written down.
- How do you keep AI from making things up?
- By narrowing the job, grounding answers in your own data, and testing against real examples before launch. Every build includes an evaluation set and logging, and anything high-stakes routes to a person instead of acting on its own.
- Is our data used to train public models?
- Not by us. Model and vendor choices are made with your data policy as a constraint, and the architecture document states exactly where your data goes.
- Where should we start?
- With one process that's high-volume, rule-heavy and tedious — not the most impressive one. A narrow first win builds the evaluation habits the larger projects depend on.
Talk through your AI & Intelligent Automation project.
A free 30-minute scoping call. You'll leave knowing whether we can close the gap, and roughly what it takes.