Salesforce MVP Hall of Fame · Certified Partner since 2010
One AI use case. In production. With the controls on.
Most pilots fail at production because nobody planned data access, identity, or governance. We assemble the stack from the process, then ship one seat with those controls attached. The first week is in the org, reading what already exists.
Certified Partner since 2010 · MVP Hall of Fame · 200+ agents in production · UAE and US desks
The work
What we do in the org.
You have a use case, or a pilot that never left the sandbox. You need a production seat, not another workshop. The runtime might be Agentforce. It might be a custom stack. We pick from the process.
Sequence
Readiness, stack, then one seat.
- 01ReadinessWhat the agent is allowed to see, and whether that data is clean enough to act on.
- 02StackModel, runtime, memory, identity, connector. From the catalog, not from a preferred logo.
- 03Production seatOne process. Then we stay for the exceptions.
The honest no
When we are the wrong partner.
The honest no: if the work is a software factory with a named delivery date, we are the wrong partner.
Questions
What we actually say.
- Which models and runtimes do you use?
- Claude, Gemini, OpenAI, AWS Bedrock, Google ADK, CrewAI, n8n, Retell, Neo4j, Cognee, IndyKite, MCP, MuleSoft Agent Fabric, and Agentforce. We pick from the use case. See /ai-tools.
- Is this the same as setting up an agent?
- Narrower. If you can already name the job a person is doing like a robot, start there and we set up that one agent. Come here when you need the production path around it: the readiness check, the runtime choice, the governance, and the seats that follow the first one.
Before you pick a partner
Read these first. Then call anyone.
Our buyer guides. They apply whether or not you hire us.
Choosing an AI partner in the UAE
Five kinds of partner compared, the PDPL, DIFC, Arabic and WhatsApp questions, and red flags.
Choosing an AI implementation partner
Evaluation criteria, deployment models, governance fluency, and red flags.
AI partner evaluation framework
A five-part scorecard: depth, governance, integration, security, delivery.
Questions to ask an AI partner
Architecture, security, governance, pricing, and who is accountable.
AI partner vs in-house team
Hiring timelines, real costs, and dependency risk, scenario by scenario.
AI pilot to production
Which of the four ways your pilot stalled, and the sequence that moves it.
The brief
Send the situation.
One email. The situation travels with it.