Speaking twice at Dreamforce · Sept 15-17 →

Salesforce MVP Hall of Fame · Certified Partner since 2010

OpenAI models and agents, implemented for enterprise.

GPT for reasoning, the Agents SDK for tool-using workflows, and Azure OpenAI when your security team wants Microsoft-stack residency. We implement the path that matches your controls, then connect it to Salesforce, n8n, and your memory layer. The first week is in the org, reading what already exists.

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Certified Partner since 2010 · MVP Hall of Fame · 200+ agents in production · UAE and US desks

Related use cases
  1. 01Models · Claude, Gemini, OpenAI, Bedrock
  2. 02Agents · Agentforce, CrewAI, ADK, A2A
  3. 03Memory · Neo4j, Cognee, RAG
  4. 04Identity · IndyKite, SSO, entitlements
  5. 05Governance · policy, audit, human override

What we put in

The work on this layer.

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.

Do you support Azure OpenAI as well as the public API?
Yes. Azure OpenAI is the usual path when data residency, Microsoft identity, or an existing Azure estate matters. The public API is faster to start when those constraints are not in play.
Is this only models, or agents too?
Both. We deploy GPT as a model and the Agents SDK as a runtime. Agents then call your tools through MCP and, when needed, talk to other agent frameworks over A2A.
Will you lock us to OpenAI?
No. We routinely route legal work to Claude and Workspace tasks to Gemini. OpenAI is one option in the stack, not the stack.
How do you control cost?
Per-team budgets, model routing, and logging. You see which use case spends what before the bill surprises finance.

The brief

Need OpenAI in a stack you can govern?

Tell us whether you need the public API, Azure OpenAI, or an agent runtime. We will come back with the architecture and the gaps.