AI stack · Automation and voice
The workflow carries the agent.
n8n moves work between models, Salesforce and the rest of your systems. Retell answers the phone. Both write back to the record and hand off when they should. 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
- 01Models · Claude, Gemini, OpenAI, Bedrock
- 02Agents · Agentforce, CrewAI, ADK, A2A
- 03Memory · Neo4j, Cognee, RAG
- 04Identity · IndyKite, SSO, entitlements
- 05Governance · policy, audit, human override
On this layer
What we put in.
The honest no: if the work is a software factory with a named delivery date, we are the wrong partner.
Where it shows up
The work this layer does.
Use cases where these tools carry the job. Each one names the stack.
Invoice processing
Extract, match, and route without a shared inbox.
HR onboarding
Provisioning sequences your HR team does not babysit.
AI Sales Pipeline Automation
Lead routing and enrichment into Salesforce.
Expense reports
Capture to policy check to reimbursement.
AI Customer Service Transformation
Voice plus chat, with one customer memory.
Meeting scheduling
Booking agents that do not lose the thread.
Health awareness
Multilingual voice where a form would fail.
Questions
What buyers ask us.
- n8n, MuleSoft or Salesforce Flow?
- Flow for logic that lives inside the org. n8n for fast, self-hostable automation across tools. MuleSoft when many systems need the same governed API. We pick per integration, not per vendor.
- Can a voice agent take real calls?
- Yes, on a named call type with clear rules: booking, status, intake. It hands off to a person when the caller asks or the answer is unsure, and the call summary lands on the Salesforce record.
- What happens when a step fails?
- It retries with a limit, then lands in an exception queue with an owner. Nothing is silently dropped, and a write is never posted twice.
The rest of the stack
One layer is not a system.
The plane this layer lives in, and the other four layers.
Durable plane
Queues and retries so a timeout does not post twice.
Models
The reasoning layer. We pick the model that fits the job, then deploy it so data stays where it should.
Agentic systems
How agents plan, call tools, and talk to each other. Frameworks and protocols, not a single vendor lock-in.
Agentic memory
What the agent remembers: graphs, identity, and long-term context so answers stay grounded.
How we engage
Deployment and governance. The work that turns a pilot into a production system.
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.
Name the process. We tell you which layer is missing.