AI Agentic System Engineering
A dedicated engineering team for AI agentic systems — autonomous, yet auditable for production. Every engineer works under the same audit, RBAC, and governance discipline, so you can automate fast without losing the ability to explain what an AI component did and why.
Every AI decision traceable and explainable, end to end
Real, delivered stack
Project-based delivery or outstaffing — whichever fits how your team works
Roles we staff
The people behind your AI platform — from engineering to process design.
Developers
Agent logic, orchestration workers, integrations.
QA
Testing the build itself — separate from third-party audits, which we also offer.
PM / BA
Requirements, process mapping, stakeholder alignment.
BPMN Engineers
Process modeling, BPMN/DMN, Operaton/Camunda configuration.
How we work
Project-based
We scope, staff, and deliver a defined solution end to end.
Defined scope · End-to-end delivery
Outstaffing
We provide roles that work inside your team and process
Flexible scope · Embedded in your team
Stack drawn from delivered engagements
LangGraph
BPMN / Operaton / Camunda
LangChain
LangSmith
Prefect
Confirmed against current team availability before we quote.
What’s built in, not bolted on
Full audit trail
Inputs → decisions → outputs, versioned and reproducible.
RBAC per action
Access is scoped per agent action, not per session.
Rollback & compensation
Human-in-the-loop plus a Saga-style pattern unwinds partial failures cleanly.
LLM Validator Node
Every response is checked before it reaches a user; failures loop back for refinement.
Intent-change handling
A mid-conversation topic shift resets the plan cleanly instead of corrupting state.
Context propagation
Entities resolved early in a session are reused, which also cuts token spend.
4–8 weeks · production-grade build
Governance & compliance approach
Exelor implements the technology — you keep control. Solutions run on your infrastructure, no real production data is touched during the build, and responsibility transfers to you formally, in writing, at handover.
NIST AI RMF: Govern → Map → Measure → Manage
Govern
A standing AI Risk Committee owns every engagement from day one, with fast incident escalation built in.
Map
Every AI component is classified at design time into Tier A, B, or C — documented, never assumed.
Measure
A golden test set, labeled by your own experts, validates every deployment against clear performance thresholds before go-live.
Manage
Any change to the model, prompts, or logic gets re-validated and signed off — nothing ships silently.
FAQ
How is pricing determined?
We don’t publish fixed pricing for any service type. Each engagement is scoped after a discovery conversation, because the right fit and effort depend on your specific workflow, not a generic package.
How quickly can a project launch?
An AI Agentic build typically launches in 4–8 weeks for a production-grade system, depending on integration complexity and the number of connected systems.
Who’s responsible for AI errors after handover?
Operational and compliance responsibility transfers to you formally, in writing, at handover — that’s set out upfront, not buried in a contract. If you’d rather we stayed involved, our Continuous QA tier covers ongoing validation, regression testing, and monitoring as the system and your business evolve.
How do you test a build without using our production data?
We don’t need it. Every deployment is validated against a golden set of real and edge-case scenarios labeled by your own subject-matter experts, data is pseudonymized before it ever reaches an AI component, and everything stays on your infrastructure — so testing is thorough without exposing live customer or transaction data.
What’s the difference between an AI Agentic build and BPMN Orchestration?
An AI Agentic build is for workflows where input can’t be reduced to a fixed flowchart — a schema plans each step and hands it to actor agents, scoped by validators and RBAC. BPMN Orchestration is a deterministic, flowchart-based process with AI in a bounded decision-support role, available as a separate engagement. We typically recommend BPMN for regulated or safety-critical processes and the agentic approach when inputs are genuinely unstructured.
Can this integrate with the tools and systems we already use?
Yes. Actor agents connect out to your existing tools and systems through MCP servers, with every input, output, and action scoped by validators and role-based access control — so integration doesn’t mean opening up unscoped access to your infrastructure.
What happens after go-live if our requirements change?
Any change to the model, prompts, or underlying logic gets re-validated and signed off before it ships — nothing changes silently in production. That review step is part of the standard build; if you want it handled on an ongoing basis after handover, that’s what our Continuous QA tier covers.
Find the right engagement model for your workflow
Tell us which workflow or system is the best first candidate — BPMN Orchestration, an AI Agentic build, or a standalone Validation & Audit.