Who This Is For
Your team spends hours a day moving information between systems: reading documents, re-keying data, chasing approvals, and routing requests to the right person.
You have a process with clear rules and frequent exceptions, and the exceptions are what eat the time.
You have already tried a chatbot or a copilot, and it helped individuals but did not change how work actually flows through the organization.
Volume is growing faster than headcount, and hiring more coordinators is not the answer you want to give the board.
You need automation that leaves a trail: who approved what, which document triggered which action, and where a human stepped in.
Typical first workflows: document intake and classification, approval chains, order and claim processing, vendor onboarding, reporting assembly, and customer-request routing.
What You Get
Autonomous workflows that read, decide, and act across your existing systems, from email and document stores to your ERP, CRM, and ticketing tools.
Human-in-the-loop checkpoints where judgment matters, with a queue your people actually want to work.
Full traceability: every agent action logged, every decision explainable, every exception routed with context.
Measured results in the terms you report on: cycle time, throughput, error rate, and hours returned to the team.
A system your engineers can operate and extend, with runbooks, monitoring, and a clear ownership hand-off.
How It Works
Design, prototype, harden, deploy. Each phase ends with something you can see working, not a status report.
Weeks 1 to 2
- •Map the process end to end with the people who run it today: inputs, systems touched, decisions made, and where it breaks.
- •Separate the rule-based steps from the judgment calls, and decide which the agent handles and which it escalates.
- •Define success metrics and the guardrails the agent must never cross.
Output: Workflow blueprint, integration inventory, and an agreed definition of done.
Weeks 2 to 5
- •Build a working agent against real, representative cases from your own history.
- •Connect to your actual systems in a sandboxed mode so the integration risk surfaces early.
- •Review outputs side by side with your process owners and tune until they trust it.
Output: A demonstrable agent handling live-shaped work with measured accuracy.
Weeks 5 to 8
- •Add retries, idempotency, rate limits, and failure handling so the agent degrades gracefully instead of silently.
- •Wire in access controls, audit logging, and the escalation paths your compliance team needs.
- •Load-test against realistic volume and run a structured exception review.
Output: Production-grade system with observability, controls, and documented failure modes.
Weeks 8 to 12
- •Roll out in stages: shadow mode, then assisted, then autonomous for the cases it has earned.
- •Train the operators and the engineers who will own it, with runbooks for the exceptions.
- •Hand off with a measurement dashboard and a plan for the next workflow.
Output: A live workflow, an owning team, and a baseline for what to automate next.
Have a workflow in mind?
Bring it to a working session. We will tell you within the hour whether it is a good first agent, and what it would take.
Example Outcomes
Anonymized scenarios that show the shape of the work and the kind of results to expect.
An agentic workflow automated 80% of quality-inspection documentation and routing, cutting cycle time from 48 hours to under 6, with full traceability built into the existing MES.
Intake, conflict checks, and engagement-letter drafting moved from a three-person coordination loop to an agent with partner sign-off. Turnaround dropped from days to hours and the audit trail improved.
A document-triage agent classified inbound correspondence, extracted the required fields, and routed exceptions to reviewers with context. Manual review stages fell from three to one.
What We Deliver
End-to-end workflow blueprint with decision boundaries and escalation rules
Production agent code, deployed into your environment, owned by you
Integrations to the systems the process actually touches
Human review queue and audit log
Monitoring, alerting, and runbooks for operators
Measurement dashboard tied to the metrics you agreed on in week one
Training and hand-off for the team that will run it
How We Collaborate
Agents only work when the people who own the process trust them. We build with your operators in the room, not around them.
Typical stakeholders: a process owner who can explain the exceptions, an engineer with access to the systems involved, and a sponsor who owns the metric we are moving.
Where it runs: in your cloud or on your private AI stack. We use the model providers you approve and never route your data through ours.
After launch: the code, the prompts, and the runbooks are yours. Ongoing support is available, but never required.
Pick the first workflow
The best first agent is a process that is painful, frequent, and well understood. If you have one in mind, we can usually scope it in a single conversation.
Book a Working Session