Connect business tools and reduce repetitive work with bounded, reviewable automation.
At a glance
Connect business tools and reduce repetitive work with bounded, reviewable automation.
What we implement. What we support.
Choose the systems you need to improve. We define the scope, dependencies and acceptance criteria before work begins.
AI business workflow automation
Best for
Connect business tools and reduce repetitive work with bounded, reviewable automation.
Implementation & integration
Map a process such as service-request triage, document intake or approval routing. Connect agreed CRM, ticketing and knowledge systems through APIs or MCP where appropriate. Use rules for deterministic steps and AI where interpretation adds value.
Managed support
Review failed runs and exceptions, monitor model and connector changes, evaluate output quality and track usage costs. Keep a named process owner and a manual fallback.
Acceptance & handover
Workflow map, permission boundaries, approval steps, audit records and evaluation cases. Measure cycle time, error rate and human review effort against a baseline; validate results before expanding automation.
Support depends on the agreed versions, integrations and access. Vendor subscriptions and licenses are separate; vendor partnership or certification is not implied. Coverage hours, escalation paths and response targets are agreed in writing. 24/7 coverage requires a managed operations agreement.
Illustrative scope, not a deployment blueprint. Interfaces, permissions and acceptance criteria are agreed for each engagement.
Rules for predictable decisions
Use deterministic rules for validation, routing and known business conditions. A workflow can be useful without a model, an agent or MCP. Establish the manual baseline and acceptance criteria first.
AI assistance for interpretation
Use a model where interpretation adds value: extracting fields, classifying a request or proposing a draft. Check outputs against allowed values and supporting records. Uncertain or incomplete results go to a named reviewer.
Bounded autonomy, when justified
Allow a system to select and execute actions only within an explicitly approved scope. Enforce permissions in the connected systems, require approval for consequential changes, and set time, retry and cost limits. Stop and route exceptions to a person.
Integration is not authorization
APIs and MCP can connect tools and data. MCP does not automatically make a workflow secure: authentication, server-side authorization, data handling and audit controls still need implementation and testing. Treat retrieved text and tool output as untrusted input.
Example: service-request intake
Illustrative design; actions and approval thresholds are agreed for each process.
Receive a request and validate required fields with rules.
AI proposes a category and summary using approved records.
A reviewer approves the proposed action; missing or uncertain information enters the exception queue.
An authorized integration performs the approved action and records the result. Failed or duplicate events use bounded retries or manual handling.
The problem
AI pilots stall when data quality, model behavior, permissions and process ownership are unclear. Start with a bounded workflow and representative evaluation cases before expanding access.
How we work it
Integrate tools and approvals
Connect only the required systems through APIs or MCP where appropriate. Validate access controls, approval gates, retries and manual fallback before enabling actions.
What you get
Workflow design with rules, AI assistance and approval boundaries
API/tool integrations with scoped permissions
Guardrail, eval, and audit configuration
Operator evaluation loop
Stack
AI Agents
MCP
Policy as Code
Outcome
Bounded automation with measured results, named ownership and a tested manual fallback.
A closer look at the deliverables
Illustrative templates for scoping and reviewing an engagement. These are not customer results, vendor-certified procedures or ready-to-run production instructions.
Illustrative expected result
Automation acceptance checklist
AI workflow evaluation report
Prerequisites
Describe the process, approved data sources and permitted actions. Capture baseline cycle time, error rate and human review effort; document permissions and approval boundaries.
Expected result / acceptance threshold
Evaluation outcomes compared with the baseline; approvals, exception routing and manual fallback demonstrated.
