Workflow Automation & AI Agents
Agents and automations wired into CRM, ERP and support tools with human approval where it matters.
The value of AI is in the work it takes off people. We build automations and agents that operate inside the tools your team already uses: reading, drafting, routing, updating and deciding within limits you set. Every action that carries risk has a human approval step, and every action is logged.
Design starts with the process, not the model. Many workflows need reliable automation with a small amount of AI judgement, and we build exactly that rather than an agent for its own sake.
- Human-in-the-loop controls are designed in from the first sketch, not added after an incident.
- Our security practice tests every agent for prompt injection and tool abuse before launch.
- We measure hours saved and error rates, so the business case is checked after launch, not assumed.
- A bank or insurer with claims, onboarding or compliance queues handled by hand at scale.
- A logistics or airline operations team where exceptions are routed by email and phone.
- A shared services centre inside a group processing invoices, tickets and requests across subsidiaries.
- A team's day is reading, classifying and re-keying rather than deciding.
- Backlogs grow at every peak and are cleared with overtime.
- Staff have started using unapproved AI tools to cope.
- Previous automation broke on the first exception.
What is included.
- 01
Process design
The workflow mapped step by step with decision points, exceptions and where a person must stay involved.
- 02
Agent and automation design
Which steps are deterministic, which use a model and what tools each agent can call.
- 03
Integrations
Connections to CRM, ERP, ticketing, email and internal systems with proper authentication.
- 04
Guardrails
Approval flows, action limits, input validation and output checks.
- 05
Monitoring and evaluation
Logging, quality sampling and alerting on failures or drift.
Four steps, no surprises.
- 01
Map the process
Workshops and observation to document the workflow and its exceptions.
- 02
Design
Automation architecture, agent boundaries and approval points.
- 03
Build
Integrations, agents and guardrails delivered in phases with staging tests.
- 04
Operate
Launch with monitoring, then tune from real usage.
From first meeting to steady state.
- 01Weeks 1 to 2
Map
Workshops and observation document the workflow, its exceptions and where a person must stay involved.
- 02Weeks 3 to 4
Design
Automation architecture, agent boundaries, tool permissions and approval points.
- 03Weeks 5 to 10
Build
Integrations, agents and guardrails delivered in phases with staging tests and a security test before launch.
- 04Week 11 onwards
Operate
Launch with monitoring, then tuning from real usage and new workflows on the retainer.
- Hours removed from the workflow per period.
- Error and rework rate on automated cases against manual handling.
- Share of cases resolved without human intervention and share correctly escalated.
- Time from case arrival to resolution.
- Automation architect
- AI engineer
- Integration developer
- Process analyst
- Security tester
- Process map and automation design.
- Built integrations and agents.
- Guardrail and approval configuration.
- Monitoring dashboard and alerts.
- Operations runbook and training.
Automation projects are fixed scope after a short design phase of one to two weeks. A first workflow typically takes four to ten weeks to production. Ongoing monitoring, tuning and new workflows run on a monthly retainer.
AI/LLM Security Assessment
Prompt injection, data leakage and model abuse testing for AI features, assistants and agents.
IT ConsultingSystems Integration & Data Infrastructure
Connect ERP, CRM, e-commerce and data sources through APIs and pipelines so data moves without hands.
AI Consulting & AutomationCustom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
Workflow Automation & AI Agents, in plain terms.
n8n, Make or Azure Logic Apps for orchestration, with LangGraph or custom code for agents that need more control. The choice depends on your systems and who will maintain it.
Limited tool permissions, approval steps for consequential actions, input and output checks and full logging. Agents get the least access needed for the task.
Usually, through APIs, database access or, as a last resort, structured file exchange. We confirm during the design phase.
The workflow is designed for it: confidence thresholds route uncertain cases to a person, and quality sampling catches drift over time.