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AI agents for business processes

An AI agent does more than answer: it reads a request, plans the steps, uses your systems and prepares or completes the work, asking a person to approve anything that matters.

Discuss your project How the work is delivered

A good fit if

  • Staff move information between email, spreadsheets, your ERP and other systems all day.
  • Requests arrive in formats too varied for rule-based automation.
  • Rules cover most cases, but exceptions need judgement and context.
  • You want supervised automation, with approvals and a clear audit trail.

What is included

  • Process map and agent design: tools, permissions, approvals, limits, fallbacks
  • Integrations: Prometheus or ERPNext, CRM, email, Microsoft 365, file storage
  • Least-privilege service accounts, secure credentials and human approval steps
  • Evaluation on real cases, with accuracy and exception rates
  • Audit logs, alerts and a dashboard: volumes, outcomes, exceptions, costs
  • Documentation, training and a runbook, including how to pause

What this service is

An agent uses a large language model to understand a goal, plan steps and use tools such as your ERP, CRM, email or document storage. A chatbot answers; an agent gets work done:

  1. ReadAn emailed purchase order arrives.
  2. CheckCustomer and stock in your ERP.
  3. PrepareA draft sales order.
  4. ApproveA person confirms it.

We treat agents like any system touching your data: designed, tested, secured, monitored and supported.

Where agents help

Good first agents run frequent, well-understood processes with clear outcomes.

Order and request intake

Emailed or form requests, checked and prepared for approval.

Finance operations

Invoice exceptions, remittance matching, follow-up drafts.

Supply chain

Lead times, delay flags, purchase suggestions in Prometheus or ERPNext.

HR and onboarding

Joiner and leaver tasks across Microsoft 365, applications and equipment.

IT operations

Ticket triage, diagnostics, routine changes for an engineer to approve.

Reporting

Figures from several systems, draft commentary, anomalies flagged.

Designed for control

Agents can misread instructions, be manipulated by malicious content or act unintentionally. Our controls:

Least privilege

Its own service identity, with only the permissions it needs.

People approve what matters

Changing records, spending money, contacting customers or deciding about an individual.

Guardrails

Prompt-injection checks; type, volume and value limits; uncertain cases go to a person.

Audit trail

Every step and tool call logged, so you see what it did and why.

A pause button

Stops at once; work continues manually, even if a power or internet outage cuts off cloud AI.

Data protection. Personal data an agent handles stays subject to the Nigeria Data Protection Act 2023 (NDPA). People have the right not to be subject to solely automated decisions that significantly affect them; where an exception applies, they can still ask for human intervention and contest it. Likely high-risk processing needs a data protection impact assessment first. Not legal advice. See AI governance and security.
Delivery

How the work is delivered

  1. Map the process

    We walk through it with its people, measure volumes and mark where people must decide.

    Output: Process map and agent opportunity.

  2. Design the agent

    Goals, tools, permissions, approvals, limits, logging and fallbacks, with a risk review.

    Output: Agent design and risk review.

  3. Build and test

    Build the agent and integrations; test historical and edge cases outside production.

    Output: Tested agent, evaluation results.

  4. Supervised pilot

    A person approves every live action; we track accuracy, exceptions and time saved.

    Output: Pilot results and tuning.

  5. Scale with oversight

    Reduce approvals only where results justify it, add volume, keep monitoring.

    Output: Live agent, runbook, monitoring.

Scope at a glance

Not included unless agreed

  • AI model, platform and cloud usage, paid to the provider
  • Third-party changes beyond supported configuration and APIs
  • Ongoing support after handover, unless agreed in writing

What we need from you

  • A process owner who explains rules, exceptions and good results
  • Representative historical cases, personal data protected as agreed
  • API access for each system, and who approves which actions

Before handover

  • Agreed accuracy and exception thresholds met on historical cases before go-live
  • Every action logged: inputs, reasoning summary, tool calls, outcome
  • Tested permissions, spend and volume limits, instant pause, manual fallback

What affects the cost

  • Steps, systems and integrations in the process
  • Input variety and number of exceptions
  • Approvals, plus security, data protection and audit needs
  • Model and cloud usage: grows with volume, usually in US dollars

No package prices: estimates in naira from an agreed scope, taxes shown separately.

Questions buyers usually ask

What is the difference between an AI agent and automation we already have?

Automation follows fixed rules on structured inputs; an agent reads unstructured requests, chooses the steps and uses several systems. We often combine both (rules in your workflow automation platform or ERP, an agent for interpretation), cutting cost and easing testing and audit.

Which platforms do you use?

Microsoft Copilot Studio, AI services from Azure, AWS and Google Cloud, open frameworks and, where data must stay with you, open-weight models on servers you control. We connect through APIs and open standards such as the Model Context Protocol, chosen by systems, security and cost.

Insight

What is agentic AI?

What AI agents are, how they differ from chatbots, where they help and the controls needed.

Have a process that eats your team's day?

Describe the process, its systems and where it gets stuck. We will discuss whether an agent, simpler automation or both would help.

Discuss an AI agent