AgentAligned

Applied AI deployment for African operations

We work inside your team to redesign priority workflows, connect the right data and tools, and deploy governed AI systems that deliver measurable results.

Book a Bottleneck Diagnostic See how the method works

One workflow. One accountable owner. One measurable outcome.

01 / The thesis

The model is rarely the bottleneck.

AgentAligned is an applied AI engineering partner that embeds with operational teams to design, build and maintain governed AI workflows. Capable AI is widely available. The hard part is fitting it into real data, permissions, systems, decisions and day-to-day work. AgentAligned starts with the workflow, builds around the people closest to it, and measures whether the result is useful before it reaches production.

Pattern A

Tool-first pilot

  • Starts with a model or product
  • Produces an impressive demonstration
  • Leaves data, controls and ownership unresolved
  • Measures novelty rather than operating value
  • Often stalls before adoption

Pattern B

Workflow-first deployment

  • Starts with a costly, recurrent workflow
  • Establishes a measurable baseline
  • Builds around real systems and people
  • Evaluates against representative examples
  • Keeps accountable people in control

02 / Model and platform optionality

Provider-neutral by design.

We select models and tools against the workflow, data, governance and performance requirements – not vendor preference.

  • OpenAIChatGPT
  • AnthropicClaude
  • GoogleGemini
  • MicrosoftCopilot
  • DeepSeek
  • xAIGrok
  • MetaLlama
  • Mistral AI

Platforms shown indicate technical compatibility and model optionality. They do not imply partnership, certification or endorsement.

03 / Method

From bottleneck to production.

A focused engagement moves through three stages. The exact scope depends on the workflow, data and risk.

One workflow. One accountable owner. One measurable outcome.

Explore the full method
  1. Bottleneck Diagnostic 1–2 weeks

    Diagnose

    Map one recurrent workflow, agree its baseline and identify the data, integration, security and adoption constraints.

    OutcomeA feasibility decision and prioritised build charter.

  2. Build Sprint 4–6 weeks for a focused first workflow

    Build and evaluate

    Build the smallest production-worthy system, connect the required tools and test it against representative examples and agreed acceptance thresholds.

    OutcomeWorking code, evaluation results and a launch decision.

  3. Sustaining Production Ongoing where required

    Run and improve

    Monitor quality, latency, cost and adoption, investigate drift, maintain integrations and improve the workflow against agreed measures.

    OutcomeA maintained production system and visible improvement backlog.

04 / Fit

Is this a strong first workflow?

We start where operating value can be measured and responsibility is clear.

Strong fit

Ready to diagnose

  • A recurrent, costly or error-prone workflow
  • An accountable business owner
  • Access to subject-matter experts
  • Representative examples or data
  • A measurable baseline and desired outcome
  • A willingness to change the process, not only add a tool

Not ready yet

Worth pausing first

  • No accountable workflow owner
  • A request for a generic AI tool recommendation
  • No access to representative data or systems
  • No measurable operating outcome
  • A company-wide transformation before proving one workflow
  • Headcount reduction as the only stated objective
Test a workflow for fit

05 / Where to start

Start where work gets stuck.

The best first use case is not the most futuristic. It is a frequent, expensive workflow with clear inputs, accountable owners and a result that can be checked.

01

FMCG and manufacturing

Problem
Teams read supplier emails and attachments, re-key details and chase mismatches across orders, deliveries and invoices.
System
An AI-powered workflow extracts approved fields, checks them against source records, proposes updates and routes exceptions.
Human control
People approve exceptions and high-risk actions before anything changes in the system of record.
Measure
Processing time, exception rate, rework and error rate.

02

Port logistics and supply chain

Problem
Teams cross-check cargo manifests, bills of lading, customs files, invoices and status messages across inconsistent formats.
System
A document workflow extracts relevant fields, compares records, identifies discrepancies and assembles an exception queue with source references.
Human control
Authorised staff resolve discrepancies and approve submissions.
Measure
Reconciliation time, unresolved exceptions, rework and missed-document rate.

03

Finance and shared services

Problem
Finance teams repeatedly match invoices, orders, approvals and supporting documents, then spend time finding the owner of each exception.
System
An AI-powered workflow reads the pack, checks the relevant records, prepares a recommendation and routes exceptions with source evidence.
Human control
Authorised staff approve payments and policy exceptions.
Measure
Touch time, cycle time, exception age and avoidable rework.
See all use cases

06 / Governance

Governance is part of the build.

Data residency is one design choice, not the whole of compliance. We map the data, minimise what the system processes, define access and retention, document third parties, test failure cases and keep accountable people in the loop.

  • G1Data mapping and minimisationKnow what enters the system, where it goes and what must be retained.
  • G2Role-based accessRestrict systems, records and actions according to authorised roles.
  • G3Deployment and residency optionsMatch hosting and model routing to the client’s operational and data constraints.
  • G4Task-specific evaluationsTest representative examples against agreed quality and risk thresholds.
  • G5Human approval and exception handlingKeep accountable people in control of sensitive actions and exceptions.
  • G6Monitoring, audit logs and incident responseRecord system behaviour, investigate failures and support an accountable response.

Executive ownership. Frontline expertise. Working systems.

Where a use case requires South African data residency, we can assess local hosting and inference options. The final architecture depends on the client's data, systems, risk requirements and legal review. Read our POPIA and sovereign AI guidance.

07 / Measurement

Agree the measure before the build.

Every sprint begins with a baseline and an acceptance threshold. Depending on the workflow, that may include cycle time, exception rate, error rate, cost per case, response time, adoption or the percentage of work that still needs manual handling.

Measurement framework Set per engagement
  • Cycle time
  • Exception rate
  • Error rate
  • Cost per case
  • Response time
  • Adoption
  • Work still handled manually
  • Baseline and acceptance threshold
This is the measurement framework used to set up an engagement. It is not a record of client results.

08 / What remains

What the engagement leaves behind.

Every engagement should create reusable operating capability, not only a presentation or isolated prototype.

  • D1Working code and configuration
  • D2Evaluation set and acceptance thresholds
  • D3Data, risk and POPIA control map
  • D4Operating documentation
  • D5Team training and handover
  • D6Monitoring plan and improvement backlog
Explore the full method

09 / Questions

Straight answers.

What is a Forward Deployed Engineer?

A Forward Deployed Engineer is a hands-on software engineer who works closely with a client's operators and technology teams. Instead of stopping at a strategy or prototype, they connect models to real data, tools, controls and workflows, then help the system work reliably in day-to-day operations.

What does AgentAligned build?

We build focused AI-powered workflows such as document reconciliation, inbox-to-system processing, internal knowledge tools, exception triage and operational decision support. The right model and architecture are selected for the job rather than forced in advance.

How do you reduce unreliable AI outputs?

We define the task, create a representative test set, agree acceptable performance, test known failure cases and add human review where the consequence of an error is high. Production monitoring then shows whether quality changes over time. No responsible team can promise to eliminate every error.

How do you approach POPIA?

We design around the relevant POPIA requirements, including purpose, minimality, access, retention, security safeguards, operators and cross-border transfers. South African data residency can be considered when it reduces risk or meets a client requirement, but hosting location alone does not establish compliance. The client's Information Officer and legal advisers remain part of the approval process.

Can ESD funding support an AI deployment?

Potentially. A corporate sponsor may be able to structure an Enterprise or Supplier Development initiative for an eligible beneficiary. Recognition depends on the applicable B-BBEE code, beneficiary status, programme design, evidence, valuation and independent verification. We start with an eligibility and programme-fit check.

Is the service limited to one AI provider?

No. We select models, hosting and tools around the task, data, quality, security, latency and cost requirements. A client may use a frontier API, a South African-hosted model, a private environment or a combination.

Next step

Show us where the work gets stuck.

Bring one recurring workflow, the people who know it and any data or system constraints. We will help determine whether it is worth a diagnostic.

Email hello@agentaligned.agency