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AI Solutions

AI document processing and intelligent automation

AI can read documents, answer from internal information, forecast demand and lift routine work. We choose use cases where it adds value, build them into your systems and keep people in control.

Discuss your project How the work is delivered

A good fit if

  • Staff spend hours typing invoices, forms or contracts into another system.
  • Answers are buried in policies, procedures, manuals and past project files.
  • You want forecasts of demand, cash flow, maintenance or churn from your own data.
  • Emailed requests in many formats are sorted, routed and summarised by hand.

What is included

  • Opportunity assessment scoring value, feasibility, data readiness and risk
  • Data handling design: data used, processing, storage and access
  • Document extraction into ERP, CRM or workflows, with confidence checks
  • Knowledge assistants that cite sources and respect permissions
  • Forecasting or risk-scoring models, plus request classification, routing, summaries
  • Evaluation plan, staff AI policy if needed, documentation and runbook

What this service is

AI answers plain-language questions from your own documents, as people now expect from ChatGPT, and takes on reading, sorting and summarising. It is one tool among many: a workflow automation rule or data analytics report sometimes suits better. Where AI adds value, we deliver it governed, not as scattered experiments.

What we build

Document processing and OCR

Invoices, purchase orders, forms, claims and contracts, checked against your ERP or CRM.

Knowledge assistants

Answers from policies, procedures, manuals and project files, citing sources. See generative AI.

Predictive analytics

Demand, cash flow, stock, maintenance or churn forecasts, tested before use.

Back-office automation

Route requests, summarise threads and case notes, draft replies for approval, match transactions.

All connect to Prometheus and ERPNext, Salesforce, HubSpot, NetSuite and Microsoft 365 through system integration. Start with a high-volume task where errors are easy to catch, such as supplier invoices or an approved policy library; early success builds confidence. Unsure? Try an AI readiness assessment.

Platforms we use

Chosen by your data, security requirements and existing licences: Microsoft Azure AI services and Copilot Studio, Amazon Bedrock and Textract, Google Gemini Enterprise Agent Platform (formerly Vertex AI) and Document AI, and open-weight models hosted on servers you control. Naming a product does not mean we are a partner of its vendor.

Responsible AI in Nigeria

Data protection law

The NDPA 2023, enforced by the NDPC: lawful basis, limits on transfers abroad, rights over solely automated decisions.

Human oversight

People review and can override outputs affecting individuals, money or compliance. No fully automated decisions about people.

Data handling

Documented use, location, retention and access; minimal personal data to models. Users know what is AI.

AI use policy

If staff use AI: approved tools, permitted data, review and disclosure, for your legal and HR advisers.

Not legal advice. The NDPC's General Application and Implementation Directive (GAID) 2025 sets out how the NDPA applies. Banks, insurers, pension operators, public bodies and other regulated organisations may face more from their sector regulators. Your legal and data protection advisers should confirm what applies.
Delivery

How the work is delivered

  1. Assess opportunities

    With the people doing the work, score use cases on value, data readiness, risk and cost.

    Output: Prioritised use cases, recommended start.

  2. Prove the concept

    Test it on your own data, in a controlled environment, against agreed accuracy thresholds.

    Output: Results and a go or no-go.

  3. Design for production

    Data handling, permissions, review points, fallbacks, logging and system integration.

    Output: Solution design and governance notes.

  4. Build and pilot

    Build into your workflows; pilot with real users, tracking accuracy, exceptions and feedback.

    Output: Piloted solution, evaluation report.

  5. Launch and monitor

    Roll out, train users, track accuracy and costs; issues reach a named owner.

    Output: Live solution, runbook, monitoring.

Scope at a glance

Not included unless agreed

  • AI platform, model and cloud subscriptions, bought from the vendor
  • Legal advice on data protection, employment or sector regulation of AI
  • Ongoing tuning and support after handover, unless agreed in writing

What we need from you

  • A business owner per use case who judges output quality
  • Representative documents or data, personal data removed or protected
  • Data protection officer input, security and data location needs, system access

Before handover

  • Written accuracy thresholds tested; low-confidence results go to a person
  • Prompts, outputs and actions logged under your retention and data protection rules
  • Manual fallback if AI is off; accuracy, usage and costs monitored

What affects the cost

  • Number and complexity of use cases
  • Volume, variety and quality of documents or data
  • Integrations, accuracy targets and evaluation effort
  • Data protection, security, data location; usage, often in US dollars

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

Questions buyers usually ask

Is our data used to train public AI models?

It should not be. We use enterprise AI services whose terms exclude customer data from training, or models hosted on servers you control, and confirm each vendor's data terms with you before real data is used.

Can the AI run in Nigeria?

Self-hosted models can run in a local data centre; large hosted AI services run abroad, as Nigeria has no major public cloud region. Where data location matters we choose to suit, check NDPA transfer rules and document where data is processed.

How accurate will it be?

Accuracy depends on the task and your data, so we measure rather than promise. The proof of concept tests your own examples against thresholds you agree, and uncertain results go to a person.

Wondering where AI fits in your organisation?

Describe the work you want AI to help with and the systems involved. We will discuss whether it fits and how to test it safely.

Discuss an AI use case