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S&S Labs
§01.BCapabilities / AI & Automation

Artificial Intelligence & Intelligent Automation

Put AI to work inside real processes, with controls, measurement and people in the loop.

Practical AI inside the systems and workflows you already run: assistants grounded in your own documents, document processing that turns paper into structured data, and automation that handles the routine so people can handle the judgment. We start from the process and the risk, not from the model.

An analyst comparing a printed document with what is on his laptop screen.

§1The problems it addresses

The problems it addresses

  1. P1

    Knowledge locked in documents

    Policies, contracts, reports and case files hold the answers, but finding them takes hours and depends on who you ask.

  2. P2

    Manual document handling

    Teams re-type data from forms, invoices and identity documents. It is slow, error-prone and does not scale with demand.

  3. P3

    Pilots that never reach production

    A promising demo exists, but nobody has solved accuracy, security, cost, or how it fits into the actual workflow.

  4. P4

    Concerns about data and control

    Sensitive data cannot go to just any service, and leadership needs to know how answers are produced and checked.

§1.1See it working

Every extracted value shows where it came from.

Choose a field to see where on the document it was read. Values below the confidence threshold go to an analyst instead of being accepted silently.

Choose a field in the list

Applications / LN-7731

Payslip, classified with 99% confidence

PAYSLIPExample Trading Ltd

EmployeePeriodA. Mensah · #041801/03/2026 – 31/03/2026

Basic salary2,150.00

Allowances300.00

Gross pay2,450.00

Income tax(392.10)

Pension(75.50)

Net pay1,982.40

Employer reg. ET-20▒·71 · Paid to account ••••2209

Extracted fields

1 field needs review

Net pay: “1,982.40”, read from the totals line.

Cross-checks

  • Net pay matches the salary credit on the bank statement (14 Mar)
  • Gross minus deductions equals net pay
  • Employer registration unclear: registry check waiting for review

RouteAnalyst review · 1 field

Scroll sideways to see the whole interface.

§2What we deliver

Solutions, not slogans.

  • Grounded assistants (RAG)

    Assistants that answer from your approved sources, cite them, respect access permissions and say when they don't know.

  • Document intelligence

    Classification, extraction and validation pipelines with confidence scores, and human review for the cases that need it.

  • AI steps inside governed workflows

    Triage, drafting, summarising and routing as steps in a workflow with approvals and an audit trail, not as a separate chat window.

  • Agents with guardrails

    Multi-step tasks orchestrated with tool access limited by policy, observable at every step, and escalation to a person.

  • Predictive models

    Forecasting, scoring and anomaly detection where historical data supports it, monitored for drift once deployed.

  • Controlled deployment

    Model and hosting choices matched to data sensitivity: managed APIs with data controls, private cloud, or self-hosted open models.

§3Applications

Where it applies.

  • Financial servicesExtracting and checking data from loan applications, with exceptions routed to an analyst for review.
  • Public sectorA policy assistant for front-line staff that answers from current regulations and cites the clause it relied on.
  • NGOsConsistent, searchable summaries of field reports across programmes and countries.
  • OperationsClassifying incoming requests, routing them to the right team and drafting a first response for a person to approve.

Illustrative applications, not a list of past clients.

§4Typical deliverables

Typical deliverables and engagement

  • 01Use-case assessment and risk analysis
  • 02Evaluation datasets and accuracy benchmarks
  • 03Production AI services and integrations
  • 04Retrieval pipelines and search indexes
  • 05Human-review interfaces
  • 06Monitoring for quality, cost and drift
  • 07Usage and data-handling policies

§5How the engagement works

  1. 01

    Assess

    Choose use cases by value and risk. Check that the data exists, is accessible and is good enough.

  2. 02

    Prove

    Build an evaluation set and a working prototype, and measure accuracy against it before anyone commits further.

  3. 03

    Integrate

    Put it into the real workflow with permissions, review steps, logging and monitoring.

  4. 04

    Improve

    Track quality and cost in production, and feed corrections back into the evaluation set.

§6See it worked through

See it in action, and the solutions it powers.

Services in this area

  • Enterprise AI integration
  • AI assistants and knowledge systems
  • Retrieval-augmented generation (RAG)
  • Intelligent document processing
  • AI-powered business workflows
  • Predictive analytics and machine learning
  • AI agents and orchestration
  • Private and controlled AI deployment

Next capability

Data & Analytics

Talk to us about ai & automation.

Describe the problem in a few sentences. We reply with questions, a first view of the approach, and who you would work with.

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