What we build, by business problem

Twelve systems we design and build. These are designs, not a client list. Your version will depend on your data, your process and your risk limits.

Customer service and sales

Faster answers, with a person for the hard ones.

  • Customer support agent

    Looks up the order, applies your policy, resolves what is allowed and escalates the rest.

    Data it needs
    Help articles, order and ticket history
    The guardrail
    Fixed limits on refunds, with a handover to your team.
    Agentic AI
  • Quote drafts from emailed requests

    An inbound email becomes a draft quote using current products and prices.

    Data it needs
    Product catalogue, price lists, past quotes
    The guardrail
    Prices come from your system, never from the model.
    Generative AIERP
  • Churn warnings inside the CRM

    A score based on orders and tickets creates a follow-up for the account owner.

    Data it needs
    Orders, tickets and activity history
    The guardrail
    Each score lists the signals behind it.
    Machine learning

Finance

Less retyping and fewer surprises at month end.

  • Supplier bills read into draft entries

    A model extracts vendor, amounts and tax lines into draft bills for review.

    Data it needs
    Past bills and your chart of accounts
    The guardrail
    Low-confidence fields are highlighted, and nothing posts unreviewed.
    Generative AIERP
  • An agent that chases overdue invoices

    It sends reminders within your rules, and passes disputes to a person.

    Data it needs
    Receivables, payment history, contacts
    The guardrail
    Fixed limits on who it contacts and how often.
    Agentic AIERP
  • Invoice anomaly detection

    A model flags duplicates and unusual amounts for a reviewer’s queue.

    Data it needs
    Journal entries and vendor history
    The guardrail
    It only flags. People decide, and their decisions improve the model.
    Machine learning

Supply chain and operations

Better plans from the history you already have.

  • Demand forecasts that feed purchasing

    Forecasts per product and warehouse become suggested reorder quantities.

    Data it needs
    Sales, stock and purchase history
    The guardrail
    Planners accept or change every suggestion at first.
    Machine learningERP
  • Failure prediction for machines

    Sensor readings and maintenance history produce a risk score that raises a work order.

    Data it needs
    Sensor data and past work orders
    The guardrail
    Maintenance staff keep the final decision.
    Machine learning
  • A purchasing agent that drafts orders

    It checks stock and forecasts, compares approved vendors and prepares a draft order.

    Data it needs
    Stock levels, vendor lists, price agreements
    The guardrail
    Only a buyer can confirm an order.
    Agentic AIERP

Knowledge and core systems

The foundations everything else stands on.

  • Company search with citations

    Staff ask questions across policies, manuals and records, and get sourced answers.

    Data it needs
    Documents and access rights
    The guardrail
    It respects permissions and says when it doesn’t know.
    Generative AI
  • Shift notes turned into searchable reports

    Free-text or spoken notes become structured reports tied to machine and batch.

    Data it needs
    Shift notes and machine records
    The guardrail
    The original note stays attached for checking.
    Generative AI
  • Legacy ERP migration in phases

    Finance, then supply chain and production move in phases, with data rehearsals.

    Data it needs
    Master data, open items and a custom code inventory
    The guardrail
    Each cutover has a tested rollback.
    ERP

Don’t see your problem here? Tell us about it.

These twelve are starting points. Most projects turn into something more specific.

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