AI & Machine Learning
Machine learning is useful when a decision is repeated often and the pattern is buried in data you already hold. We build models and assistants that support specific decisions, integrate them with the systems people use, and keep a person in the loop where judgement matters.
Who it helps
Where ai & machine learning tends to fit
If more than one of these sounds familiar, the solution is likely worth a conversation.
- Organisations making the same forecast, review or approval decision hundreds of times a month.
- Teams holding years of records in accounting, practice management, CRM or operational systems that nobody has time to analyse.
- Businesses processing high volumes of invoices, forms, statements or contracts by hand.
- Managers who need answers from internal documents without reading every file.
Business problems
The problems this is usually brought in to solve
Numbers arrive too late to act on
Reports are assembled monthly from exports, so problems are visible weeks after the decision that caused them.
Documents are keyed in by hand
Invoices, application forms and statements are read and typed by staff, which is slow and produces inconsistencies between people.
Exceptions hide inside volume
Unusual transactions, duplicate payments or out of pattern orders only get noticed when somebody happens to look closely.
Services and capabilities
Core capabilities and optional additions
Core capabilities are part of a typical build. Optional additions are included only when the agreed scope calls for them. What you receive in the end depends on the scope we agree together, so treat this as the menu rather than the order.
Core capabilities
- Business process automation for repetitive, rules based steps
- Predictive analytics and forecasting on your historical data
- Anomaly detection to flag records that sit outside normal patterns
- Document processing to read invoices, forms and statements into structured fields
- AI assistants that answer questions using your own documents
- Knowledge search across shared files, procedures and correspondence
- Decision support that surfaces a recommendation with the evidence behind it
- Data preparation, including cleaning, matching and joining sources
- Model evaluation against a held out sample so accuracy is measured, not assumed
- Human review steps for anything that affects money, safety or compliance
- Operational monitoring so you can see when a model drifts
Optional additions
- Scheduled retraining as new records accumulate
- Self service dashboards for the people who use the output
- Multi language document handling where your paperwork requires it
- Additional review queues for seasonal peaks in volume
Anything listed as optional is quoted separately and only built if you decide it is worth having.
What an engagement can include
How a scoped project is put together
Not every project needs every step. This is the shape a typical engagement takes, and the scope you agree selects the parts that apply.
- 1Discovery workshop to identify the decisions worth supporting and the data available for them
- 2Data review and preparation plan, including known gaps and quality issues
- 3Baseline measurement, so improvement can be compared against how the process performs today
- 4Model or assistant build, with reviewable outputs shown to your team as work progresses
- 5Integration with the systems where the decision is actually made
- 6Human review and escalation design
- 7Monitoring, documentation and handover
Typical workflows
How it works day to day
These are the workflows this solution usually supports. Yours will be shaped during discovery.
Document processing
- 1A document arrives by email, upload or shared folder
- 2Text and key fields are extracted and matched to a supplier or customer record
- 3Confidence is scored and low confidence items are routed to a person
- 4Approved records are written to your finance or operational system
Forecasting and review
- 1Historical records are assembled and prepared
- 2A forecast or expected range is produced for each period, product or site
- 3Figures that fall outside the expected range are flagged for review
- 4A manager accepts, adjusts or rejects the recommendation, and the decision is recorded
Integration options
What it can connect to
Every system also has to allow the connection in the first place. Where a platform does not offer a supported way to integrate, we will tell you during discovery rather than work around it quietly.
Accounting platforms
Chart of accounts, transactions, contacts and invoices, read through an API or scheduled export.
Business systems with an API
Operational records read and written back so the output appears where the work happens.
Document stores and shared drives
Source documents and templates, indexed so assistants can search them securely.
Data warehouses and reporting tools
Prepared datasets published for existing dashboards rather than creating a second reporting stack.
Identity provider
Sign in and permissions inherited from your existing directory, so access follows your staff changes.
Delivery and handover
What you receive, and what stays with you
- Written scope that states what is included, what is deferred and how accuracy will be judged
- Model and data documentation covering inputs, assumptions and known limitations
- Handover session with the people who will review or act on the output
- Runbook describing how to monitor, retrain and troubleshoot the solution
Security and operations
Security and operational considerations
These are the controls and habits that come with the solution. The detail is agreed for your environment during scoping.
- Data minimisation, using only the fields a decision actually needs
- Access controls that follow your existing groups rather than a separate permission list
- Sensitive records kept within your agreed environment, with retention stated before work begins
- Audit logging of automated decisions and the reviews that followed them
- Clear boundaries for what is advisory and what is allowed to act automatically
Questions
AI & Machine Learning questions, answered
It depends on the decision. Some problems work with a few thousand structured records, others need far more history. During discovery we look at what you hold and tell you honestly whether there is enough signal for a useful model.
Only where you decide that is appropriate. Most of the work we do keeps a person in the loop for anything affecting money, safety or compliance, with automation handling the preparation and routing around them.
It stays in the environment agreed at the start of the project, and we use the minimum fields the decision requires. Retention and access are written into the scope before any work begins.
We measure against a held out sample of your own records and compare the result with how the process performs today. You see the measured outcome and its limitations rather than a general claim of accuracy.
Usually, if the documents are consistent enough to train on. We test against a representative set of your real documents and report extraction rates per field, then route anything low confidence to a person.
Related solutions
Often considered alongside this
- Custom Web ApplicationsCustomer portals, staff systems, dashboards and workflow applications built to match how your organisation actually operates.View solution
- Managed Database & DBA ServicesDatabase administration, performance tuning, backup verification, recovery testing, migrations and availability planning.View solution
- Financial Technology SolutionsFinance workflows, reconciliation, approvals, audit trails and cash flow reporting built on the systems your finance team uses.View solution
Next step
Discuss your ai & machine learning requirements
Tell us about your current systems, the people involved and what you need the outcome to be. We will suggest a scope, flag anything that needs checking first, and explain what the work involves.