Architecture and product flow
Define the users, system boundaries and acceptance criteria before implementation. Make the important integration and operating decisions visible.
We design and build AI applications around your business requirements. The work includes architecture, interfaces, integration, testing and an operating plan for the finished system.
Model behaviour is one part of the product. The interfaces, data flow, controls and operating responsibilities need to fit together.
Define the users, system boundaries and acceptance criteria before implementation. Make the important integration and operating decisions visible.
Build the interfaces and AI behaviour, with model choices, latency and cost controls matched to the product. Keep external connections clearly separated.
Test the agreed workflows and exceptions before release. Prepare handover guidance and define monitoring, support and future changes with your team.
Example Workflow
A build sequence tied to agreed acceptance criteria and the workflow your team needs to run.
Agree the users, workflow and evidence needed for acceptance.
Choose architecture, interfaces and data boundaries.
Implement in reviewable parts and check real behaviour.
Agree operating responsibilities and the next improvements.
A successful compilation is not evidence that a business workflow is ready. We review the agreed functional and operational checks, unresolved issues and responsibilities before release.
Explore the workflow, the controls and the decisions people retain.
Demonstration
A shared control layer runs insurance, mortgage and finance workflows through one engine. It routes tasks to model tiers, holds sensitive actions for a person and records the workflow’s decisions and results.
Receive and route
Intake identifies the workflow and routes the task to an appropriate model tier through a shared entry point.
Check and approve
Policy checks and a named human gate govern a proposed high-risk action before it can proceed.
Record and compare
The demonstration records reason codes and compares measured tokens, elapsed time and cost for the same task across model tiers.
Controls and responsibility
Policy failures are recorded too. Checks cover authority and excluded or masked information. The self-hosted comparison is labelled as a benchmark; it is not a general savings promise.
Demonstration
Prior-period records and current financial statements become a structured, formula-driven review package. AI proposes document interpretations and mappings; code validates them and produces the figures for professional approval.
Map and validate
Propose the account mappings, validate them in code and build preliminary figures for the first approval gate.
Calculate and reconcile
Software computes adjustments and builds the workpaper, source references and open-item list. Items that do not tie are flagged.
Review and export
A professional approves the prepared work before spreadsheet and import-file exports. Corrections rerun the affected stages.
Controls and responsibility
Figures come from code. Approval gates retain professional authority. Outgoing model requests are checked for excluded sensitive fields, and the administration view records model-tier cost per run.
Yes. We start by understanding the current application and its constraints, then scope the AI capability and any required changes around it.
Private, on-premise or other deployment requirements are assessed during architecture. The approach depends on your data, access and operating needs; model adaptation or fine-tuning is considered only where it fits the task.
Handover, operational support and future development are agreed for the engagement. We define who operates the system and how changes are reviewed.
Bring a workflow, a requirement or a problem your team wants to solve.