Financial Services
Systems built for environments where auditability, resilience and regulatory reporting are the design constraints.
Service
AI & Automation
We build AI and automation into existing operations: document processing, customer support, forecasting and the internal workflows that quietly consume the most hours.
01Overview
What this actually involves
Most AI initiatives stall at the pilot. The demonstration works, and then it meets real data, real edge cases and a team that has no way to correct it when it is wrong.
We work backwards from a process with a measurable cost. Before any model is chosen, we establish what accuracy is good enough, what happens when the system is uncertain, and who reviews the output. Those answers determine the architecture — and sometimes they determine that rules beat a model entirely.
Everything we build assumes a human in the loop where the cost of being wrong is real, with evaluation and monitoring so quality is a metric you can watch rather than a claim we made at handover.
02Capabilities
What we deliver
Document & data extraction
Invoices, contracts, claims and forms turned into structured data with confidence scoring and review queues.
Retrieval-augmented assistants
Internal assistants grounded in your own documentation, with citations and access control that respects existing permissions.
Process automation
Workflow automation across existing systems, replacing the manual copy-and-paste between them.
Forecasting & anomaly detection
Demand, capacity and quality models trained on your data, deployed with monitoring for drift.
03Business benefits
What you get from it
Hours back, measured
We baseline the process before automating it, so the saving is a number rather than an impression.
Consistency at volume
The thousandth document is handled exactly like the first, including at the end of a long shift.
Confidence you can act on
Uncertain cases route to a person instead of being guessed at silently — which is what makes the automated cases trustworthy.
No dependence on one vendor
Model access sits behind an abstraction, so a provider change is a configuration decision rather than a rebuild.
04Technology
What we build it with
Tools chosen for this practice, and why they earn their place.
- Backend01
Python
Data pipelines, ML services and automation where library depth wins.
- Data01
PostgreSQL
The default relational store. Correct, extensible, and boring in the best way.
- AI06
OpenAI
Language capability where quality per token justifies the dependency.
Anthropic
Long-context reasoning and tool use in agentic workflows.
LangChain
Orchestration scaffolding for retrieval and multi-step agents.
pgvector
Vector search without operating a second database.
PyTorch
Custom model training where an off-the-shelf API will not do.
MLflow
Experiment tracking and model registry, so results are reproducible.
05Process
How the engagement runs
Process selection
Candidate workflows scored on volume, cost, error tolerance and data availability. Not everything should be automated.
Baseline & success criteria
Current accuracy, throughput and cost measured, and the threshold for going live agreed in writing.
Prototype & evaluation
Built against a held-out set from your real data, with an evaluation harness that survives past the pilot.
Production integration
Wired into existing systems with review queues, audit logging and fallback behaviour for outages.
Monitor & improve
Drift detection, human corrections fed back, and periodic re-evaluation against the original baseline.
06Industries
All industriesWhere we apply it
Healthcare
Clinical and administrative systems built to the standards the sector requires — interoperable, auditable and designed for clinical workflow.
Logistics & Supply Chain
Visibility and control across the movement of goods — from warehouse operations to last-mile delivery.
Manufacturing
Connecting the plant floor to the systems that plan it — production data, quality and maintenance in one operational picture.
Want this scoped against your systems rather than described?
Book a technical assessment08Questions
What clients ask before starting
Next step
Thinking about ai & automation?
Start with a fixed-price technical assessment. You get an architecture decision record, a delivery plan and a real estimate — and you are free to take them elsewhere.