// Systems we build
Six types of AI systems.
Each solves a specific class of problem.
What it does
Read, extract, classify, and summarise documents — contracts, reports, correspondence — without manual effort.
When you need it
You have large volumes of documents that people are reading manually and summarising by hand.
How it's built
LLM extraction pipelines with fine-tuned classification models, structured output APIs, and human review interfaces integrated into existing document workflows.
Typical stack
OpenAI
FastAPI
PostgreSQL
React
AWS// Production stack
What runs in production.
Organised by system layer.
Every layer has a job. We select tools per layer based on the system's requirements — not vendor preference or familiarity.
OpenAI
Anthropic
Google
Meta
RedisPostgreSQL
MongoDB
ElasticsearchAWS
Azure
GCP
Kubernetes
Docker
Terraform
Datadog
Grafana
Snowflake
Databricks
dbt
AirflowPostgreSQL// Implementation framework
From problem definition
to deployed system.
Every stage has defined deliverables and a defined client role. We have done this enough times to know what happens at each step — and what happens when it doesn't.
We define the problem precisely — not the AI solution. We analyse available data, map existing workflows, and establish whether the proposed system is technically feasible and operationally viable before any engineering begins.
- ▸Problem definition document
- ▸Data quality assessment
- ▸Feasibility verdict
- ▸Architecture options paper
- ▸Go/no-go recommendation
2–3 process owners, data samples, access to existing workflow documentation
We design the complete system before writing a line of model code — integration architecture, data flows, API design, access control, security posture, and observability instrumentation. Integration decisions come before model decisions.
- ▸System architecture document
- ▸Integration specification
- ▸Data pipeline design
- ▸Security architecture
- ▸Infrastructure cost model
IT/infrastructure contacts, API documentation for existing systems, compliance requirements
Engineering sprints with weekly working demonstrations on real data — not slides, not prototypes. Every sprint produces deployable code. Monitoring, logging, and error handling are built from sprint one, not retrofitted at the end.
- ▸Working system increments weekly
- ▸Test results against real data
- ▸Integration endpoints
- ▸Observability dashboard
- ▸Documentation
Weekly review participation, feedback on working system, access to test data
Controlled deployment into the production environment with staged rollout — starting with a defined subset of traffic or documents before full scale. Integration testing against live systems. Performance baseline established before go-live.
- ▸Production deployment
- ▸Integration test results
- ▸Performance baseline report
- ▸Runbook for operations
- ▸Team training sessions
Infrastructure access, user group for staged rollout, internal comms support
Post-deployment monitoring for latency, accuracy, and model drift. The system measures itself. We establish SLAs, review performance quarterly, and operate within agreed parameters — handling model updates, provider changes, and data shifts as they occur.
- ▸Ongoing SLA
- ▸Monthly performance reports
- ▸Drift alerts and remediation
- ▸Model update management
- ▸Quarterly review sessions
Designated internal owner, incident escalation contact, periodic data access for retraining
// Deployed systems
Systems in production.
Problems solved. Numbers real.
The problem
A top-50 UK law firm had paralegals spending 4+ hours per matter reading, flagging, and summarising contract clauses. At 80+ active matters monthly, this was consuming junior fee-earner capacity that should have been on client work.
The system
We built a document intelligence platform that ingests contracts, extracts and classifies clauses by type and risk level, generates structured matter summaries, and presents a human review interface for sign-off. The system integrates directly into their existing DMS — no parallel workflow, no new login.
OpenAIFastAPIReactAWSPostgreSQLIntegrated into existing NetDocuments DMS via REST API. No change to existing file management workflow.
The outcome
Review time dropped from 4+ hours to under 45 minutes. The platform processes 300+ documents per day across 6 practice groups.
The problem
A FTSE 100 payments operator was processing 50M+ daily transactions with a rules-based fraud engine that generated high false-positive rates — blocking legitimate transactions and requiring manual review queues staffed around the clock.
The system
We built a real-time ML scoring system that runs alongside the existing rules engine, adding a probabilistic fraud score to every transaction at sub-100ms latency. The model is trained on 18 months of labelled transaction history and retrained monthly as fraud patterns shift.
KafkaRedis
PythonKubernetesDockerDeployed as a sidecar scoring service integrated into the existing transaction processing pipeline via Kafka event stream. No changes to core payment infrastructure.
The outcome
False positive rate reduced by 40%, eliminating the manual review backlog. The model runs 24/7 across all transaction channels.
// Where we've deployed
Named systems. Specific sectors.
We do not list sectors to signal breadth. We list the specific systems we have built and deployed in each sector.
Legal Services
- ▸Contract review and clause extraction platforms
- ▸Due diligence document intelligence systems
- ▸Compliance monitoring and exception reporting pipelines
- ▸Matter summarisation and knowledge retrieval systems
Financial Services
- ▸Real-time fraud scoring and detection systems
- ▸Automated KYC document processing pipelines
- ▸Compliance workflow automation and audit trail systems
- ▸Risk classification and reporting platforms
Media & Broadcast
- ▸Rights management and workflow orchestration platforms
- ▸Content tagging, metadata extraction, and search systems
- ▸AI-powered media asset management integrations
- ▸Production schedule automation systems
Professional Services
- ▸Enterprise knowledge platforms for research teams
- ▸Meeting summarisation and action extraction systems
- ▸Proposal generation and document assembly tools
- ▸Client-facing AI assistants with source attribution
// How we build
Four engineering principles.
Not brand claims.
We build for production, not proof-of-concept
Every sprint produces deployable code. Monitoring, logging, error handling, and fallback paths are built from sprint one — not retrofitted at handover.
When we finish, you have a live system. Not a prototype that needs six months of productionisation work before it can be used.
Integration architecture before model selection
We design how the system connects to your existing infrastructure — APIs, data flows, authentication, access control — before selecting or fine-tuning any model. A system that doesn't integrate cleanly doesn't get used.
AI that fits inside your existing operations. Not a parallel system your team has to manage separately on top of everything else they do.
Every system is observed and measured
We instrument every deployed system with latency tracking, output quality monitoring, and drift detection from day one. LangSmith for LLM observability. MLflow for model performance. Established baselines before go-live.
You always know how your system is performing — because it measures itself. You don't need to ask us whether it's working.
One team. Same engineers. Full lifecycle.
The engineers who design the architecture write the code, build the deployment pipeline, and operate the system post-launch. No context loss between design, build, and deployment phases.
One team accountable from brief to production and beyond. No handoffs where requirements get reinterpreted, no gaps where knowledge gets lost.
// Engineering questions
Questions only answered well by teams who have actually operated AI systems in production.
These are the questions technical buyers ask at due diligence. We answer them now.