Document & Evidence Summarisation
Structured summaries for risk, compliance, audit, case handling, and operational documentation.
GenAI systems built for clarity, reliability, and context.
Context-aware generative workflows that enhance decision-making, improve throughput, and remain fully traceable and auditable.
GenAI is not "creative AI." It is structured interpretation.
In regulated or high-consequence environments, GenAI's value does not come from creativity. It comes from its ability to produce consistent interpretation, reduce cognitive load, and turn unstructured information into reliable, structured outputs.
We design GenAI systems that:
- ✓make reasoning steps observable
- ✓output consistent, validated structures
- ✓reduce decision ambiguity
- ✓interact cleanly with your existing systems
- ✓perform predictably under load
- ✓expose explainability and guardrails by design
GenAI is most effective when treated as a reasoning engine, not a general-purpose assistant.
Primary Use Cases
GenAI excels at interpretation and reasoning workflows — distinct from execution-focused agentic AI and interaction-focused conversational AI.
Classification & Decision Support Signals
Assign categories, risk levels, flags, or recommended actions — consistently and quickly.
Structured Data Extraction
Convert unstructured text into validated domain objects, entities, and fields.
Narrative Generation for High-Consequence Processes
Drafts for regulatory submissions, operational case notes, and compliance narratives.
Prioritisation & Triage Assistance
Rank events, alerts, or cases based on context, pattern signals, and rules.
Interpretation of Multi-Document Inputs
Synthesise across multiple files, users, or data sources — something agentic AI is not optimised for.
How We Engineer GenAI Systems
LLM-centric engineering focused on structured reasoning, not agent orchestration. Every component is designed for predictability and operational integrity.
1
Domain & Data Conditioning
We shape the input space: documents, records, business rules, and constraints. The model never chooses its own context — we control it deterministically.
2
Structured Prompt & Output Contract Design
We design output schemas that enforce format correctness: JSON structures, domain object shapes, strict field validation, enumerations & constraints.
3
Reasoning Path Engineering
We build chain-of-thought, multi-step reasoning flows with: verification loops, reranking logic, consistency checks, fallback flows, rule-based tripwires.
4
Evaluation, Drift Detection & Runtime Integrity
Every GenAI system includes: automated scenario benchmarking, hallucination stress tests, regression prompts, telemetry dashboards, cost & latency monitors.
This is why our GenAI solutions survive in real operations — not just labs.
Architectural Patterns We Use
Retrieval-Augmented Generation (RAG)
Domain-constrained retrieval pipelines ensure context is factual, relevant, and audit-ready.
Verification & Consistency Checking
Each output is validated through schema enforcement, rule checks, and re-evaluation when needed.
Hybrid Reasoning Pipelines
Combining LLM reasoning with deterministic rules, scoring functions, or business logic.
How GenAI differs from Agentic & Conversational AI
| Capability | GenAI | Agentic AI | Conversational AI |
|---|---|---|---|
| Purpose | Interpretation & reasoning | Multi-step execution & orchestration | Interaction & dialogue |
| Strengths | Structure, clarity, summaries, classification | Automation, tool-use, workflows | Human-aligned interaction |
| Best For | Documents, evidence, policies, signals | Operational tasks, coordination | Support, guidance, information |
| Output Type | Structured objects | Actions & tool calls | Conversations & responses |
GenAI is the reasoning engine.
Agentic AI is the execution engine.
Conversational AI is the interaction layer.
Outcomes for the Organisation
Clarity where ambiguity existed
Summaries, classifications, and extractions become consistent across teams.
Lower operational friction
Processes accelerate because reasoning steps are automated.
Higher quality decisions
Improved signal-to-noise ratio in complex workflows.
Predictable AI behaviour
Tested, measured, observable — not opaque.
Relevant Expertise
GenAI engineering draws on deep expertise in language models, domain design, and AI-native development practices.
Relevant Accelerators
GenAI-specific tools and components that ensure quality, security, and operational readiness.
Case Studies
GenAI systems delivering structured reasoning and operational clarity in high-consequence environments.
Evidence Extraction for Regulatory Narratives
Building structured reasoning systems that extract evidence from complex regulatory documents, reducing manual review cycles and maintaining compliance standards.
Screening Modernisation
Implementing GenAI-powered classification and risk signal generation for transaction screening and sanctions compliance workflows.
Credit Decisioning System
Deploying interpretable GenAI reasoning to augment credit assessment workflows with explainable risk classifications and supporting evidence.