AI Agents & Automation
What we do
We build AI agent systems that reliably execute complex workflows - document processing, research synthesis, code generation, customer support triage, and multi-step decision making. Our agent architectures include proper error handling, structured output validation, human-in-the-loop checkpoints, and evaluation frameworks that ensure output quality at scale. Our team has deployed agent systems processing thousands of documents daily, handling customer inquiries with measurable resolution rates, and automating compliance workflows in regulated industries.
Intelligent Document Processing
Automated extraction, classification, summarisation, and routing of unstructured documents at enterprise scale. We combine OCR, layout analysis, and LLM-based understanding to process contracts, invoices, medical records, and regulatory filings with accuracy rates that meet or exceed human reviewers.
Workflow & Process Automation
Multi-step agent pipelines that replace manual processes while maintaining quality and auditability. We build orchestration layers using LangGraph, CrewAI, or custom frameworks that coordinate multiple specialised agents, manage state across long-running tasks, and provide clear audit trails for compliance.
RAG Systems & Knowledge Bases
Retrieval-augmented generation pipelines for question answering over proprietary knowledge bases. We implement chunking strategies, hybrid search combining BM25 and vector similarity via Pinecone or Weaviate, reranking with cross-encoders, and citation grounding that lets users verify every answer against source documents.
Custom LLM Fine-Tuning & Deployment
Domain-specific fine-tuning of open-source models using LoRA, QLoRA, or full fine-tuning for tasks where general-purpose models fall short. We handle training data curation, evaluation benchmark design, and deployment on your infrastructure for data sovereignty. Cost optimisation through model distillation and quantisation for production serving.
How we work together
Workflow Analysis & Scope Definition
We map the manual process end-to-end, interview the people who currently perform it, and identify which steps are suitable for automation and which require human judgment. We define quality thresholds, error budgets, and escalation paths for each step. This analysis identifies the highest-value automation targets and the order in which to pursue them.
Agent Architecture & Tool Design
Design of the agent pipeline - LLM selection between GPT-4, Claude, Gemini, or open-source models like Llama and Mistral based on cost, latency, and capability requirements. We define tool interfaces, memory management strategies, guardrails for content safety, and fallback strategies for edge cases. Prompt engineering is treated as software engineering with version control and regression testing.
Evaluation & Safety Testing
Systematic evaluation against diverse test cases including adversarial inputs, ambiguous instructions, and production-representative scenarios. We build evaluation harnesses that measure accuracy, hallucination rate, latency, cost per operation, and safety compliance. Models are stress-tested against failure modes before any production exposure.
Deployment & Continuous Improvement
Gradual rollout with shadow mode testing, human review sampling, and A/B comparison against manual baselines. We implement feedback loops that capture user corrections, track resolution quality over time, and trigger model updates or prompt refinements when performance degrades. Dashboards give your team visibility into agent behaviour and cost.