AI agents, RAG & automation

Where AI fits into your business

We develop AI capabilities inside business applications: document search, internal copilots, customer-support assistance, document processing and connected workflows. The starting point is a specific task, the available data and a result that can be evaluated.

An assistant answers or drafts. A workflow follows defined steps. An agent can select tools and decide the next step within a bounded task. We select the appropriate level of autonomy for each use case.

• Internal knowledge and onboarding
• Support triage and response preparation
• Extraction from business documents
• Research and account summaries for sales teams

RAG: answers grounded in your documents

Retrieval-augmented generation connects a language model to a search system. When a person asks a question, the application retrieves relevant passages from authorised sources and supplies them as context for a sourced answer.

The implementation includes document ingestion, parsing, segmentation, metadata, an index and retrieval. Depending on the corpus, we can combine keyword and vector search, rerank results and filter by permissions. Updates and deletion of source documents must also be reflected in the index.

The assistant cites the passages it uses and can state that the available evidence is insufficient. Retrieval improves grounding; it does not eliminate incorrect answers. We test retrieval and answer quality separately.

• PDF, knowledge bases and approved business sources
• Ingestion and document refresh pipeline
• Metadata and permission-aware retrieval
• Sourced answers and an evaluation question set

Agents that work with your tools

We design agents for clearly defined tasks: inspect a record, retrieve missing information, prepare a response or propose an update to an application. Tool calls use documented inputs and outputs, with the application enforcing permissions.

The design defines which actions can run automatically, which require approval, and when execution must stop or escalate to a person. Budgets, timeouts and execution logs keep the task reviewable. Several specialised agents are an option when a simpler workflow is insufficient.

• Tool calling and structured outputs
• Task state, bounded memory and checkpoints
• Human approval for consequential actions
• Execution traces, retry limits and fallback

Connect AI to the systems you already use

An AI capability becomes useful when it is available where the work happens. We integrate it into web applications, customer portals, CRM systems and workflow tools through APIs, webhooks and, where appropriate, MCP interfaces.

For example, an incoming request can be classified, enriched with authorised customer context and turned into a draft ticket. The team checks the proposed action before a connector writes to the destination system. We handle duplicate events, failures and handover to a person.

• Custom API and webhook connectors
• n8n orchestration and scheduled jobs
• Identity, credentials and scoped permissions
• Idempotency, retries and operational alerts

From documents to structured information

We can build pipelines that extract fields, classify documents, summarise records and prepare content for review. OCR may be needed for scans; structured output is checked against a schema and business rules before it is accepted.

Use cases include routing support requests, preparing summaries of a customer dossier and extracting information from forms or supplier documents. Ambiguous records enter a review queue rather than silently becoming authoritative data.

Access, data boundaries and human control

Before implementation we identify allowed sources, user roles, sensitive fields, retention needs and the permitted model-hosting options. Access rules apply to retrieval and tool execution, not just to instructions sent to the model.

Documents and tool responses are treated as untrusted input. We separate their content from application instructions, minimise exposed credentials and test scenarios such as malicious instructions in a document or an attempted cross-user lookup. Hosting and compliance requirements are reviewed for each project.

Evaluate quality, time and cost

A representative evaluation set is prepared with your team: common tasks, difficult questions, missing evidence, outdated sources and permission boundaries. We measure retrieval relevance, grounded answers, task completion, latency and cost per execution.

Changes to prompts, models or the document index are checked against this baseline. Operational monitoring includes errors, escalations, usage and user feedback. Acceptance thresholds are agreed for the actual use case; no accuracy or savings figure is promised without measurement.

From a focused pilot to an integrated product

Discovery: choose a useful task, inspect the data and agree on evaluation criteria. Pilot: build a limited version using representative examples. Integration: connect identity, source systems, the user interface and operational controls. Handover: deliver the code, configuration, tests and operating guidance.

The model and technology choices follow your constraints. API-hosted models, open-weight deployment, vector storage and orchestration libraries are options to assess, rather than a fixed stack imposed on every project.

• Source and connector inventory
• Working pilot with evaluation results
• Integration, tests and observability
• Versioned code, configuration and documentation

What to bring to the first discussion

Describe the task, who performs it, which systems it touches and what a useful result looks like. A few anonymised examples and an outline of data-access constraints help us assess feasibility. The first engagement can be a technical review or a bounded pilot.

These are proposed engineering services and illustrative use cases. Project-specific feasibility, responsibilities and delivery scope are established before implementation; the examples are not claims of completed client deployments.