Applied AI across the full stack

No. Most engagements start with one service line — whichever layer the immediate problem sits in. Additional services are added when the architecture requires them, not by default. A single well-scoped engagement is better than a broad one where half the scope is speculative.

The decision depends on three variables: how domain-specific the knowledge is, how frequently it changes, and what the failure mode of a hallucination costs. RAG is the right default when the knowledge base is large, dynamic, and needs to be citable — the model retrieves from your documents rather than relying on parametric memory. Fine-tuning is warranted when the task requires internalised style, terminology, or structured output patterns that retrieval alone cannot enforce. A base LLM integration works when the task is general-purpose and the output does not need to be grounded in proprietary data.

Automation executes a deterministic, pre-defined sequence of steps — the same path every run, with branching only where the branches are explicitly coded. An agent uses an LLM to plan a sequence of tool calls at runtime, which means it can handle variable inputs but introduces non-determinism. That non-determinism requires scoped tools, approval gates on irreversible actions, step and spend ceilings, and full run tracing — none of which automation needs.

Hallucination is a retrieval and architecture problem more than a prompting one. The primary controls are: grounding outputs in a verified retrieval layer so the model cites sources it actually retrieved; running a separate verification step before any generated content reaches a downstream system; restricting the model's output format to structured schemas where possible, which makes non-compliant outputs detectable at parse time; and instrumenting confidence signals so low-confidence outputs are flagged rather than silently passed through.

Security is enforced at the tool layer, not the prompt layer. Each tool the agent can call is issued its own scoped credential with the minimum permissions its function requires — read-only where writes are not needed, never broad API keys. Irreversible or high-impact actions sit behind a human approval gate enforced by the runtime, not a prompt instruction. Every tool call, its inputs, its result, and the model's reasoning at that step are logged to a replayable trace. Spend and step ceilings are set in runtime config, not requested in the system prompt.

What gets extracted depends on the document type and what is trained against it. For structured forms and tables — invoices, contracts, financial statements — we use layout-aware OCR models and NER pipelines fine-tuned on your document corpus. For semi-structured documents, extraction uses LLM-based parsing with a schema enforcement layer that rejects or flags non-conforming outputs. Accuracy is measured against a labelled validation set from your actual documents before the pipeline goes live — generic benchmark numbers are not a proxy for performance on your specific formats.

We build three evaluation layers: automated metric tracking on system outputs — extraction accuracy, retrieval relevance scores, response latency, error rates; human review sampling on a statistically meaningful slice of outputs, which is the only way to catch failure modes automated metrics miss; and feedback loops from downstream systems. We also monitor for distribution shift in inputs, because a system accurate on its training distribution can degrade silently when real-world inputs drift.

Yes. The Data & AI Analytics service connects to your existing data layer — BigQuery, Snowflake, Redshift, lakehouse architectures, or on-premise databases. We do not require a platform migration. The integration pattern depends on whether the AI system needs real-time access, in which case we use streaming or low-latency query interfaces, or batch data, in which case we work with your existing ETL schedules.

The engineering controls we build by default — human oversight gates, run traces, scoped permissions, and documented decision logic — align with the Act's technical requirements for high-risk systems. We build to the controls your compliance and legal teams specify. The overlap between good AI engineering practice and the Act's requirements is significant: the accountability features that make a system auditable are largely the same features that make it safe to operate.

Yes. We review your actual data before finalising the architecture. If the data is not in a state that supports the system — inconsistent schemas, missing labels, inadequate volume for fine-tuning, or access controls that would block the pipeline — we say so in the scoping phase. The Data & AI Analytics service covers data preparation and pipeline work if that is a prerequisite.

The first delivery is always the smallest useful slice of the actual problem — a live system on real inputs, not a prototype on synthetic data. Timeline depends on data access latency, integration complexity, and approval gates required for production write access. A narrow, well-scoped agent or chatbot can reach production in weeks. A multi-service pipeline with document extraction, a retrieval layer, and write integrations to multiple systems takes longer, and we scope that honestly before work starts.