AI & Intelligent Automation

AI work rarely fails at the model. It fails at everything around it: grounding answers in real data, measuring whether the output is actually correct, and keeping spend predictable. We build the parts that make an AI feature safe to put in front of customers.

What this covers

• AI Product Features

  Assistants, summarisation, semantic search and classification built into your product, with evaluation and cost controls instead of a demo that drifts.

• Agents & Workflow Automation

  Multi-step automation across your own systems and data, with human approval at the points where being wrong is expensive.

• Data Engineering & Retrieval

  Pipelines, storage and retrieval-augmented generation, so answers are grounded in your data and cite where they came from.

What you get

• Answers grounded in your own data, with the source shown
• Evaluation that runs in CI, so quality is measured rather than hoped for
• Token spend that is budgeted and observable per feature

Typical stack

Models & SDKs

OpenAIAzure OpenAIAnthropic ClaudeGoogle GeminiVercel AI SDKModel Context Protocol

Retrieval & grounding

pgvectorEmbeddingsHybrid searchRAG pipelines

Data ingestion

PDF & document parsingCSV and ETLConvexWebhooks

Evaluation & cost control

Eval harnesses in CIPrompt versioningToken budgetingGuardrails

Languages

TypeScriptPython