Three kinds of work,explained plainly.

Assistants that answer questions from your documents and business data, and show the source behind every answer.

The problem

The information people need is spread across documents, shared drives, and business systems. Keyword search misses relevant material, and generic chat tools cannot reliably answer from your own content.

Typical uses

• Questions about policies, procedures, and manuals
• Searching contracts, reports, and research documents
• Internal assistants for support, sales, or operations teams
• Questions that need both documents and structured (SQL) data

What you receive

• A pipeline that ingests, splits, and indexes your documents
• Search that combines keyword and semantic retrieval
• Answers with citations to the passages they came from
• Access rules for what each user is allowed to retrieve
• A test set of real questions used to measure retrieval and answer quality

Limits to know about

Answers can only be as good as the source material. An assistant can still be wrong: citations let people check an answer, and high-stakes decisions need human review. Fully offline deployments give more control over data but can run smaller models.

Technologies we use Python · OpenAI / Azure OpenAI · Local models (Ollama) · Qdrant, pgvector, FAISS · BM25 hybrid search · FastAPI

Software that carries out approved, multi-step tasks across your systems, with a person approving the steps that matter.

The problem

Routine work moves between email, spreadsheets, and business systems. Rule-based automation breaks when inputs vary, and unsupervised AI is not acceptable for actions with real consequences.

Typical uses

• Triage and routing of incoming requests
• Extracting information from documents into business systems
• Updating records from several sources
• Security-operations playbooks and incident triage

What you receive

• A map of the workflow, marking which steps need human approval
• Integrations with your tools and APIs, limited to the permissions each step needs
• AI only where inputs vary; ordinary rules where they do not
• A log of every action the system takes
• Tests for both the expected path and failure cases

Limits to know about

Not every workflow benefits from an AI agent, and deterministic steps should stay deterministic. Integrations depend on the APIs and access your systems provide. Actions with financial, legal, or safety impact should keep a human approval step.

Technologies we use Python · LangGraph · FastAPI · REST APIs · Model Context Protocol (MCP) · Cortex XSOAR

Taking an AI prototype into dependable use: measuring its quality, deploying it, and keeping it observable once people rely on it.

The problem

Many AI prototypes work in a demo but have no way to measure quality, no repeatable deployment, and no visibility once real users depend on them.

Typical uses

• Reviewing an existing prototype before a wider rollout
• Building an evaluation set and regression checks
• Containerised deployment on AWS, Azure, or GCP
• Reducing model cost and response time

What you receive

• An evaluation dataset and automated quality checks
• A repeatable deployment pipeline (containers, CI/CD)
• Logging and tracing of model calls
• A written runbook for operating the system

Limits to know about

Evaluation measures quality on the cases you test; it cannot guarantee behaviour on every input. Any ongoing support is agreed separately for each engagement.

Technologies we use Docker · Kubernetes · AWS · Azure ML · MLflow · Databricks

Smaller areas where the team has relevant experience. Get in touch to discuss whether one fits your situation.

• Computer vision and image analysis

  Detection, classification, and segmentation models for images and video, such as vehicle classification or rooftop segmentation.

  Ask about this: Computer vision and image analysis

• Machine learning and predictive models

  Classification, forecasting, and recommendation models with time-aware evaluation and a repeatable training pipeline.

  Ask about this: Machine learning and predictive models

• Custom AI product engineering

  Full-stack development around an AI feature: interface, APIs, data layer, deployment, and monitoring.

  Ask about this: Custom AI product engineering

• Technical review of an AI idea or prototype

  A discussion of whether an idea is feasible, what data it needs, and what a credible architecture could look like. Scope is agreed case by case.

  Ask about this: Technical review of an AI idea or prototype