Skip to content
Delta AI Engineering

Services

What we design, build and run.

Each service ends in working software or a decision you can act on, with the evidence that it meets the target we agreed.

Agentic systems

Multi-agent workflows that plan, call tools and hand off to people at the right moment, built on explicit state so every step can be inspected.

What you get

  • Tool use with typed contracts and permission boundaries
  • Human approval checkpoints for consequential actions
  • Traces for every run, replayable for debugging

Deliverables

  • Workflow graph and state model
  • Tool and MCP integrations
  • Evaluation suite
  • Runbook

Retrieval and knowledge

Grounded answers from your own documents using hybrid search, semantic reranking and citations people can check.

What you get

  • Hybrid keyword and vector retrieval
  • Reranking tuned on your queries, not a demo set
  • Answers that cite their sources or decline

Deliverables

  • Ingestion pipeline
  • Index and reranker
  • Answer service with citations
  • Quality dashboard

Cloud and platform engineering

The foundation AI runs on: containerised services, CI/CD, infrastructure as code and observability from the first commit.

What you get

  • Repeatable environments with Docker and Kubernetes
  • Automated delivery pipelines with gated releases
  • Logs, metrics and traces wired before launch

Deliverables

  • Reference architecture
  • IaC modules
  • CI/CD pipelines
  • Observability stack

Evaluation and LLMOps

Measure quality, latency and cost continuously so model or prompt changes ship on evidence instead of intuition.

What you get

  • Task-specific evaluation sets and graders
  • Regression gates in CI for prompts and models
  • Cost and latency budgets per workflow

Deliverables

  • Eval harness
  • Baseline report
  • Release gates
  • Monitoring alerts

Applied data science

From raw records to decision-ready reporting, with modelling choices explained in language your stakeholders can act on.

What you get

  • Data quality assessment before modelling
  • Interpretable models where decisions affect people
  • Reporting designed for the reader, not the analyst

Deliverables

  • Data audit
  • Model and validation notes
  • Dashboards
  • Handover session

AI strategy and architecture

A short, structured engagement to decide where AI earns its place, what to build first and what it should cost to run.

What you get

  • Use cases ranked by value, feasibility and risk
  • Build, buy or wait recommendations
  • Architecture and budget for the first release

Deliverables

  • Opportunity map
  • Architecture decision records
  • Delivery plan
  • Risk register

Ways to work together

Start small, or embed with your team.

  • Discovery sprint

    A fixed, short engagement that ends with a ranked opportunity map, an architecture and a costed plan.

    Best when: You know AI could help but not where to start.

  • Build a vertical slice

    One production-ready workflow, delivered end to end with evaluation and monitoring.

    Best when: You have a priority use case and want it live.

  • Embedded engineering

    Our engineers join your team to deliver, review and mentor, with shared ownership of outcomes.

    Best when: You are scaling AI work and need senior capacity.

Tell us the change you need. We will tell you how we would measure it.

A short first conversation, a written summary afterwards, no obligation.

Start a project