AI Workflows

AI workflows, built for production

AI workflows that run reliably in production are rarely about the model. They're about how the work is sequenced, how errors are handled, how the system stays visible to the people running it, and where humans stay in the loop. That's the part we focus on.

Overview

What Are AI Workflows?

An AI workflow is a defined sequence of steps that takes an input — a document, a request, a record, a customer enquiry — and produces an outcome, with one or more of those steps using an AI model. Each step is recorded and inspectable, which is what separates a workflow from a one-off use of a chatbot or a single API call.

We design workflows around three principles:

Defined execution

Each step has a clear input, a clear output, and a clear contract. The workflow follows a predictable path even when the model output varies.

Full traceability

Every step is logged. When something goes wrong, you can see what happened, where, and why — without reverse-engineering it from production data.

Resilience by default

Failures are expected, not exceptional. Workflows are built with retries, fallbacks, and graceful degradation, so the failure of one component doesn't take the whole system down.

Audience

Who we work with

  • Engineering leaders shipping AI features into existing products.
  • Operations leaders running high-volume processes that can't tolerate unreliable outputs.
  • Teams with working prototypes that need to become robust enough for real users, real data, and real load.
  • Organisations in regulated environments where traceability isn't optional.
Challenges

The problems we most often see

  • AI features that behave differently in production than in testing. A model that performs well on clean test data can fail in less obvious ways once it's handling real users, partial system failures, and timeouts from upstream services. The reliability problem is rarely the model itself — it's the workflow around the model.
  • Processes too irregular for traditional automation. Many workflows span several systems, branch depending on context, require human approval at certain points, or have to handle exceptions that rule-based tools can't. These are good candidates for AI workflows — but only if the orchestration is built carefully.
  • No visibility once the system is running. Workflows that can't be inspected, traced, or audited become impossible to debug and often impossible to deploy in environments where accountability matters. Adding this kind of visibility later is painful and expensive; building it in from the start costs almost nothing.
    • Approach

    How we approach the work

    • 01 — Engineering around the model. The model is one component in a larger system, not the system itself. Most of the engineering effort goes into the parts around the model — sequencing, integration with existing systems, error handling, evaluation, monitoring. That's where reliability is actually earned.
    • 02 — Visibility. Every step in a workflow should be inspectable. What came in, what the model returned, what the system did with it, what the outcome was — all of it logged from day one. Adding this later is difficult; building it in from the beginning costs almost nothing and pays off through the life of the system.
    • 03 — Human oversight, where it counts. Human review at the moments where judgement actually matters. We design human-in-the-loop controls for exceptions, low-confidence outputs, or steps with material business impact. Not on every task — which would defeat the point of automating the work — but at the moments where human judgement is genuinely needed.
    • 04 — Built for change. Architectures that survive the next eighteen months. AI tooling and model capabilities shift quickly. We aim for designs that allow components to be swapped without rewriting the system, because the model that's right today may not be the right one in eighteen months.
    Use Cases

    Where AI workflows fit

    Software companies and product teams

    Embedding AI features inside an application, where every user request has to behave predictably and stay cost-sensible as usage grows.

    Common use cases:

    • Semantic search over product content or user data
    • In-product features that generate, classify, or summarise content
    • Smart recommendations and routing that learn from user behaviour
    • Backend pipelines that handle high-volume requests asynchronously

    Operations teams in mid-sized companies

    Automating document-heavy or multi-system processes that have outgrown manual handling but don't fit rule-based tools.

    Common use cases:

    • Invoice and document processing with human review on edge cases
    • Lead qualification across CRM, external data, and routing logic
    • Customer support triage across channels and product lines
    • HR screening and onboarding flows that pull from multiple sources

    Multi-team and compliance-heavy processes

    Coordinating workflows across departments or systems, where the whole process needs to be visible, consistent, and accountable.

    Common use cases:

    • Approval workflows where AI prepares or summarises information for reviewers
    • Compliance and audit pipelines with traceable decisions and source data
    • Cross-system data reconciliation across multiple platforms
    • Process automation that combines AI judgement with deterministic logic
    Process

    How a typical engagement runs

    Most workflow projects move through five phases. We work alongside your team throughout, with weekly check-ins so you can see progress, raise questions early, and shift priorities as the project evolves.

    1

    Discovery

    We start by understanding your process — how it works today, what systems and data are involved, and what success would look like. The output is a written plan with a realistic scope, timeline, and cost.

    2

    Build

    We design and implement the workflow alongside your team, integrating with the systems, APIs, and data you already use. Logging, tracing, and monitoring are built in from the first commit.

    3

    Validation

    We test against real edge cases — not just the happy path — and validate the workflow against your actual data. Where load matters, we test under realistic load.

    4

    Deployment

    Production rollout with monitoring, alerting, and the human-in-the-loop controls the use case requires. We stay close during the first weeks of live use, when the differences between test and production tend to show up.

    5

    Handover, and what comes next

    We hand over the workflow with full documentation and a clear plan for how your team will own and evolve it. Everything we build — code, infrastructure, operational knowledge — is yours.

    From there, you have two options: take the workflow in-house and run it yourselves, or have us continue alongside you for monitoring, issue handling, and ongoing updates. Both work for us; we'll talk through the choice at the start of the engagement.

    Get in touch

    Let's talk about your project

    Most engagements begin with a short discovery phase: a few days spent understanding the process, the data, the systems, and what success would actually look like. The output is a written plan with a realistic scope, timeline, and cost — and an honest read on whether we're a good fit.

    We're glad to start the conversation, whether you have a clearly scoped project, a rough idea you're still thinking through, or a specific problem you'd like a second opinion on.

    Contact us