Custom AI Systems

Custom AI Systems for Workflows No Product Models Correctly

Some operations do not fit a category. Multi-party approvals, deadline-bound fulfilment, regulated handoffs, decades of process encoded in a legacy system. That is a build, not a subscription.

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When do you need a custom AI system instead of an off-the-shelf tool?

You need a custom system when the workflow crosses several systems that were never designed to talk to each other, when the rules are specific enough that no product models them, when the process is bound by a hard external deadline or regulatory step, or when the data cannot leave your environment. If an off-the-shelf tool fits, use it — it will be cheaper and faster. The case for building is that the alternative is a permanent layer of people compensating for the gap between the tool and the actual process.

Capabilities

What the system actually does

Not a feature list for its own sake — each of these exists because it removes a specific piece of repeated work.

Multi-system orchestration

Workflows that span commerce, ERP, CRM, finance and third parties as one coherent process.

Event-driven architecture

A durable event model, so state is reconstructable and nothing depends on a job that silently failed.

Legacy system integration

Working with systems that have no modern API — database access, file exchange, scheduled extracts, wrappers.

Document and data extraction

Turning unstructured input into validated, checkable records.

Domain-specific agents

Agents scoped to one job with an explicit action allow-list, not a general assistant with database access.

Human-in-the-loop design

Approval steps, review queues and override paths designed in from the start.

Audit and traceability

Every decision reconstructable: inputs, rule applied, action taken, who approved.

Internal tooling

The operator-facing screens that make an automated process manageable by real staff.

How it works

How we approach a build

The failure mode in custom AI work is starting from the model. We start from the operational process and the systems that already hold the data.

Nothing here is a black box. Every step writes an audit record: what triggered it, what data it used, what it changed, and whether a person approved it.

01Process mappingWhat actually happens today, including the parts that live in people’s heads and the spreadsheet nobody mentions.

02System and data access reviewWhat each system exposes, what it does not, and what it will cost to get at. This is where most timelines are really decided.

03Event model designDefine the events, states and transitions the process moves through, so behaviour is inspectable rather than emergent.

04Boundary definitionWhat the system may do alone, what needs approval, what always goes to a person.

05PrototypeThe riskiest step first — usually the integration or the extraction accuracy, not the conversation.

06PilotA narrow live slice with real data, measured against a baseline agreed before it started.

07Integration and hardeningError handling, retries, monitoring, access control, audit and the operator screens.

08Scale and hand overDocumentation, runbooks and the ability for your team to change rules without calling us.

Guardrails

AI when it can. Humans when it should.

In a custom build the escalation model is part of the specification, agreed before anything is written.

Every automated action is defined explicitly. There is no general permission to act.
Approval thresholds and review queues are built as first-class features, not bolted on later.
Operators can override any automated decision, and the override is recorded.
The system fails towards a human, never towards a guess.
Access follows least privilege: the AI reads only what the workflow needs and writes only where it is permitted.

Reference architecture

How it fits your existing systems

We do not replace what you run on. The automation layer sits alongside it and is bound by what each system permits.

Interfaces

How the process is triggered and observed.

Web appInternal adminAPIsWebhooksChat / voiceScheduled jobs

Domain layer

The rules of your business, expressed as code rather than as prompt text.

Event modelState machineBusiness rulesValidationApproval workflow

AI layer

Used where interpretation is genuinely needed.

ExtractionClassificationRetrievalReasoning stepsScoped agentsEvaluation harness

Integration

Meeting each system where it is.

REST / GraphQLDatabase accessFile exchangeMessage queuesLegacy wrappersThird-party APIs

Platform

What keeps it running.

Cloud infrastructureQueues + workersMonitoring + alertingBackupsCI/CDSecrets management

Reference architecture, not a screenshot of a specific client deployment. Actual components depend on the systems you already run.

Before / after

What changes operationally

Before

A process held together by spreadsheets and institutional memory
Off-the-shelf tools that cover 60% and create work for the rest
No single view of where a case actually is
Failures found days later during reconciliation
Knowledge concentrated in two people

After

The process encoded, observable and testable
Systems integrated around the real workflow
One state model, one source of truth for case position
Failures detected and escalated when they happen
Documented, handed over, changeable by your own team

Operational changes, not performance claims. Any numbers we publish come from a measured deployment and are named as such.

Where it applies

Common starting points

The best first deployment is narrow, high-volume and measurable. These are the ones that usually qualify.

Score your own workflows

Deadline-bound fulfilment

Where a fixed external event — a flight, a shift, a cut-off — decides whether the order survives.

Multi-party approval workflows

Cases that must pass through several roles with evidence at each step.

Document-heavy operations

Where the bottleneck is reading and validating documents, not deciding.

Legacy system modernisation

Adding an intelligent layer over a system you cannot replace yet.

Internal operator tooling

The screens that let a small team run a large automated process confidently.

Integrations

Systems we connect to on this work

LaravelPHPPythonNode.jsReact / React NativeMySQLPostgreSQLMongoDBRedisMagentoShopifyAWSGoogle CloudDigitalOceanDockerMessage queuesLLM APIs and self-hosted models

Platforms and technologies we have built against. Not an endorsement or partnership claim.

FAQ

Questions we get asked

Buy if a product genuinely fits your process. Build when the gap between the product and your process would be filled by people doing manual work forever. We publish a Build vs Buy calculator precisely so you can reach that answer without a sales conversation.

Yes. Where data cannot leave your environment we design for self-hosted models or private endpoints. There is a real cost and capability trade-off, and we will set it out rather than pretending there is not.

Data access, almost every time. Model quality is rarely the constraint; getting reliable, permissioned, real-time access to the systems that hold the truth is. We front-load that in discovery so it does not surface as a surprise in month three.

You do. We build in your repositories where you have them, document the system, and hand over the ability to run and change it. Building dependency into a handover is not a business model we are interested in.

Relevant industries

Work out the numbers

Give us one repetitive problem.

Tell us about one workflow that keeps reaching a person when it should not. We will come back with how we would automate it, what stays human, and what it takes to build.

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