AI agents, visual n8n workflows and Airtable review interfaces. Built with your knowledge, reusable guidance and skills, so you can understand the work and have a hand in improving it.
Consultancy, a scoped project or ongoing collaboration. We agree what’s included, your involvement and the fee before paid work starts.
Work out where AI could help, or investigate a workflow that needs attention.
Scoped consultancy. Agree the questions, outputs and time. The findings can stand on their own.
An agent and visual workflows for a defined task, or changes to an existing system.
A scoped project. Agree the deliverables, checks and project quote before the build.
Keep improving the system as you use it and your work changes.
Sessions or a block of time. Agree the systems, work and time covered.
You don’t have to figure it all out alone
For founders, small teams and people improving a process inside a larger business. Here is the work we can help take off your plate.
| With Pattern Works | Doing it yourself |
|---|---|
Hands-on help with n8n, Airtable and AI | Learning and connecting the tools yourself |
Rules, knowledge and skills organised with you | Working out what to tell the agent |
Outputs you can check, correct and approve | Building your own review process |
Real examples, testing and troubleshooting | Finding time to test and debug |
Shared skills with a clear version history | Keeping copies and updates in step |
Help improving the system as you use it | Maintenance on top of your other work |
You stay involved in the decisions that matter. We bring the tools and implementation, and work with you on the knowledge behind the task.
Book a 30-minute discovery call ↗We work through your process, tools and examples to identify what could help, what needs investigating and what should stay with a person.
Start with a job you already do: handling enquiries, preparing reports or checking documents. We work through real examples with you, draw out the decisions behind them and organise the guidance an agent needs.
Rules set the boundaries the agent must follow. Reference material holds the information it needs to find. Skills explain how to carry out a task, with the right tools and checks. We work out what belongs where, who maintains it and which decisions stay with you.
What must always happen.
Sources the agent can find when it needs them.
How to do the task, which tools to use and what to check.
Mapping the process is the most important part of the work, and the part that requires the most judgement.
Left to its own devices, AI will typically create something madly complex. Deciding what should become an agent, a semi-deterministic workflow or a deterministic workflow needs deliberate judgement. We always err on the side of simplicity.
When an n8n system starts to look like spaghetti, that is a warning sign. We want a clean, organised system that you can follow.
There are three buckets. All differ considerably, and all need to be selected very appropriately.
Typically pure code or inbuilt n8n native nodes, with no LLMs or LLM agents involved.
There is nuance here: a Jev-type or classification model could handle a prescribed decision within that workflow.
Take a completed form, create a record and assign it using rules you have already defined.
Uses AI within a predetermined workflow, which can include chained AI calls.
Appropriate when costs matter and you want the process to be the same every time.
Read an enquiry, extract what is needed, draft a response and check it through a predetermined sequence of AI calls.
An agent is a lot more flexible and a lot more autonomous. It can do things much more flexibly, which is absolutely necessary for a whole range of different tasks.
It is a lot easier to maintain: you don’t need to keep creating new workflows for every variation.
Investigate an enquiry, ask follow-up questions, find the information needed and choose the appropriate tools as the task develops.
The agent typically does work itself and executes deterministic and semi-deterministic workflows.
Our biggest priority is keeping things simple. We lean on agents where possible, integrating deterministic and semi-deterministic workflows where efficiency, cost and the requirements of the task are particularly important.
An example of how the parts connect
An agent uses skills and calls visual workflows.
Outputs and their status appear in a review interface.
Check. Correct. Approve.
We build agents in n8n, equip them with skills and connect the workflows and apps they need. We can store outputs in Airtable and build interfaces where you review, correct and approve them before the next step.
You can follow the process, inspect a result and help improve the system without having to read all the code.
The agent, reusable skills, visual n8n workflows, app connections and review interface needed for the task. We agree the boundaries, test representative cases with you and provide the documentation and walkthrough you need to use it. We can also repair or extend a system you already have.
These checks help us assess a new build and test improvements to an existing system.
A manageable starting point might be 1–20 inputs. Retrieve them, run the workflow, save the outputs and evaluate them, with deterministic checks along the way.
We work with you and your domain expert to develop the metric. An LLM judge uses it to mark complete outputs and recommend improvements. Where useful, agreed examples show what a good result looks like.
Review the findings together. Tinker with the metric as you find nuances you hadn’t factored in, then test the changes.
They test explicit rules without asking a model to interpret quality. For an article workflow, we might check:
A check can show that a citation exists. Assessing whether the source supports the argument takes further judgement.
No. Some outputs can be checked against an exact expected answer using rules. Others need human review, an LLM judge or a combination. Reference examples can help express the standard, but a good article doesn’t have to copy one gold-standard answer.
Our recommendation is to tinker with the metric slowly and keep the evals manageable for a human to review. Sometimes deadlines push for a more automated approach, and that is fine.
| Where we are with the metric | Slower, more deliberate | Short turnaround — need something quick |
|---|---|---|
| Still finding nuances we didn’t factor into the metric | Tinker with the metric slowly and iterate on it until we’re happy with it. | Deadlines can push us towards a more automated approach, and that’s fine. But we still need a carefully thought-through metric and checking process. |
| Happy with the metric and checking process | Run a controlled set of evals that are manageable for a human to review alongside the recommendations. | Scale up the operations, go in bulk and automate those processes. The automated process will only ever be as good as the model and metric combination. |
It needs to be done effectively, with the domain expert and the business.
Taste isn’t something you can just generate automatically without a relationship. You have to spend time encoding your taste into the metric.
The same evaluation loop can run across a larger dataset, with more cases running at once. Classification models can help sort recurring issues or route cases for review.
We choose the nodes and storage around the job. That might mean n8n’s built-in evaluations, or a different setup using Airtable, Supabase and other tools.
You still need a manageable way to review the findings and recommendations. A proposed improvement also needs checking for unnecessary complexity.
The tools we know and use
We favour tools that make the work visible and give you something you can build on. We also adapt the approach to the tools you already use.
Agents and visual workflows that connect your apps and make the steps available to inspect.
Data and review interfaces for non-technical people. Check outputs, record decisions and build on the information.
A shared home for guidance, skills and scripts, with version history and a clear way to review changes.
A database and backend when the work calls for SQL, APIs or a custom application.
For work that people need to review regularly, Airtable is often our preferred interface. Supabase is another option when a database and a custom interface better fit the job. We agree the tools, access and running costs with you.
Consultancy & preparation
Your team knows the work. We help identify where AI could make a useful difference and what needs to be in place first. That might be clearer guidance, better access to information or one small task worth testing.
An investigation, findings and options, a process map, organised source material, reusable guidance or a test plan. We agree the output and the expert time needed. The findings can stand on their own.
Make the knowledge usable
Build & repair
Connect a task across your tools, build a first agent or repair a workflow that needs attention. We use visual n8n workflows, skills and scripts, with clear boundaries and checks shaped around real examples.
We agree the connections, scope limits, acceptance checks and client responsibilities before building. Your experts review representative results. The agreed delivery includes documentation and a walkthrough; ownership, licences and supplier costs are made explicit.
Continuing collaboration
A useful system gives you something to learn from. We can work alongside your team to investigate results, refine guidance, test changes and build confidence in looking after the work.
Through agreed sessions or a defined block of time. We specify the systems and work covered, your team’s involvement and how additional changes are agreed. Support hours and incident handling depend on the arrangement.
Choose how to resource the work
You can build internally, bring us in for a defined piece of work, or have us work alongside your engineers. Here is how the responsibilities differ.
| With Pattern Works | Building in-house |
|---|---|
| Bring in engineering help for an agreed piece of work. | Allocate internal engineering time or recruit for it. |
| Work with us to draw out the knowledge behind the task. | Make time to capture and organise the guidance yourselves. |
| Develop and test an agent or workflow together. | Choose the tools, build the connections and run the tests. |
| Receive the agreed documentation and a walkthrough. | Prepare your own documentation, ownership and handover. |
| Add continuing help when there is useful work to do. | Arrange ongoing maintenance and improvements internally. |
Both routes depend on people who know the task. Your experts help shape guidance and judge the results.
Book a 30-minute discovery call ↗We quote for the work you need. Effort depends on the task, existing systems, access, complexity, testing and the time needed to work with your experts. Where uncertainty is substantial, we can agree a bounded investigation first.
Before paid work starts, we agree deliverables, responsibilities, acceptance checks and fees. Tool subscriptions, hosting and model or API usage are identified separately, including any authorised testing budget. Changes to scope are agreed before extra work.
A few examples of the work are enough to begin a conversation. You need someone who understands the task and can help judge results. We can help identify the technical work and explain how the system operates.
Our recommended starting point is for your business to own its operating accounts, billing and recovery access. We help with setup and use named access. The quote identifies any n8n, hosting or other subscriptions separately from our work; the account and billing arrangement is agreed before setup.
We agree which providers and access methods fit the task, who pays them and what spending is authorised. Where an API account is needed, we recommend a business-owned account with appropriate access. Enter keys through the service’s credential system; keep them out of enquiry emails. A chat subscription and API usage are separate purchases.
We agree the questions to investigate, the engineering time and any supplier-usage budget before paid tests. We use representative examples and review the results with your team. If more time or usage is needed, we agree it before extending the work. A useful finding may be a reason to change direction or stop.
That depends on the tools, data and operating requirements. We assess whether a suitable cloud service meets the need or whether another arrangement is required. Hosting, data location, access and responsibility for administration are agreed as part of the scope.
We start with a manageable set of inputs, develop the evaluation metric with you and use deterministic checks and, where useful, an LLM judge. Your experts review the findings and recommendations with us, and we test changes against the same cases.
How we run evalsWe can scope an investigation or repair. First we need to understand the current workflow, access, dependencies and available records. Any ongoing support depends on that assessment and an agreed scope.
The scope sets out the deliverables, ownership and licences, documentation, walkthrough and access handover. We explain relevant platform or third-party dependencies. Continuing help is optional; covered systems, hours, incident handling, changes and extra charges are agreed explicitly. There is no automatic promise of round-the-clock support.
Bring a task you want an agent to help with. We’ll discuss the tools, guidance and review process it needs, then agree the scope and quote.
Book a discovery call