AI and workflow automation.
Support answers, order triage, content operations and internal copilots — built against your catalogue, policies and order history, with a person still on the decisions that need judgement.
- Best for
- Teams buried in repeat work
- Engagement
- Pilot, then rollout
- Delivery steps
- 6
What is ai & workflow automation?
AI and workflow automation applies language models and rules-based automation to repetitive work inside a business — support replies, order triage, content operations, data entry — while a person keeps the decisions that need judgement. Coder71 grounds every deployment in the client's own catalogue, policies and order data.
AI and workflow automation applies language models and rules-based automation to repetitive work inside a business — support replies, order triage, content operations, data entry — while keeping a person on the decisions that need judgement.
Every merchant has now been pitched AI automation, usually as a percentage of tickets deflected. Deflection is the wrong number. A bot that closes a ticket without solving the problem has not removed the contact — it has moved it to email, or to a chargeback, or to a review.
What we automate
Work that is repetitive, high in volume, and checkable against data you already hold. Those three conditions matter together: drop any one of them and automation stops being safe.
Support resolution
Order status. Returns eligibility against a written policy. Product questions with a specification behind them. Delivery windows, address changes, invoice copies. These are retrieval problems with an unambiguous right answer, which is exactly what this technology is good at.
Triage and routing
Tagging, prioritising and routing before anyone opens the queue. Often the highest-value automation available and the least discussed, because it does not remove human work — it removes the twenty minutes at the start of every shift spent deciding what to do first.
Content operations
Product descriptions, attributes, translations and metadata at catalogue scale, drafted for review rather than published blind. For a 30,000-SKU catalogue this is work that was never going to be done by hand, so the comparison is not "cheaper than a copywriter" — it is "exists at all".
Internal copilots
Answers over your own documentation, policies and historical tickets, for staff rather than customers. Lower risk, because a person is reading the output and can tell when it is wrong, and often the best place to start.
What we leave alone
Goodwill decisions. Damaged and missing items. Anyone who is already angry. Anything with a legal or safety dimension. The cost of getting those wrong exceeds the cost of a person answering them, and no confidence threshold changes that arithmetic. An automation strategy that does not name its exclusions has not been thought about.
| Contact type | Automate? | Why |
|---|---|---|
| Order status and tracking | Yes | Structured data, one correct answer, no judgement |
| Returns eligibility | Yes | A policy check the model can be grounded in |
| Product specification questions | Yes | Retrieval from a spec sheet rather than generation |
| Triage, tagging and routing | Yes | High volume, low risk, and it speeds up every human reply |
| Damaged or missing items | No | Needs a judgement call and usually a goodwill decision |
| Complaints and escalations | No | The cost of getting it wrong exceeds the cost of a person |
| Anything legal or safety-related | No | Accountability has to sit with a named person |
Grounding, and why it is the whole job
A model on its own produces fluent text. Fluent and correct are different properties, and the gap between them is where merchants get hurt. So every deployment we build retrieves from your actual sources — catalogue, policies, order history, documentation — and answers from what it found, with the source available for checking.
Confidence is measured, not assumed. Below the agreed threshold the conversation goes to a person, with the context attached so the customer does not repeat themselves. That handoff is a feature, and the quality of it is most of what separates automation people tolerate from automation they complain about.
How it is measured
Resolution without human contact, checked thirty days out rather than at the moment the ticket closed. Every deployment ships with an evaluation set built from your own historical contacts and a quality bar agreed before launch, so "is it working?" is a query rather than an opinion.
On the grocery platform we run, 41% of contacts now resolve without a human, and first response on everything else fell from four hours to twenty minutes — because the queue is shorter. The second number is the one the client actually cared about, and it is the one that would have been invisible under a deflection metric.
How a project runs
We start with one workflow, not a platform. A narrow pilot against real historical data tells you within weeks whether the economics work for your specific mix of contacts, and it is cheap to stop. If the numbers hold, the second workflow is faster because the retrieval, evaluation and escalation plumbing already exists.
It integrates with what you run — Gorgias, Zendesk, Intercom, Shopify, your ERP — rather than asking your team to work somewhere new. An automation that requires a new tab is an automation people route around.
Governance, data and staying on the right side of it
Customer data in these systems is still customer data. We keep retention explicit and short, keep personal data out of prompts where it is not needed for the answer, log what was sent and what came back so a decision can be reconstructed, and document which processors are involved so your privacy notice can name them. If a deployment needs a regional processing boundary, that is a design constraint, not an afterthought.
Who this is for
Support teams whose volume grows with revenue and whose headcount cannot. Operations teams doing repetitive triage by hand. Merchants with catalogues too large to describe properly. Businesses with a decade of documentation nobody can search. If the automation needs a system built around it, that becomes custom software, and we scope the two together.
What it costs, and how long it takes
A single narrow workflow and a programme across support, operations and content are different investments, and the variables are your contact mix, the state of the data being retrieved, and the number of systems involved. The pilot exists precisely so that decision is made on evidence rather than on a forecast.
Bring us a month of anonymised tickets and we will tell you honestly what share is automatable. Book a free consultation.
What you actually get.
Measured on resolution, not deflection
A bot that closes a ticket without solving the problem has moved the contact, not removed it. We report resolution without human contact, thirty days out.
Grounded in your own data
Retrieval over your catalog, policies and order history, so an answer is checkable rather than merely plausible.
A person on the hard calls
Goodwill, damage and escalation stay with your team. Automating those costs more than it saves.
Proven on a pilot first
One narrow workflow against real historical data tells you within weeks whether the economics work for your contact mix — and it is cheap to stop.
Inside the tools you already use
Wired into Gorgias, Zendesk, Intercom, Shopify or your ERP. An automation that needs a new tab is one people route around.
Included in every build.
- Support automationOrder status, returns eligibility and product questions, grounded in your systems.
- Order and ticket triageRouting, tagging and prioritisation before anyone opens the queue.
- Content operationsProduct copy, translations and metadata drafted at catalog scale, reviewed by your team.
- Internal copilotsAnswers over your own documentation, for staff rather than customers.
- Workflow integrationWired into the helpdesk, ERP or store you already run.
- Evaluation and guardrailsA test set, an agreed quality bar, and a hand-off to a person when confidence is low.
Discovery to launch, step by step.
How long it runs depends on your catalogue, integrations and how much has to be migrated. You get a schedule with the proposal, after discovery — not before it.
Find the volume
We read a month of your real contacts or tasks and classify them, so the opportunity is measured rather than estimated. You get an honest share of what is genuinely automatable.
Pick one workflow
A single narrow use case with high volume and a checkable right answer, rather than a platform rollout. Deliberately small enough to be cheap to stop.
Ground it
Retrieval wired to your catalogue, policies, order history and documentation, so answers come from your sources. Every answer is traceable to what it was drawn from.
Evaluate
An evaluation set built from your own historical contacts, with a quality bar and an escalation threshold agreed before launch. Quality becomes a query rather than an opinion.
Pilot in production
Live on a slice of real traffic, with a person reviewing output and a clean handoff whenever confidence drops. Measured on resolution at thirty days, not on tickets closed.
Extend
If the numbers hold, the next workflow is faster, because retrieval, evaluation and escalation already exist. And if they do not, you have spent a pilot rather than a programme.
- Claude
- OpenAI
- Node.js
- PostgreSQL
AI & workflow automation — asked and answered.
Work that is repetitive, high in volume, and checkable against data you already hold — all three conditions together. In practice that means order status, returns eligibility against a written policy, product questions with a specification behind them, ticket triage and routing, and catalogue content at a scale nobody was going to write by hand.
Send a brief, get a real answer.
One paragraph is enough. A senior engineer replies within one business day with scope, risks and a timeline — not a sales call.
- A reply within one business day, from an engineer rather than an account manager.
- Scope, risks and an honest timeline — including the parts we would push back on.
- No obligation and no sales call. If we are not the right fit we will say so.
Services that pair with this.
Custom software development
Ordering platforms, portals and internal systems built around how your business works, rather than around what a product allows.
Scope your system
Custom e-commerce website development
Main serviceStores built as software rather than configured from a template: Next.js or Vue.js on the front, Node.js or Laravel behind it, and a catalogue model that matches how you actually trade.
Build your store
Tell us the goal. We’ll scope the work.
New website, a rebuild, Shopify, a mobile app, custom software or SEO — one paragraph is enough. You get scope, risks and a timeline back within one business day, from the engineer who would build it.