64ARCS helps traditionally run businesses use software and AI in everyday work. Start with the paperwork, repeated typing and information people chase by phone. Keep what works, and use AI where it makes the job easier.
Connect the tools your team already uses, then consider AI for a specific task, such as reading an order or summarising an account. Judge the result by the work it removes, including the time spent checking its output.
Start with a workflow, its source records and access rules. Compare a model with a deterministic rule or a process change. Define validation, confirmation and a fallback before connecting model output to a write path.
The work includes a business system in daily use since July 2026 and two products, PathWay AI and 64Griddle, available as browser pilots since September 2026.
Choose the level of detail.
Plain words, no jargon.
If you write software, start at Engineering detail.
Watch where someone copies a message into a form, reads a paper order or phones for a figure. Choose a task that takes time every day and agree what a useful improvement would look like.
Choose a frequent task with a clear input and an outcome staff can check. Record how long it takes today, including corrections, so there is a fair comparison with the proposed change.
Trace inputs, outputs and exceptions through one workflow. Establish a baseline for completion time and errors, and test on representative examples before choosing an implementation.
AI can help turn spoken or handwritten orders into items someone can check, or put account details into plain words. A calculation or a fixed business rule may need no AI at all.
Consider a model for language or varied documents. Keep calculations and fixed checks in ordinary software. Include review time, running cost and mistakes when deciding whether a model helps.
Use a model for extraction or summarisation where inputs vary. Keep arithmetic and policy checks deterministic. Compare the full workflow with and without the model, including validation failures and latency.
Build around the screens, records and habits that still serve the business. The aim is fewer steps for the person doing the job, with a clear way to check and correct a suggestion.
Put assistance at the point where the task happens. Avoid a separate tool that asks staff to copy information between systems. Keep the existing source for prices, balances and other agreed facts.
Connect through the existing system’s supported interfaces and enforce permissions on the server. Keep authoritative values in their owning system and treat generated content as a proposal to validate.
A person needs a way to finish the task when AI gets something wrong or is unavailable. Decide what needs checking, who can approve a change and how to carry on without the suggestion.
Agree which actions need confirmation and how staff recover from an error. Try the change with the people who will use it, and include a manual route or a rule-based fallback where needed.
Define failure behaviour for missing records, invalid output and model outages. Test permission boundaries and write validation separately from generation, with an explicit fallback for the workflow.
A simpler form, a connected record or a clearer process may be enough. The choice depends on the task and the people doing it.
A business system and two products, each with a different job. The product browser pilots opened in September 2026; their limits are set out on the Products page.
Staff can check stock and dues, capture orders by voice or photograph and review the items before placing an order. The existing ERP still prices and bills it.
How it was builtA student works on an assignment inside the terms their teacher set, and the session is kept as a record both of them see. It shows how the work happened and does not judge who wrote it. Replies are prepared examples, and no generative model is connected.
What it does, and where it stopsIt reads an AI prompt before you send it, marks where an assistant would have to guess, and offers wording that you accept or reject. Version 0.5.3 runs on local rules in the browser and makes no network requests.
What it does, and where it stopsThe first step is to watch your team work and find where time goes on repeated tasks.
Agree what should get easier, which records it needs and whether a rule or AI would help.
It is written in small pieces, and you see each one working before the next begins.
Connect the new piece to the records and tools your team uses, with fewer steps to finish the job.
We stay on afterwards. A piece is replaced only after you have watched the new one run beside it.
Map the routine work, including exceptions and workarounds, with the people responsible for it.
We agree which system owns each fact, what gets connected and what gets built.
Try each working piece with the staff who will use it, including the checks it asks them to make.
Connect existing tools and keep ownership of prices, balances and approvals clear.
We stay on, and any replacement runs beside the original before it takes over.
Trace the workflow through its source records, permissions and manual handovers.
Agree the owner of each fact, where each rule is enforced, and the one write path.
The server comes first, with its rules, transactions and migrations, and the apps follow in slices that ship.
Validate suggested changes against source records and business rules before writing through the owning system.
Replace a module only after it has run beside the original, read-only, on real data.
Your team may rely on paper, spreadsheets, phone calls or software built years ago. The starting point is a task that is slow or difficult today. If a spreadsheet already handles it well, there is no reason to replace it just to introduce AI.
To discuss a task your team spends too much time on, write to hello@64arcs.com or use the form. A person reads it and replies within two working days. The first call is sixty minutes on how your operation runs, no deck.
