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Yeti Technology

AI & Automation

Workflow Automation & Internal Tools

The hours your team loses to re-keying, copy-paste, and chasing status are an engineering problem. We map the real process, automate what should be automatic, and build honest tools for the rest.

Most operational drag is not one big broken system. It is a dozen small handoffs held together by exported spreadsheets, forwarded emails, and someone remembering to follow up — invisible in any org chart and expensive every single day. We treat that glue as an engineering problem: map how the work actually flows, including the exceptions everyone works around, then automate the repetitive, rule-heavy parts and build internal tools for the parts that need a person. AI where it genuinely helps, deterministic code where it is enough.

Hours returned

repetitive, rule-heavy work moved from people to software

Fewer errors

rules executed by code instead of concentration

Seen end to end

pipelines that report their own status — no more chasing

Where automation actually pays

Not every process should be automated, and saying so is part of the service. The payback lives in work that is repetitive, rule-heavy, and error-prone under boredom — data moved between systems, documents assembled from the same sources every week, approvals that stall because nobody was notified. We size the opportunity honestly before anything is built, and if the honest answer is "fix the process, not the software", you get that answer.

  • High-volume, rule-heavy work where software is simply better than boredom
  • Handoffs between systems currently bridged by exports and re-keying
  • Status-chasing replaced by pipelines that report themselves
  • An honest assessment first — including the cases where automation is not the fix

Map the process first, automate second

The documented process and the real one are rarely the same thing, and automating the documented one is how automation projects fail. We sit with the people doing the work, trace the real flow — including the exceptions, workarounds, and undocumented judgement calls — and only then decide what software should own. The exceptions are not noise; they are the spec.

  • The real workflow traced with the people who run it, not the slide about it
  • Exceptions and edge cases catalogued before a line of code
  • A staged plan: the highest-payback step first, proving value before widening scope
  • Process changes proposed where they beat software — cheaper is cheaper

Internal tools people actually want to use

Back-office software is where design usually goes to die, and the cost is quiet: workarounds, shadow spreadsheets, and tools that decay into being ignored. We bring the same design discipline to internal tools as to consumer apps — fast, obvious, keyboard-friendly, with the states real operations hit (bulk actions, partial failures, "undo") actually designed. A tool people like is a tool that keeps its data clean.

  • Interfaces designed around the operator’s real day, not the database schema
  • Fast, keyboard-first flows for high-volume work
  • Bulk operations, audit history, and undo — the unglamorous features that decide adoption
  • Role-appropriate views so each person sees their work, not everyone’s

Integrations that don't rot

Automation lives or dies at the seams — the APIs, webhooks, and file drops connecting the systems you already run. We build those seams the way we build apps: typed contracts, retries and idempotency for the days networks misbehave, monitoring that notices silence as well as errors, and documentation your next engineer can pick up. Built to be kept, like everything else we ship.

  • Integrations with the systems you already run — accounting, CRM, storage, messaging
  • Retries, idempotency, and dead-letter handling for the failure days
  • Monitoring and alerting that notice when a quiet pipeline stops being a healthy one
  • Documented and handed over — no dependency on the person who built it

Humans stay in the loop where judgement lives

The goal is not a business that runs itself; it is a team spending its hours on judgement instead of re-keying. Deterministic code handles the rules, AI handles the fuzzy middle where it measurably earns its place, and approval gates sit wherever a mistake has real cost. Every automation ships with an off switch, an audit trail, and the same three months of free post-launch support as our apps.

Every engagement includes

  • Native architecture planning before code
  • Senior developer review on every pull request
  • App Store & Play Store launch support
  • 3 months of free post-launch support
See the full process, review gates, and support terms →

Frequently asked questions

What kinds of processes can be automated?

The best candidates are repetitive, rule-describable, and currently done by hand across systems: onboarding steps, report assembly, invoice and document handling, data syncs, approval chains, notifications. In discovery we map your workflows and rank them by payback — some of the highest-value automations turn out to be the least glamorous.

Should we use off-the-shelf automation tools or build custom?

Off-the-shelf first, honestly assessed. If a no-code tool or a native integration between your existing systems covers the job, we will point at it — that advice is free and it builds the kind of trust we like working under. Custom earns its keep when workflows cross systems in ways templates cannot follow, when volume or auditability outgrows the tools, or when the interface itself is the problem.

How do we know the investment is worth it before committing?

Discovery produces a map of the current process with its real volumes and time cost, against a scoped estimate for the automation. You see the trade before you commit to the build. We deliberately stage delivery so the highest-payback step ships first — the case for step two is made by step one running in production.

Who maintains it after launch?

Every build includes three months of free post-launch support, and integrations are monitored so failures announce themselves instead of hiding. After that, we stay on under a support arrangement or hand over documented, tested code your own team can run — the same handover standard as our apps.

Does automation have to involve AI?

No, and it often should not. Rules that can be written as code should be code: deterministic, testable, cheap to run. AI earns its place in the fuzzy middle — reading documents, drafting responses, classifying the ambiguous cases — and when we use it there, it ships with evaluation and human review. We choose the boring tool wherever the boring tool wins.

Related services

Ready to talk about workflow automation?

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