AI Automation
AI workflow automation built as production software, with approval gates, audit trails and rollback on every step that touches a real system.
AI and automation are not the same thing
Classic automation follows rules you wrote in advance. It is fast, cheap and completely predictable, and it breaks the moment reality stops matching the rulebook. AI automation handles the part rules never covered: reading an unstructured email, deciding which of nine exception paths applies, or judging whether an invoice actually matches the purchase order.
The two belong together. In almost every workflow we ship, deterministic automation moves the data and an LLM handles only the judgement step in the middle. That split is what keeps scope, latency and failure modes under control, and it is the single biggest difference between an AI workflow that survives production and one that quietly gets switched off after a month.
We start from your actual process, not from a tool. Discovery maps every step, who touches it, how long it takes and what happens when it goes wrong. Only then do we decide which steps deserve a model, which stay as plain code, and which should keep a human in the loop for good. Most AI for business automation fails on this split rather than on model quality, because the judgement step was never separated from the plumbing.
Teams that start here often pair it with AI agent development, AI integration and CRM development.
The question is rarely "can AI do this step". It is "what does this step scope us when the AI is wrong, and can we catch it before the customer does".
Why most AI automation projects stall after the pilot
The demo works. Then it meets real data, real volume and a real compliance team.
Traditional automation platforms handle the happy path well and then dump every exception into a human queue, which is exactly where the scope sits.
A model is given write access to billing or CRM with nothing between it and the customer, so one confident hallucination becomes a real financial event.
When finance or a regulator asks why a decision was made, there is no record of the input, the reasoning or the version of the prompt that produced it.
Every step calls a frontier model because nobody separated the judgement steps from the plumbing, so the automation effort more than the labour it replaced.
What an AI automation engagement actually delivers
A working, monitored workflow in production, plus the operational tooling your team needs to own it.
A written map of the target workflow with volumes, cycle times, exception rates and the scope of each manual touch, so the business case is measurable rather than assumed.
Deterministic steps run as ordinary code or n8n nodes; only genuine judgement calls reach a model, which keeps latency predictable and scope roughly an order of magnitude lower.
Every action is classified by blast radius. Low-risk actions run unattended, anything financial or customer-facing waits for a human decision in Slack or your existing tool.
Input, model version, prompt version, output and the human who approved it, stored per run, so any decision can be reconstructed months later.
Retries, timeouts, dead-letter queues and a defined fallback for every external dependency, so one flaky API does not take the workflow down.
Dashboards for throughput, exception rate and scope per run, plus documentation and a working session so your team can change the workflow without calling us.
From process map to a live workflow through controlled rollout
One workflow, end to end, in production, before we scope the next.
A working session with the people who run the process today. We time the steps, count the exceptions and agree the one workflow with the clearest payback.
We split the workflow into deterministic steps and judgement steps, define the approval boundary, and write down what "wrong" looks like for each decision.
Weekly increments against your real data in a sandbox, with a working demo every Friday so the process owner can correct us early rather than at handover.
The workflow runs alongside your team without acting, and we compare its decisions with theirs until agreement is high enough to trust.
Live on a slice of volume with approval gates wide open, then we tighten the gates as the audit log earns confidence.
The tools we use to automate business workflows
Chosen so you can run and change the workflow yourself, not so you are locked into us.
Self-hostable workflow engines carry the deterministic steps, retries and scheduling, so the expensive model calls stay narrow.
A frontier model handles classification, extraction and judgement, with a smaller model for high-volume, low-stakes steps.
Workflows have to reach the systems the business already runs on, with typed clients and idempotency on every write.
Every run is traceable end to end, with scope and error rate visible per workflow rather than as one monthly bill.
What this looks like in production
Cutting a three-day claims backlog to same-day
A mid-sized insurer received claims as email attachments in a dozen formats. Six people spent their mornings reading documents, keying fields into the claims system and routing anything unusual to an assessor. Backlogs of three days were normal and got worse every storm season.
We kept the routing, validation and system writes as deterministic steps, and used a model for exactly two things: extracting fields from the document, and deciding whether a claim was straightforward enough to auto-route. Anything above a value threshold, or anything the model was not confident about, went to a human with the extracted data pre-filled.
The team was not replaced. They stopped keying data and started reviewing the small share of claims that genuinely needed judgement.
What an AI automation agency should measure before it builds
We would rather ship one workflow your operations team fully understands than five they are afraid to touch.
We baseline the manual process before we automate it, so the improvement is a number you can take to a board rather than a claim on a slide.
Every action starts behind an approval gate. Gates open as the audit log earns trust, not because the demo went well.
We build on self-hostable orchestration and your own cloud accounts. If you stop working with us, the workflow keeps running.
